research related to abstract ideas or concepts is

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Difference Between Conceptual and Empirical Research

The main difference between conceptual and empirical research is that conceptual research involves abstract ideas and concepts, whereas empirical research involves research based on observation, experiments and verifiable evidence.

Conceptual research and empirical research are two ways of doing scientific research. These are two opposing types of research frameworks since conceptual research doesn’t involve any experiments and empirical research does.

Key Areas Covered

1. What is Empirical Research     – Definition, Characteristics, Uses 2. What is Empirical Research     – Definition, Characteristics, Uses 3. What is the Difference Between Conceptual and Empirical Research     – Comparison of Key Differences

Conceptual Research, Empirical Research, Research

What is Conceptual Research?

Conceptual research is a type of research that is generally related to abstract ideas or concepts. It doesn’t particularly involve any practical experimentation. However, this type of research typically involves observing and analyzing information already present on a given topic. Philosophical research is a generally good example for conceptual research.

Conceptual research can be used to solve real-world problems. Conceptual frameworks, which are analytical tools researchers use in their studies, are based on conceptual research. Furthermore, these frameworks help to make conceptual distinctions and organize ideas researchers need for research purposes.

Figure 2: Conceptual Framework

In simple words, a conceptual framework is the researcher’s synthesis of the literature (previous research studies) on how to explain a particular phenomenon. It explains the actions required in the course of the study based on the researcher’s observations on the subject of research as well as the knowledge gathered from previous studies.

What is Empirical Research?

Empirical research is basically a research that uses empirical evidence. Empirical evidence refers to evidence verifiable by observation or experience rather than theory or pure logic. Thus, empirical research is research studies with conclusions based on empirical evidence. Moreover, empirical research studies are observable and measurable.

Empirical evidence can be gathered through qualitative research studies or quantitative research studies . Qualitative research methods gather non-numerical or non-statistical data. Thus, this type of studies helps to understand the underlying reasons, opinions, and motivations behind something as well as to uncover trends in thought and opinions. Quantitative research studies, on the other hand, gather statistical data. These have the ability to quantify behaviours, opinions, or other defined variables. Moreover, a researcher can even use a combination of quantitative and qualitative methods to find answers to his research questions .

Figure 2: Empirical Research Cycle

A.D. de Groot, a famous psychologist, came up with a cycle (figure 2) to explain the process of the empirical research process. Moreover, this cycle has five steps, each as important as the other. These steps include observation, induction, deduction, testing and evaluation.

Conceptual research is a type of research that is generally related to abstract ideas or concepts whereas empirical research is any research study where conclusions of the study are drawn from evidence verifiable by observation or experience rather than theory or pure logic.

Conceptual research involves abstract idea and concepts; however, it doesn’t involve any practical experiments. Empirical research, on the other hand, involves phenomena that are observable and measurable.

Type of Studies

Philosophical research studies are examples of conceptual research studies, whereas empirical research includes both quantitative and qualitative studies.

The main difference between conceptual and empirical research is that conceptual research involves abstract ideas and concepts whereas empirical research involves research based on observation, experiments and verifiable evidence.

1.“Empirical Research: Definition, Methods, Types and Examples.” QuestionPro, 14 Dec. 2018, Available here . 2. “Empirical Research.” Wikipedia, Wikimedia Foundation, 15 Sept. 2019, Available here . 3.“Conceptual Research: Definition, Framework, Example and Advantages.” QuestionPro, 18 Sept. 2018, Available here. 4. Patrick. “Conceptual Framework: A Step-by-Step Guide on How to Make One.” SimplyEducate.Me, 4 Dec. 2018, Available here .

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1. “APM Conceptual Framework” By LarryDragich – Created for a Technical Management Counsel meeting Previously published: First published in APM Digest in March (CC BY-SA 3.0) via Commons Wikimedia 2. “Empirical Cycle” By Empirical_Cycle.png: TesseUndDaanderivative work: Beao (talk) – Empirical_Cycle.png (CC BY 3.0) via Commons Wikimedia

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How We Use Abstract Thinking

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

MoMo Productions / Getty Images

• How It Develops

Abstract thinking, also known as abstract reasoning, involves the ability to understand and think about complex concepts that, while real, are not tied to concrete experiences, objects, people, or situations.

Abstract thinking is considered a type of higher-order thinking, usually about ideas and principles that are often symbolic or hypothetical. This type of thinking is more complex than the type of thinking that is centered on memorizing and recalling information and facts.

Examples of Abstract Thinking

Examples of abstract concepts include ideas such as:

• Imagination

While these things are real, they aren't concrete, physical things that people can experience directly via their traditional senses.

You likely encounter examples of abstract thinking every day. Stand-up comedians use abstract thinking when they observe absurd or illogical behavior in our world and come up with theories as to why people act the way they do.

You use abstract thinking when you're in a philosophy class or when you're contemplating what would be the most ethical way to conduct your business. If you write a poem or an essay, you're also using abstract thinking.

With all of these examples, concepts that are theoretical and intangible are being translated into a joke, a decision, or a piece of art. (You'll notice that creativity and abstract thinking go hand in hand.)

Abstract Thinking vs. Concrete Thinking

One way of understanding abstract thinking is to compare it with concrete thinking. Concrete thinking, also called concrete reasoning, is tied to specific experiences or objects that can be observed directly.

Research suggests that concrete thinkers tend to focus more on the procedures involved in how a task should be performed, while abstract thinkers are more focused on the reasons why a task should be performed.

It is important to remember that you need both concrete and abstract thinking skills to solve problems in day-to-day life. In many cases, you utilize aspects of both types of thinking to come up with solutions.

Other Types of Thinking

Depending on the type of problem we face, we draw from a number of different styles of thinking, such as:

• Creative thinking : This involves coming up with new ideas, or using existing ideas or objects to come up with a solution or create something new.
• Convergent thinking : Often called linear thinking, this is when a person follows a logical set of steps to select the best solution from already-formulated ideas.
• Critical thinking : This is a type of thinking in which a person tests solutions and analyzes any potential drawbacks.
• Divergent thinking : Often called lateral thinking, this style involves using new thoughts or ideas that are outside of the norm in order to solve problems.

How Abstract Thinking Develops

While abstract thinking is an essential skill, it isn’t something that people are born with. Instead, this cognitive ability develops throughout the course of childhood as children gain new abilities, knowledge, and experiences.

The psychologist Jean Piaget described a theory of cognitive development that outlined this process from birth through adolescence and early adulthood. According to his theory, children go through four distinct stages of intellectual development:

• Sensorimotor stage : During this early period, children's knowledge is derived primarily from their senses.
• Preoperational stage : At this point, children develop the ability to think symbolically.
• Concrete operational stage : At this stage, kids become more logical but their understanding of the world tends to be very concrete.
• Formal operational stage : The ability to reason about concrete information continues to grow during this period, but abstract thinking skills also emerge.

This period of cognitive development when abstract thinking becomes more apparent typically begins around age 12. It is at this age that children become more skilled at thinking about things from the perspective of another person. They are also better able to mentally manipulate abstract ideas as well as notice patterns and relationships between these concepts.

Uses of Abstract Thinking

Abstract thinking is a skill that is essential for the ability to think critically and solve problems. This type of thinking is also related to what is known as fluid intelligence , or the ability to reason and solve problems in unique ways.

Fluid intelligence involves thinking abstractly about problems without relying solely on existing knowledge.

Abstract thinking is used in a number of ways in different aspects of your daily life. Some examples of times you might use this type of thinking:

• When you describe something with a metaphor
• When you talk about something figuratively
• When you come up with creative solutions to a problem
• When you analyze a situation
• When you notice relationships or patterns
• When you form a theory about why something happens
• When you think about a problem from another point of view

Research also suggests that abstract thinking plays a role in the actions people take. Abstract thinkers have been found to be more likely to engage in risky behaviors, where concrete thinkers are more likely to avoid risks.

Impact of Abstract Thinking

People who have strong abstract thinking skills tend to score well on intelligence tests. Because this type of thinking is associated with creativity, abstract thinkers also tend to excel in areas that require creativity such as art, writing, and other areas that benefit from divergent thinking abilities.

Abstract thinking can have both positive and negative effects. It can be used as a tool to promote innovative problem-solving, but it can also lead to problems in some cases:

• Bias : Research also suggests that it can sometimes promote different types of bias . As people seek to understand events, abstract thinking can sometimes cause people to seek out patterns, themes, and relationships that may not exist.
• Catastrophic thinking : Sometimes these inferences, imagined scenarios, and predictions about the future can lead to feelings of fear and anxiety. Instead of making realistic predictions, people may catastrophize and imagine the worst possible potential outcomes.
• Anxiety and depression : Research has also found that abstract thinking styles are sometimes associated with worry and rumination . This thinking style is also associated with a range of conditions including depression , anxiety, and post-traumatic stress disorder (PTSD) .

Conditions That Impact Abstract Thinking

The presence of learning disabilities and mental health conditions can affect abstract thinking abilities. Conditions that are linked to impaired abstract thinking skills include:

• Learning disabilities
• Schizophrenia
• Traumatic brain injury (TBI)

The natural aging process can also have an impact on abstract thinking skills. Research suggests that the thinking skills associated with fluid intelligence peak around the ages of 30 or 40 and begin to decline with age.

Tips for Reasoning Abstractly

While some psychologists believe that abstract thinking skills are a natural product of normal development, others suggest that these abilities are influenced by genetics, culture, and experiences. Some people may come by these skills naturally, but you can also strengthen these abilities with practice.

Some strategies that you might use to help improve your abstract thinking skills:

• Think about why and not just how : Abstract thinkers tend to focus on the meaning of events or on hypothetical outcomes. Instead of concentrating only on the steps needed to achieve a goal, consider some of the reasons why that goal might be valuable or what might happen if you reach that goal.
• Reframe your thinking : When you are approaching a problem, it can be helpful to purposefully try to think about the problem in a different way. How might someone else approach it? Is there an easier way to accomplish the same thing? Are there any elements you haven't considered?
• Consider the big picture : Rather than focusing on the specifics of a situation, try taking a step back in order to view the big picture. Where concrete thinkers are more likely to concentrate on the details, abstract thinkers focus on how something relates to other things or how it fits into the grand scheme of things.

Abstract thinking allows people to think about complex relationships, recognize patterns, solve problems, and utilize creativity. While some people tend to be naturally better at this type of reasoning, it is a skill that you can learn to utilize and strengthen with practice. 

It is important to remember that both concrete and abstract thinking are skills that you need to solve problems and function successfully. 

Gilead M, Liberman N, Maril A. From mind to matter: neural correlates of abstract and concrete mindsets . Soc Cogn Affect Neurosci . 2014;9(5):638-45. doi: 10.1093/scan/nst031

American Psychological Association. Creative thinking .

American Psychological Association. Convergent thinking .

American Psychological Association. Critical thinking .

American Psychological Association. Divergent thinking .

Lermer E, Streicher B, Sachs R, Raue M, Frey D. The effect of abstract and concrete thinking on risk-taking behavior in women and men . SAGE Open . 2016;6(3):215824401666612. doi:10.1177/2158244016666127

Namkoong J-E, Henderson MD. Responding to causal uncertainty through abstract thinking . Curr Dir Psychol Sci . 2019;28(6):547-551. doi:10.1177/0963721419859346

White R, Wild J. "Why" or "How": the effect of concrete versus abstract processing on intrusive memories following analogue trauma . Behav Ther . 2016;47(3):404-415. doi:10.1016/j.beth.2016.02.004

Williams DL, Mazefsky CA, Walker JD, Minshew NJ, Goldstein G. Associations between conceptual reasoning, problem solving, and adaptive ability in high-functioning autism . J Autism Dev Disord . 2014 Nov;44(11):2908-20. doi: 10.1007/s10803-014-2190-y

Oh J, Chun JW, Joon Jo H, Kim E, Park HJ, Lee B, Kim JJ. The neural basis of a deficit in abstract thinking in patients with schizophrenia . Psychiatry Res . 2015;234(1):66-73. doi: 10.1016/j.pscychresns.2015.08.007

Hartshorne JK, Germine LT. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span . Psychol Sci. 2015;26(4):433-43. doi:10.1177/0956797614567339

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Conceptual Research: Definition, Framework, Example and Advantages

Conceptual Research: Definition

Conceptual research is defined as a methodology wherein research is conducted by observing and analyzing already present information on a given topic. Conceptual research doesn’t involve conducting any practical experiments. It is related to abstract concepts or ideas. Philosophers have long used conceptual research to develop new theories or interpret existing theories in a different light.

For example, Copernicus used conceptual research to come up with the concepts of stellar constellations based on his observations of the universe. Down the line, Galileo simplified Copernicus’s research by making his own conceptual observations which gave rise to more experimental research and confirmed the predictions made at that time.

The most famous example of conceptual research is Sir Issac Newton. He observed his surroundings to conceptualize and develop theories about gravitation and motion.

Einstein is widely known and appreciated for his work on conceptual research. Although his theories were based on conceptual observations, Einstein also proposed experiments to come up with theories to test the conceptual research.

Nowadays, conceptual research is used to answer business questions and solve real-world problems. Researchers use analytical research tools called conceptual frameworks to make conceptual distinctions and organize ideas required for research purposes.

Conceptual Research Framework

Conceptual research framework constitutes of a researcher’s combination of previous research and associated work and explains the occurring phenomenon. It systematically explains the actions needed in the course of the research study based on the knowledge obtained from other ongoing research and other researchers’ points of view on the subject matter.

Here is a stepwise guide on how to create the conceptual research framework:

01. Choose the topic for research

Before you start working on collecting any research material, you should have decided on your topic for research. It is important that the topic is selected beforehand and should be within your field of specialization.

02. Collect relevant literature

Once you have narrowed down a topic, it is time to collect relevant information about it. This is an important step, and much of your research is dependent on this particular step, as conceptual research is mostly based on information obtained from previous research. Here collecting relevant literature and information is the key to successfully completing research.

The material that you should preferably use is scientific journals , research papers published by well-known scientists , and similar material. There is a lot of information available on the internet and in public libraries as well. All the information that you find on the internet may not be relevant or true. So before you use the information, make sure you verify it.  

03. Identify specific variables

Identify the specific variables that are related to the research study you want to conduct. These variables can give your research a new scope and can also help you identify how these can be related to your research design . For example, consider hypothetically you want to conduct research about the occurrence of cancer in married women. Here the two variables that you will be concentrating on are married women and cancer.

While collecting relevant literature, you understand that the spread of cancer is more aggressive in married women who are beyond 40 years of age. Here there is a third variable which is age, and this is a relevant variable that can affect the end result of your research.  

04. Generate the framework

In this step, you start building the required framework using the mix of variables from the scientific articles and other relevant materials. The research problem statement in your research becomes the research framework. Your attempt to start answering the question becomes the basis of your research study. The study is carried out to reduce the knowledge gap and make available more relevant and correct information.

Example of Conceptual Research Framework

Thesis statement/ Purpose of research: Chronic exposure to sunlight can lead to precancerous (actinic keratosis), cancerous (basal cell carcinoma, squamous cell carcinoma, and melanoma), and even skin lesions (caused by loss of skin’s immune function) in women over 40 years of age.

The study claims that constant exposure to sunlight can cause the precancerous condition and can eventually lead to cancer and other skin abnormalities. Those affected by these experience symptoms like fatigue, fine or coarse wrinkles, discoloration of the skin, freckles, and a burning sensation in the more exposed areas.

Note that in this study, there are two variables associated- cancer and women over 40 years in the African subcontinent. But one is a dependent variable (women over 40 years, in the African subcontinent), and the other is an independent variable (cancer). Cumulative exposure to the sun till the age of 18 years can lead to symptoms similar to skin cancer. If this is not taken care of, there are chances that cancer can spread entirely.

Assuming that the other factors are constant during the research period, it will be possible to correlate the two variables and thus confirm that, indeed, chronic exposure to sunlight causes cancer in women over the age of 40 in the African subcontinent. Further, correlational research can verify this association further.

Advantages of Conceptual Research

1. Conceptual research mainly focuses on the concept of the research or the theory that explains a phenomenon. What causes the phenomenon, what are its building blocks, and so on? It’s research based on pen and paper.

2. This type of research heavily relies on previously conducted studies; no form of experiment is conducted, which saves time, effort, and resources. More relevant information can be generated by conducting conceptual research.

3. Conceptual research is considered the most convenient form of research. In this type of research, if the conceptual framework is ready, only relevant information and literature need to be sorted.

QuestionPro for Conceptual Research

QuestionPro offers readily available conceptual frameworks. These frameworks can be used to research consumer trust, customer satisfaction (CSAT) , product evaluations, etc. You can select from a wide range of templates question types, and examples curated by expert researchers.

We also help you decide which conceptual framework might be best suited for your specific situation.

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• v.373(1752); 2018 Aug 5

Varieties of abstract concepts: development, use and representation in the brain

Anna m. borghi.

1 Department of Dynamic and Clinical Psychology, Sapienza University of Rome, Via degli Apuli 1, Rome 00185, Italy

2 Institute of Cognitive Sciences and Technologies, Italian National Research Council, Via San Martino della Battaglia 44, Rome 00185, Italy

Laura Barca

Ferdinand binkofski.

3 Division for Clinical Cognitive Sciences, University Hospital Aachen, Pauwelsstrasse 17, 52074 Aachen, Germany

Luca Tummolini

Associated data.

This article has no additional data.

The capacity for abstract thought is one of the hallmarks of human cognition. However, the mechanisms underlying the ability to form and use abstract concepts like ‘fantasy’ and ‘grace’ have not been elucidated yet. This theme issue brings together developmental, social and cognitive psychologists, linguists, anthropologists, cognitive scientists, neuroscientists, philosophers and computer scientists to present theoretical insights and novel evidence on how abstract concepts are acquired, used and represented in the brain. Many of the contributions conceive concepts as grounded in sensorimotor systems and constrained by bodily mechanisms and structures. The theme issue develops along two main axes, related to the most promising research directions on abstract concepts. The axes focus on (i) the different kinds of abstract concepts (numbers, emotions, evaluative concepts like moral and aesthetic ones, social concepts); (ii) the role played by perception and action, language and sociality, and inner processes (emotions, interoception, metacognition) in grounding abstract concepts. Most papers adopt a cognitive science/neuroscience approach, but the theme issue also includes studies on development, on social cognition, and on how linguistic diversity shapes abstract concepts. Overall, the theme issue provides an integrated theoretical account that highlights the importance of language, sociality and inner processes for abstract concepts, and that offers new methodological tools to investigate them.

This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.

1. Introduction

Compared to concrete concepts like ‘bottle’, abstract concepts like ‘fantasy’ refer to more complex situations and do not possess a single and perceptually bounded object as referent; furthermore, their content is more variable both within and across individuals [ 1 , 2 ].

Understanding how abstract concepts might be represented is a crucial problem for contemporary research. This challenge has become particularly topical in recent years, due in large part to the development of embodied and grounded theories of cognition (e.g. [ 3 – 12 ]). In the past few years a number of embodied proposals have been advanced, aiming to show that abstract concepts are grounded in the sensorimotor system, like concrete concepts. Our special theme issue is characterized by an embodied and grounded approach to abstract concepts; at the same time, most contributions recognize that in order to fully account for the representation of abstract concepts an extension beyond purely grounded approach is needed.

Several trends in the recent literature on abstract concepts (review: Borghi et al . [ 13 ]) provide a background for our special theme issue ( figure 1 ). The first is the acknowledgement that it is necessary to distinguish different kinds of abstract concepts and their corresponding brain representations. The second trend is the emergence of multiple representation views. Finally, a third trend explores the variability of abstract concepts across natural languages.

A sketch of our theme issue. The figure illustrates the fact that varieties of abstract concepts exist, and that abstract concepts are grounded in multiple dimensions—perception–action, but also language, sociality and inner processes, in particular interoception and metacognition.

Abstract concepts cover a vast domain, ranging from numbers to emotions, and from social roles to mental state concepts. This heterogeneity is one of the main reasons why it has been difficult to find a theory able to account for the variety of abstract concepts. However, only few papers have started to analyse the different kinds of abstract concepts, and new methodological tools have facilitated these efforts. Future research will have to explore this domain further and identify sub-typologies of abstract concepts, investigating their differences in content, mechanisms and neural underpinnings. Providing a clear analysis of the different kinds of abstract concepts is therefore crucial and urgent. In our theme issue we have included papers that propose new tools to investigate fine-grained differences between kinds of abstract concepts [ 14 – 16 ] and papers that focus on specific sub-kinds of abstract concepts (e.g. numbers [ 17 ]; emotions [ 18 , 19 ]; evaluative concepts like aesthetic and moral ones [ 20 ]).

The second trend is the emergence of multiple representation views. According to such approaches abstract concepts are grounded in sensorimotor systems but also involve linguistic, emotional and social experiences as well as internal experiences. For example, embodied approaches could be combined with statistical/distributional approaches that emphasize the importance of linguistic experience [ 21 ]. Multiple representation views are the most promising candidates to account for abstract concepts in their diversity and variety. However, the specific mechanisms of such accounts still need to be elucidated. Both abstract and concrete concepts are grounded in perception and action, but to a different extent. Future research will need to convincingly highlight the main dimensions that characterize abstract concepts (linguistic, social, emotional) and to verify whether these dimensions assume different values for different kinds of abstract concepts. In our theme issue we have included a section on grounding of abstract concepts in perception and action systems [ 22 – 27 ] and two sections focusing on the roles of the inner experience (situatedness in inner processes [ 28 ], interoception [ 29 ] and metacognition [ 30 ]) and of the linguistic and social experience in grounding abstract concepts [ 31 – 35 ].

A third trend concerns the variability of abstract concepts across natural languages. Recent research inspired by neo-whorfian ideas shows that natural languages shape the way we think about and use concepts [ 36 , 37 ]. Abstract concepts are more detached from sensory experiences, and so could be more affected by linguistic variability than concrete concepts. As such, one paper in the issue focuses on a specific kind of abstract concepts, i.e. odour concepts, from a crosslinguistic perspective [ 38 ].

2. Kinds of abstract concepts

The necessity to provide a precise analysis of the differences between kinds of abstract concepts is now widely recognized. The first part of this section presents two studies proposing new insights and methodological tools that reveal fine-grained differences between kinds of concepts [ 14 , 15 ]; the second part of the section includes papers that focus each on a specific kind of abstract concept (numbers [ 17 ], emotions [ 18 , 19 ], moral and aesthetic concepts [ 20 ], social concepts [ 16 ]).

Desai, Reilly and van Dam [ 14 ] investigate the neural basis of four types of abstract concepts (numerical and emotional concepts and two higher-order abstract processes, morality judgements and theory of mind), examining their similarities and differences through meta-analyses. Desai et al . demonstrate that all four concepts engage areas largely overlapping with those activated by concrete concepts, indicating that abstract concepts are indirectly grounded in objects and situations. The results also show significant overlaps in the activations of morality and theory of mind concepts, which are likely processed referring to social and episodic memories or to emotions and imagery. The heterogeneity of abstract concepts and the widespread distribution of their brain representation cast doubts on theories that propose a single mechanism to account for all kinds of abstract concepts.

Ghio, Haegert, Vaghi and Tettamanti [ 15 ] present a functional magnetic resonance imaging study, in which they manipulated sentence polarity (affirmative, negative) and different kinds of abstract (mental state, emotion, mathematics) and concrete (related to mouth, hand, leg actions) concepts. This is particularly interesting because sentence polarity is considered to be at the interface between syntax and semantics. Applying a multivariate pattern analysis approach, they find clear distinctions between affirmative abstract, negative abstract, affirmative concrete and negative concrete sentences. Ghio et al . conclude that sentential negation polarity modulates brain activation in distributed semantic networks.

Fischer and Shaki [ 17 ] provide a review of empirical work on the cognitive signature of numerical knowledge, such as the numerical distance effect and the size effect. The discussed evidence indicates that number concepts are placed along the continuum from abstract to concrete (or modal) representations, where numbers (just like other conceptual knowledge) are treated by recurring to specific sensory-motor experiences.

Winkielman, Coulson and Niedenthal 's paper [ 18 ] focuses on emotion concepts, a peculiar kind of concepts because they differ from other abstract concepts in having ‘some form of bodily information as critical, necessary (but not sufficient) components’. The authors illustrate how emotion concepts are formed, represented and used, reviewing current behavioural and neural literature on them. They propose and defend an embodied theory of emotional concepts, the CODES (context-dependent embodied simulation) view. A crucial assumption of this view is that the link between concepts and somatosensory and motor involvement is highly flexible and context-dependent.

Brookshire and Casasanto [ 19 ] use transcranial direct current stimulation (tDCS) to study the link between affective motivation and motor actions. Affective motivation is cerebrally lateralized according to handedness, specifically with the hand typically used to perform approach/avoidance-related actions. Repeated tDCS stimulation increased neuronal excitability in either the participants' left or right dorsolateral prefrontal cortex (DLPF). Such stimulation changed participants' experience of approach-motivated emotions: that is, in right-handers, the experience of approach emotions such as ‘enthusiastic’ increased after left-excitatory stimulation relative to right-excitatory stimulation. The opposite pattern emerged for left-handers, thus providing evidence for the grounding of emotional concepts in spatio-motor experience.

Within the variety of abstract concepts, moral concepts like ‘justice’ and ‘freedom’ have often been considered as a paradigm case, which also presents a challenge for embodied and grounded approaches. In their Opinion piece, Fingerhut and Prinz [ 20 ] argue that moral concepts belong to the more general domain of evaluative concepts, which also include aesthetic concepts like ‘beauty’. In both morals and aesthetics, concepts are used to evaluate things as good or bad, which is something that goes beyond mere perception. The authors focus in particular on moral badness and aesthetic goodness and argue that, when we conceptualize something as good or bad, we experience our bodily responses to that thing. The moral and aesthetic domains are distinguished by the different emotions that they evoke.

Rice, Hoffman, Binney and Lambon Ralph [ 16 ] use data of three neuroimaging studies comparing category-selective responses within the anterior temporal lobes (ATLs) to test the hub-and-spoke theory, which suggests that the ATL contribution to semantic representation is transmodal and concerns all categories. Rice et al . compare the hub-and-spoke theory with an alternative theory stating that this region's responses are modality- and category-selective, and respond specifically to socially relevant concepts including faces. The results of the three studies can be accommodated by a graded version of the hub-and-spoke model. An anterior ventral ATL region responds to images of people but also to their spoken names (transmodality), while the ‘core’ ventral ATL responds more strongly to all conceptual categories.

3. Grounding of abstract concepts in multiple systems.

An emergent view proposes that abstract concepts are not only grounded in perception and action, but also in language, sociality and emotions. Section 3a provides evidence that abstract concepts are grounded in sensorimotor system. The contributions of sections 3b and 3c move from the assumption that, since abstract concepts are more detached from sensorial experience than concrete ones, they rely more on the inputs of others and require more internal resources to be processed. Thus, compared to concrete concepts they would rely more on emotions and internal inputs and would involve more linguistic and social experiences.

(a) Grounding in perception and action experience

The contributions in this section focus on how abstract concepts are embodied and grounded in perception and action systems. One paper [ 22 ] addresses grounding and embodiment of abstract concepts from a comparative and phylogenetic point of view, another one [ 23 ] demonstrates the role and integration of sensorimotor and linguistic experience selecting a special case, that of the concept of causation. Two experimental papers [ 24 , 25 ] demonstrate grounding of concepts of gender, number and time, one paper [ 26 ] overviews current computational and robotics studies on grounding of abstract concepts, and finally one paper [ 27 ] provides a critical perspective on possible limitations of a grounded approach.

Cuccio and Gallese [ 22 ] develop an embodied view on abstract concepts, contrasting it with the so-called Computational and Representational Theory of Mind. In presenting their approach, they start from a comparative phylogenetic perspective. They argue that, phylogenetically, both abstract and concrete concepts are grounded in Embodied Simulation mechanisms and in experience-based bodily regions. At the same time, concrete and abstract concepts might have differences in acquisition and representation. Cuccio and Gallese [ 22 ] propose that the Peircean notion of icon and abduction can provide the tools to understand the mechanisms underlying embodied simulation with both concrete and abstract concepts.

Pulvermüeller [ 23 ] analyses learning and grounding of abstract concepts in experience, focusing on a specific example. He namely investigates how, from causal events, we come to form and understand the concept of causation. He proposes a model, specified in its cortical circuits, and identifies two operating mechanisms: family resemblance and linguistic symbols. In the model, semantic anchor neurons connect word forms to word referents; these connections are stronger for concrete than for abstract words. The higher variability of abstract concepts is captured by a family resemblance pattern of partial overlap. Hence, according to the model, different causal actions are put together through family resemblance: causal binding is guaranteed by the similar perception–action features shared by different causal events. Linguistic symbols are then used to put together the different semantic features of the subsets of causal actions.

Just as affective evaluations activate the perceptual contrast between brightness and darkness, Semin, Palma, Acartürk and Dziuba [ 24 ] draw on research in physical anthropology to advance the hypothesis that the same sensory dimension could also ground the abstract category of gender, with light colours being used to mark the concept of ‘female’ and dark colours to mark the concept of ‘male’. The authors find convergent support for this hypothesis in three experimental studies that employ different paradigms. Whereas the valence–brightness mapping has been understood as a metaphorical mapping, the authors conjecture that conceptual metaphors are not responsible for the gender–brightness mapping whose experiential origins could potentially be linked to a systematic difference in shades of skin colour between the sexes.

Dimensional abstract concepts such as time and numbers are mentally represented along a mental line. In the search for the mechanisms that might generate the spatial bias of time and numbers, Roman, Flumini and Santiago [ 25 ] explore as a potential candidate the directionality adopted by caregivers when exploring pictures or reading books to their children. The authors presented a speechless comic in either standard (left-to-right) or mirror reversed (right-to-left) form to adult participants, and then asked them to draw three geometrical objects whose relative position is specified by auditorily presented sentences (e.g. ‘the square is between the cross and the circle’). The idea is that the directionality induced by the comic exploration affects the construction of a mental model, thus creating a spatial bias that influences the drawing task. Results from three experiments converge in suggesting that the directionality adopted when presenting visual materials to children might induce the early start for spatial biases.

Adopting an embodied and grounded approach to abstract concepts typically assumes that, similarly to concrete concepts, they ultimately have some kind of experiential origin. Detailed computational models of how this process is possible, however, are still very scant. In their contribution, Cangelosi and Stramandinoli [ 26 ] review the state of the art on this important issue from the perspective of the design of artificial cognitive agents, i.e. robots that are capable of grounding concepts and words by integrating perception and action via direct experience. The authors discuss two main strategies that have been explored to ground concepts without direct sensorimotor experience of their referents. In the ‘grounding transfer’ strategy, new concepts and words are acquired via word combinations whose meaning have been previously learned via direct grounding. Alternatively, a different strategy for learning abstract concepts is to combine gestures and action with words, such as in the use of finger counting to teach a child (or a robot) to count. Fully implemented robotic models of both strategies are discussed.

In their Opinion piece, Pecher and Zeelenberg [ 27 ] raise doubts on whether sensory-motor grounding alone can fully explain abstract concepts. Reviewing the key tenets of two important approaches (conceptual metaphor theory and situated conceptualization), they insist that the indirect grounding strategy that these approaches assume has been challenged by recent evidence indicating that even concrete concepts are not always grounded in sensory-motor processes. From this perspective, hybrid models that combine sensory-motor experience and language emerge as a more viable option.

(b) Grounding in inner experience

The papers in this section focus on the importance of the distinction between external versus internal situational elements [ 28 ] and investigate theoretically and empirically how inner experiences, especially interoception [ 29 ] and metacognition [ 30 ], influence abstract concepts representation.

Challenging standard approaches in the literature, Barsalou, Dutriaux and Scheepers [ 28 ] propose to abandon the traditional distinction between concrete and abstract concepts. The authors insist that concepts emerge to support situated action, i.e. the action of an embodied agent embedded in a physical and social environment. In this view, a concept derives its meaning in interaction with other concepts representing other situational elements together with their integration. As a consequence, Barsalou et al . [ 28 ] claim that current approaches that study concepts in isolation have provided a distorted account. According to the authors, a more complete understanding of concepts requires their study in the context of situated action. In this perspective—the situated conceptualization framework—the authors offer a new account of abstract concepts in terms of two new distinctions: (i) external versus internal situational elements, and (ii) situational elements versus situational integrations.

Connell, Lynott and Banks [ 29 ] focus on the grounding of concepts on interoception (i.e. sensation within the body). They present a mega-study based on the collection of modality-specific ratings of perceptual strength for over 30 000 words. Analysis of naming and lexical decision response time on a selected sample of 500 concepts documents the importance of interoceptive information for the perceptual grounding of abstract concepts, and even more of emotional concepts.

Going beyond merely sensorimotor resources, in his Opinion piece, Shea [ 30 ] explores whether metacognition about concepts—the thoughts and feelings that thinkers have about a concept—can itself ground abstract concepts. Focusing on how abstract concept can be grounded in characteristics that make them distinctive from one another, Shea identifies two examples of this possibility. One such example is the judgement that we should defer to others in how a given concept is used. Shea argues that metacognitive deference can either be explicit or implicit, but that in both forms it can provide a new resource to understand how some abstract concepts are grounded. Another example is our internal assessment of which concepts are useful. Although metacognition is potentially important for grounding concrete concepts as well, this resource, together with the connection to a wider group of concept-users that it enables, is especially important for abstract concepts.

(c) Grounding in linguistic and social experience

The contributions in this section focus on the role of linguistic and social experience for abstract concepts. The section focuses on how language and social interaction shape abstract concepts [ 31 , 32 , 34 ], on how the re-enactment of linguistic experience might have an embodied counterpart, i.e. the activation of the mouth [ 32 ], and on the role of iconicity in concrete and abstract concepts [ 33 , 34 ]. One paper [ 35 ] casts doubts on the exclusive importance ascribed to language for conceptual acquisition, when linguistic information is dissociated by a grounded approach. Finally, a paper [ 38 ] explores how different natural languages influence the concept of odour, assuming in a neo-whorfian perspective that our concepts are shaped by the various spoken languages.

Borghi, Barca, Binkofski and Tummolini [ 31 ] propose that words, as social tools, extend our cognitive capabilities and induce us to rely on others to complement our knowledge. In their view, the WAT (Words As social Tools) view, linguistic, social and inner experience play a role of paramount importance for abstract concepts. Consistently with this view, they illustrate recent evidence obtained with children and adults showing that the activation of linguistic experience leads to the involvement of the mouth motor system, and discuss the mechanisms underlying such involvement. The activation of the mouth motor system could be due to the re-enactment of the acquisition and experience, to the re-explanation of the word meaning through inner speech, or to a metacognitive mechanism. Specifically, the authors focus on a process that they call ‘social metacognition’. This process implies the recognition of the inadequacy of our concepts and the need to rely on others' competence/knowledge to integrate them.

Dove [ 32 ] focuses on the role played by language in concepts, proposing that language is an external symbolic system that we use in an embodied way, endowed with a powerful influence on our cognition: in his words, ‘language is an ontogenetically disruptive cognitive technology that expands our conceptual reach’. He reviews theoretical and empirical literature on this issue and advances four predictions, discussing them in light of recent evidence: (i) concepts are grounded first of all directly in action, perception and emotional system; (ii) language plays a major role in the representation of abstract concepts; (iii) language influence is flexible and context-dependent; (iv) the role of language differs over the course of development.

Lupyan and Winter [ 33 ] address two apparently related questions: how abstract is language, and why isn't language more iconic (iconicity = similarity between form of words and word meanings)? They demonstrate that abstractness is a pervasive linguistic phenomenon, and contend that in order to understand it we should turn to language. Language namely describes facts that guide our actions, it helps categorization, and language statistics provide a rich source of knowledge. The authors propose that languages are not highly iconic exactly because abstractness is so pervasive. Highly iconic words do not have an arbitrary relationship to their referents, they are more concrete and connected to more specific contexts than abstract words. If languages were highly iconic they would lose some of the flexibility that guarantee the possibility to abstract. Iconicity would thus render it more difficult, both to learn and to express abstract meanings. By not being iconic, ‘words can take on a life of their own, helping to carve joints in nature’.

Zdrazilova, Sidhu and Pexman [ 34 ] used a novel task, the taboo task, in which participants had to communicate the meanings of concrete and abstract words without using the target-word. Results reveal clear differences: with abstract words, participants referred more to people and to introspection and used more metaphorical and beat gestures, whereas with concrete words they referred more to objects and entities and their speech was accompanied by more iconic gestures. Consistently with multiple representation views, abstract concepts referenced different kinds of experiences, especially internal and social ones.

Ponari, Norbury, Rotaru, Lenci and Vigliocco [ 35 ] question the different role that language (in particular the statistical co-occurrence of words) might have for the acquisition of abstract and concrete words. They study the performance of children with Developmental Language Disorder (DLD) in an auditory lexical decision task and in a semantic definition task, with the hypothesis that, given their linguistic deficit, this group's performance should be worse for abstract words than for concrete ones. The absence of a different accuracy between the two types of concepts, the authors suggest, questions the supposedly prominent role of linguistic information for abstract words.

Odours are often considered difficult to conceptualize and notoriously difficult to verbalize: for this reason, Majid, Burenhult, Stensmyr, de Valk and Hansson [ 38 ] asked Dutch and Jahai speakers, i.e. speakers from a population of hunter-gatherers of the Malay Peninsula, to name odours, measuring response times and facial expressions. Compared to Dutch speakers, Jahai speakers were both more succinct and quicker in naming odours, using abstract concepts (e.g. musty) rather than referring to concrete odour sources (e.g. smells like lemon). Emotional reactions to odours instead did not differ across the two cultures/languages. The variation of odour terms across cultures suggests that different cultures and languages can differently shape our concepts—and this might happen in particular for concepts that do not refer directly to a concrete, single object, as do odour concepts.

4. Conclusion

The theme issue has succeeded in putting together state-of-the-art research on abstract concepts, in suggesting new methodological tools and in identifying new research directions. The contributions help us to reach some preliminary conclusions that might be reframed as questions useful to pave the way for further research.

First, the very notion of abstract concepts should be rethought, in light of the variability of the results concerning different kinds of abstract concepts. The old fashioned contrast between concrete and abstract concepts should be discarded in favour of the idea of a multidimensional space, in which concepts differing both in abstractness level and along other content dimensions are distributed; importantly, in some cases the role of these dimensions and what is abstract and concrete can vary depending on the culture and the spoken language.

Second, while embodied and grounded sensorimotory foundations of abstract concepts are not under discussion, at the same time the majority of contributions converge in showing that to fully account for abstractness other sources of experience beyond perception and action should be considered. Among these, interoception, sociality and language play a major role.

Third, the contributions highlighted the necessity of an integrated perspective that considers both conceptual acquisition and development (two papers focused on this [ 31 , 35 ]) and conceptual representation in the brain.

Future research should then lead to the emergence of a multiple representation view, flexible enough to account for abstract concepts in their varieties, and to explain their acquisition and representation. We hope that this theme issue has contributed to paving the way for further research on abstractness, this ubiquitous and extremely sophisticated characteristic of human cognition and language.

Acknowledgements

Thanks to Penny Pexman for editing the proposal that is at the origin of this manuscript. A special thanks to Giovanni Pezzulo, who suggested that we submit our ideas to Philosophical Transactions B .

Data accessibility

Competing interests.

We have no competing interests.

We received no funding for this study.

• Review article
• Open access
• Published: 30 January 2017

Grounded understanding of abstract concepts: The case of STEM learning

• Justin C. Hayes 1 , 2 &
• David J. M. Kraemer 2  

Cognitive Research: Principles and Implications volume  2 , Article number:  7 ( 2017 ) Cite this article

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Characterizing the neural implementation of abstract conceptual representations has long been a contentious topic in cognitive science. At the heart of the debate is whether the “sensorimotor” machinery of the brain plays a central role in representing concepts, or whether the involvement of these perceptual and motor regions is merely peripheral or epiphenomenal. The domain of science, technology, engineering, and mathematics (STEM) learning provides an important proving ground for sensorimotor (or grounded) theories of cognition, as concepts in science and engineering courses are often taught through laboratory-based and other hands-on methodologies. In this review of the literature, we examine evidence suggesting that sensorimotor processes strengthen learning associated with the abstract concepts central to STEM pedagogy. After considering how contemporary theories have defined abstraction in the context of semantic knowledge, we propose our own explanation for how body-centered information, as computed in sensorimotor brain regions and visuomotor association cortex, can form a useful foundation upon which to build an understanding of abstract scientific concepts, such as mechanical force. Drawing from theories in cognitive neuroscience, we then explore models elucidating the neural mechanisms involved in grounding intangible concepts, including Hebbian learning, predictive coding, and neuronal recycling. Empirical data on STEM learning through hands-on instruction are considered in light of these neural models. We conclude the review by proposing three distinct ways in which the field of cognitive neuroscience can contribute to STEM learning by bolstering our understanding of how the brain instantiates abstract concepts in an embodied fashion.

Significance

Increasing academic proficiency in science, technology, engineering, and mathematics (STEM) fields is not only a goal of educators in these disciplines, but also a national priority spurred on by international comparisons revealing that US high school students currently rank 27 th in mathematics and 20 th in science out of the 34 nations that comprise the Organisation for Economic Co-operation and Development (OECD, 2012 ). As new technologies have emerged in recent decades that allow for a more detailed exploration of the inner workings of the brain, there appears to be the promise of brain research becoming a useful resource for improving educational outcomes. However, while research on the brain basis of learning and memory has greatly advanced our understanding of brain function, it has not often been clear how this research can translate to the classroom and inform educational practice. A clearer understanding of the neural basis of STEM learning in general, and a precise evaluation of hands-on learning activities in particular, may be able to play a role in developing activities and structured curricula that allow students to grasp certain fundamental STEM concepts. In the present review, we explore the connection between grounded cognition—the notion that knowledge partially relies on neural mechanisms pertaining to sensory and motoric processes—and STEM learning, evaluating several theories describing how the brain supports concept learning and proposing new research avenues awaiting exploration.

Introduction

Semantic knowledge consists of the non-episodic, conceptual information human beings use to understand the world (Tulving, 1984 ). One recognizes the objects in his or her environment by performing neural computations enabling access to a vast repository of information. An object’s identity, function, properties, and form reside in this semantic network, in addition to the abstract knowledge required to understand intricate mathematical and scientific theories. Importantly, a distinction must be made between the ways that the term abstract has been used in the literature. For instance, the concept dog can, hypothetically, be represented with a prototype that does not refer directly to an actual dog, but instead a statistical aggregation of the most frequently encountered features of all dogs (e.g., Rosch, 1973 ). In this way, a mental construct that represents a generalizable form, but not a discrete instantiation, of a concrete object can be construed as abstract. The second sense in which the term abstract is often used denotes a concept lacking a tangible referent in the real world (e.g., Paivio, 1965 ), such as justice or peace . In the present discussion, we use the term abstract primarily in this second sense, to refer to intangible concepts (e.g., peace, rebellion). Accordingly, we will use the term concrete when referring to concepts such as dog , although we proceed with the understanding that it is necessary to generalize or abstract across exemplars of dogs to refer to a common label. In other words, we consider a concept to be concrete if it refers to an object that may be perceived directly in the world, while abstract concepts rely entirely on relational properties between other concepts (e.g., peace is an emergent property of a given state of other concepts and their interactions and relationships to each other).

An effort to understand how abstract concepts relating to science and mathematics are most effectively learned is imperative given that the US educational system now ranks 35 th in mathematics and 29 th in science, compared to other industrialized nations (DeSilver, 2015 ). Therefore, domestic policy makers and educators alike are concerned with the state of learning in STEM disciplines. It is no surprise, then, that educational researchers are eager to understand both the mechanisms enabling conceptual knowledge to be stored, accessed, and manipulated, and those optimizing the semantic network for increased learning proficiency.

Theories of embodied cognition, or grounded cognition (GC), may play an integral role in the search for a means to improve STEM learning. Adherents of theories related to GC maintain that sensorimotor networks—brain regions (located in sensorimotor cortex and nearby association cortex) that are preferentially responsive to information within a specific sensory modality—play a prominent role in information processing and semantic retrieval (for a recent review of relevant neuroimaging data, see Martin, 2016 ). Such networks consist of simultaneously activated brain regions representing the properties of a given concept—for example, seeing a tool activates left hemisphere areas including the ventral fusiform cortex, parietal cortex, and ventral premotor cortex (vPMC), regions associated with visual object identification (form, color, shape, etc.), and manipulation, respectively. Additionally, as frontal and parietal sensorimotor activation occurs not only amidst cognitively demanding tasks (e.g., planning for the use of a hammer), but also while individuals passively view images of tools (e.g., Chouinard & Goodale, 2010 ), it seems likely that such properties relate to the tool concept itself and not only to specific task demands. With these findings and similar findings related to the embodied nature of semantic knowledge (e.g., Goldberg, Perfetti, & Schneider, 2006 ; Hauk, Johnsrude, & Pulvermüller, 2004 ) in mind, it is surprising how little is known regarding the influence of embodied processes on STEM learning (Han & Black, 2011 ; Kontra, Lyons, Fischer, & Beilock, 2015 ). Whereas a number of investigations (e.g., Freeman et al., 2014 ; Winstone & Millward, 2012 ; Zacharia et al., 2015 ) have had success exploring teaching methods beyond lecturing—enhancing student engagement through reading, writing, group discussion, and virtual laboratories—we aim to elucidate the benefits of hands-on approaches to learning, an important component in the effort to improve STEM pedagogy.

In the present review, we argue that the cognitive science literature has much to glean from future studies considering how the STEM pedagogy benefits from hands-on activities, designed to bolster the conceptual knowledge underlying scientific learning. We begin this discussion by clarifying the definition of grounded cognition, a controversial term than has been applied to several distinct theories, and then direct the discussion to grounded theories of abstract (e.g., lacking a real-world referent) conceptual processing and representation in the human brain. Finally, we explore possible ways that learning interventions based on theories of GC can benefit students in STEM fields by proposing new empirical directions, bolstering the field’s current understanding of the benefits of tactile learning in both live and virtual environments.

Divergent theories of semantic processing

The basic premise of grounded (for our purposes we will interchangeably use the terms grounded, embodied, and situated cognition, but see Barsalou, 2008 , for a discussion of some important differences) theories of cognition is that the brain, body, and environment comprise a single, dynamic system. Such a system enables thinking organisms to extend cognition beyond the central nervous system, enhancing computational efficiency by taking advantage of primordial cortical processes (e.g., Dehaene & Cohen, 2007 ). Considering a knowledge system comprised of these three components has important implications: neural organization necessarily influences how the body and environment are perceived, the body sends feedback to the brain and is used as a metric for navigating the environment (e.g., Witt & Proffitt, 2008 ), and the environment constrains the ways in which complex behaviors may be executed (Chiel & Beer, 1997 ). Within this framework, context necessarily influences how knowledge is retrieved and subsequently represented (Kiefer & Pulvermüller, 2012 ; Yee & Thompson-Schill, 2016 ), given that context (i.e., geographical, situational, spatial, emotional, cognitive) is an integral component of one’s experiences and the associated concepts. Thus, context influences how a given concept manifests across novel situations. For instance, a hammer is a small hand-held tool used to drive nails when considering a household project, and an ornate weapon of war when contemplating the Norse God of Thunder. Substituting the former object in the context of the latter situation would be erroneous—in other words, one unfamiliar with the story of Thor would be making a semantic error when bringing the household tool to mind while reading about the Demigod’s weapon of choice. Yet, to an outsider, this mistaken individual would appear to fully comprehend the sentence “Thor’s weapon of choice was a hammer.” This illustration explains how two people may have a conversation about an object regardless of whether they have the same object in mind. Therefore, according to the GC view, semantic knowledge is inextricably linked to the context in which it is retrieved, yet flexible enough to be socially transmittable.

Amodal theories of semantic representation

The nature of the semantic system is a contentious topic. Amodal theorists (e.g., Fodor, 1998 ; Mahon & Caramazza, 2009 ) posit that findings typically attributed to GC are epiphenomenal—i.e., not centrally related to the concepts at hand—and that an amodal symbolic system enables concept retrieval. Accordingly, they argue, while sensorimotor activity enhances semantic representations it is not constitutive of conceptual knowledge, but instead results from spreading neural activation following the retrieval of a given concept in an amodal symbolic system. The key distinction here is that the central component of a concept—that which is crucial to truly knowing it—must exist as an amodal symbol, prima facie, in order for the activity to spread to distinct sensorimotor areas following concept activation. Adherents of this type of theory cite examples of category-specific semantic impairments in clinical populations following lesions to the left temporal lobe, illustrating the modality independence of discrepancies in the semantic network. For instance, cases of double-dissociations in patients unable to recognize either animate or inanimate objects, regardless of the presentation modality, provide support for this theory. Given that focal lesions to this so-called semantic hub can disrupt an entire category of semantic content, the argument for a single cortical conceptual system, divided along categorical lines, is compelling; however, this is not the only interpretation of the available patterns of neuropsycholgical evidence. A number of case studies (e.g., Carbonnel, Charnallet, David, & Pellat, 1997 ; Kemmerer, Rudrauf, Manzel, & Tranel, 2012 ; McCarthy & Warrington, 1988 ) challenge the notion that concept categories exist amodally, represented via the interactions of discrete, iconic symbols located in an innate module in MTL.

Multimodal theories of semantic representation

Adherents of grounded theories of semantic representation deny the requirement of elementary conceptual representations for semantic models to function properly. Instead, they argue that concept representations are both multimodal and contextually unique, relying on networks distributed throughout the cortex and reconstructed using the features (shape, texture, sound, etc.) and modalities (visual, phonological, tactile, etc.) in which they were acquired (e.g., Allport, 1985 ; Carbonnel et al., 1997 ; Farah & McClelland, 1991 ; Hsu, Frankland, & Thompson-Schill, 2012 ; Martin, 2016 ; McCarthy & Warrington, 1988 ). For instance, McCarthy and colleagues describe the case of patient (T.O.B.) who was unable to recognize the spoken names of animals, yet, when asked to identify photos of animals, provided robust descriptions. Due to the consistency of the patient’s impairment over time, the authors suggest a phonological impairment leading to semantic deficits. Patient E.C., on the other hand, suffered from complete visual agnosia, in addition to difficulties with non-visual knowledge of animals (Carbonnel et al., 1997 ). When asked to identify animals using verbal cues, E.C. was better at categorizing domestic rather than wild animals—difficulty recognizing the latter, suggest the authors, exemplifies a visual impairment. In other words, his lack of experience interacting with wild animals restricted E.C.’s ability to represent them in modalities other than vision (also see Warrington & Shallice, 1984 ; Martin, 2007 ), while domestic animals, frequently encountered in everyday life, provide a wealth of multimodal (e.g., tactile, emotional, and auditory) experiences for the patient to draw from. Martin’s ( 2016 ) GRAPES (grounding representations in action, perception, and emotion systems) theory provides additional support for this idea, suggesting that while concepts are organized based on object properties, such properties are dependent upon one’s experiences—i.e., acquired through a specific sensory modality in accord with one’s physiology and environment. As an example, for sighted organisms, shape is represented in occipital cortex given that the property can be most easily extricated from objects in the environment using vision. In other words, although knowledge about an object’s shape is typically acquired using the eyes, it may be acquired through other senses if vision is unable to encode the property, as is the case for those with congenital blindness. Nevertheless, evidence (Kiefer & Pulvermüller, 2012 ; Ricciardi et al., 2009 ) suggests that object properties maintain their general location in the brain regardless of the modality in which they were obtained. For example, the congenitally blind represent object properties typically associated with vision, such as form, in the same areas of occipital cortex as sighted individuals.

Consistent with Martin’s theory and other theories of distributed semantic representations, Farah and McClelland ( 1991 ) developed a computational model of semantic memory, noting that categorical deficits emerge spontaneously following lesions within this network comprised entirely of modality-specific subcomponents. The authors assert that categorization results from the correlation between the properties of objects belonging to each category—e.g., living versus non-living. Living things, for instance, are represented primarily by visual traits due to their properties (e.g., visually distinct, non-manipulable) while non-living things are represented primarily based on functional traits due to their properties (e.g., highly manipulable). Furthermore, Farah and McClelland’s model demonstrated that non-primary representations (e.g., functional properties of living things) can be impaired following severe damage to the primary modality (e.g., visual properties of living things), and spared if the damage is minimal. This finding suggests that 1) disparate brain regions rely on reciprocal inputs to reach the threshold necessary to retrieve a given conceptual representation, and 2) whether or not a category is impaired depends on the extent to which the primary representational modality is disrupted. Such a notion is consistent with prior work, including Allport’s ( 1985 ) thesis, stating that distributed assemblies of neurons firing in distinctive patterns represent conceptual knowledge. Thus, Farah and McClelland’s model, informed by prior neuropsychological findings (McCarthy & Warrington, 1988 ; Warrington & Shallice, 1984 ), provides a parsimonious explanation for both categorical and non-categorical impairments of conceptual representation.

A hybrid theory of semantic representation

Perhaps pointing to a synthesis of these neuropsychological findings, hybrid theories of grounded cognition based in cognitive neuroscience (Barsalou, 1999 , 2008 ; Kiefer & Pulvermüller, 2012 ; Pulvermüller, 2013 ) argue that conceptual representation relies on both sensorimotor and multimodal (amodal) processes. Hypothesizing that distributed neural assemblies (DNAs) (Kiefer & Pulvermüller, 2012 ; Pulvermüller, 2013 ) comprise multiple contextually dependent semantic circuits, such theories account for both sensorimotor and abstract knowledge. This notion is consistent with Barsalou’s ( 1999 ) Perceptual Symbol Systems (PSS) theory, asserting that multimodal sensorimotor representations preclude the need for amodal symbols—those typically associated with atomistic conceptual theories (e.g., Fodor, 1998 ). Convergence zones (CZs; e.g., Damasio, 1989 ), cortical areas where streams of disparate neural traces converge, likely account for the influence that distinct sensorimotor areas exert upon one another (Farah & McClelland, 1991 ; Pulvermüller, 2013 ). Accordingly, nerve impulses originating in modality-specific regions and representing concept features (shape, color, etc.), propagate from their point of origin to adjacent regions, eventually synapsing on interneurons receiving input from additional modality-specific neurons. Such neurons not only integrate feature traces into a coherent representation, but also control timing via Hebbian learning mechanisms (simultaneous firing of NMDA receptors) feeding an integrated trace to higher cortical areas, resulting in robust, multimodal conceptual representations (Friston, 2003 ). Due to the parsimony of a hybrid, grounded/symbolic account of neural processing, we consider such a theory plausible in light of the following observations:

1. There are numerous studies illustrating the role of sensorimotor cortex in conceptual processing (for a review, see Kiefer & Pulvermüller, 2012 ; Bergen, 2012 ).

2. Amodal theories offer a narrow view of conceptual content (e.g., “knowing” is often operationalized as “naming”; Mahon & Caramazza, 2009 ), and there is ample neuropsychological, computational, and neuroimaging evidence that semantic information is multimodal (Allport, 1985 ; Carbonnel et al., 1997 ; Farah & McClelland, 1991 ; Goldberg et al., 2006 ; McCarthy & Warrington, 1988 ; for a review, see Martin, 2007 ).

3. Concepts are context dependent (e.g., Machery, 2009 ; for reviews, see Connell & Lynott, 2014 ; Yee & Thompson-Schill, 2016 ). Thus, a single, inflexible representational module cannot be assumed to exist in the absence of any direct evidence to that effect, given that multiple neural systems are necessary for contributing contextual cues (e.g., visuospatial regions in occipital and parietal cortex for visually coded geographic information). Without such cues, serving to enrich the meaning of a concept and rendering it contextually flexible, a semantic representation is rendered incomplete.

4. In agreement with Dehaene and Cohen’s ( 2007 ) Neuronal Recycling Hypothesis, grounding conceptual knowledge in perceptual and motor systems addresses the question of how advanced human intellectual systems, such as language and mathematical reasoning, developed across a relatively short evolutionary time scale (e.g., writing began ~5400 years ago). On the other hand, a dedicated module for semantic processing would require an evolutionarily expensive process unlikely to occur in such a short amount of time.

Thus, the semantic knowledge system appears to entail sensory-specific representations—organized according to within-modality features—relying on convergence zones to integrate information across modalities. So far, however, we have mainly considered data pertaining to knowledge of concrete objects. Given the importance of abstract knowledge for both education and general learning, we now consider how a grounded system supports knowledge of abstract concepts.

Sensorimotor contributions to abstract concept retrieval

The notion of a concept being abstract can be conceived in two distinct ways (see Introduction). For instance, any concept potentially represented in the mind by a prototype (e.g., one’s mental image of carrot; Rosch, 1973 ) could be characterized as being abstract. Here, we consider a second sense of the term abstract, one consistent with Paivio’s ( 1965 ) work, suggesting that nouns exist on a spectrum ranging from concrete to abstract, and that nouns representing concepts referring to perceptible objects evoke the most vivid mental imagery. Conversely, concepts with no tangible referent, such as peace and justice , are more difficult to process because they do not evoke the same type of mental imagery. Therefore, in the present discussion, we describe intangible concepts as abstract and those more directly accessible to the sensory system as concrete.

To further illustrate this distinction, consider that when one reads words such as justice and compassion in everyday encounters, such ideas are embedded in a situational context—e.g., a criminal being apprehended by the police or a family rescuing a dog from an animal shelter, respectively. Moreover, contexts vary drastically for abstract concepts given the flexibility of their meaning (Barsalou, 2008 ; Granito, Scorolli, & Borghi, 2015 ). One might describe bravery in the context of a soldier rescuing a fellow soldier on the battlefield; the term could also describe a shy student giving a speech in front of her peers. While the concept itself is representative of the same central idea across both of these contexts, i.e., carrying out an action despite one’s fears, these two situations share no common perceptual features. Instead, understanding that the concept bravery applies in both situations lies in comprehending the relationship between an agent and his or her context (actions, environment, etc.). Therefore, the underlying commonalities across these unique instantiations of such concepts are understood through analogical reasoning processes (e.g., Gentner, 1983 ; Gick & Holyoak, 1983 ). In other words, abstract concepts consist of the relational properties arising from the interaction of two or more objects or agents in a given circumstance, and such concepts share an underlying commonality despite dissimilarity on the surface-level (i.e., perceptual features). The multimodal contextual features of concrete concepts, on the other hand, are relatively more consistent across contexts, providing a great deal of information about the meaning of the concept. For instance, door knobs vary in subtle ways, but they generally maintain a recognizable form and are found within a proscribed area on almost any door. Therefore, we consider the primary distinction between abstract and concrete concepts to be the features comprising their representations—i.e., concrete concepts refer to tangible objects and abstract concepts refer to the emergent properties which result from the interaction of concrete concepts.

The context of abstract semantic content is unstable, while that of concrete semantic content is comparatively more durable—i.e., a carrot possesses diagnostic features that enable its identification despite subtle contextual discrepancies (a carrot may be brown but maintain its form; a carrot may have a different form but maintain its salient orange color, etc.). Therefore, it is more difficult to retrieve the meaning of abstract compared to concrete concepts. This is likely due to both the rapid recall of multimodal features associated with a given concrete object concept, and the computational demands necessary for retrieving the structural similarity between abstract concepts, resulting from unpredictable variation in contextual features across situations. Therefore, when asked to identify an abstract concept, in a word/non-word task for instance, if the concept is presented in isolation with no contextual information, one must construct a context in real time in order to understand the concept—this would explain why individuals are slower to identify abstract concepts (Schwanenflugel & Shoben, 1983 ; Xiao, Zhao, Zhang, & Guo, 2012 ).

Additionally, abstract concepts, unlike concrete concepts, in which an object is perceived before being assigned a label, require a label to subsume the contextual constraints associated with the concept. This account is consistent with the notion of Recchia and Jones ( 2012 ) that comprehending abstract words requires context-specific cues, while concrete words rely on object features. Namely, when processing words describing concrete concepts (e.g., car, dog ) in a scene, people tend to focus their attention to the features of the objects themselves; conversely, when learning novel abstract words (e.g., disorder ), attention is shifted toward the scene as a whole, while individuals attempt to establish the relationship between agents and objects in the display (Granito et al., 2015 ). Consider an image depicting the Boston Tea Party, when asked to identify the tea concept, one might point out the substance being dumped from bags into the harbor; conversely, if asked to identify rebellion, one would rely not only on the interaction between agents engaging in rebellious behavior, but also the geographic location, the affect of the agents and bystanders, and biographical knowledge gleaned in a history class. Thus, while identifying a concrete concept requires one to evoke a flexible yet consistent prototype acquired via statistical regularities across contexts (e.g., Kiefer & Pulvermüller, 2012 ), identifying a concept that is entirely relational requires a great deal more computation resulting from the process of discovering an analogous relationship between contexts, and thus searching a scene for cues denoting such a relationship which is necessary to determine if a given token fits the concept (see the “Predictive coding and Hebbian learning” section below).

Dove ( 2016 ) argues that abstract concepts are not easily reconcilable under current embodied theories, citing a number of physiological studies demonstrating activation differences while participants process concrete rather than abstract concepts. Furthermore, familiarity with such concepts results in their apparent dissociation from sensorimotor regions and an increased reliance on left hemispheric structures (see Binder, Westbury, McKiernan, Possing, & Medler, 2005 ). While such findings may appear to bolster amodal conceptual theories, suggesting distinctive mechanisms for processing concrete and abstract concepts, the shallow recognition protocols (e.g., lexical discrimination tasks) used in many such tasks fail to account for the contextual relativity inherent to abstract knowledge. In other words, recognizing a word does not require the same depth of information processing as retrieving its meaning. Xiao et al. ( 2012 ), for instance, argue that contextual details facilitate recollection of concrete words leading to faster RTs during a recognition task. Evidenced by an increased parietal P600 response for tangible concepts, an evoked potential typically associated with the integration of contextual details, the authors conclude that abstract words are more difficult to process given the absence of such details when interpreted independently of context, which is crucial for understanding them (Katja Wiemer-Hastings & Xu, 2005 ). In other words, concrete concepts (e.g., car, shoe ) evoke a rapid representation grounded in modality-specific features (visual, tactile, etc.) while abstract concepts (e.g., inequality ) require external conceptually relevant cues used to derive a complete understanding of the idea.

Thus, understanding abstract concepts requires one to comprehend the relationship between objects, rather than the objects themselves. This is an idea that is entirely consistent with the literature addressing analogical transfer (e.g., Gentner, 1983 ). Consider the resemblance between what we have characterized above as an abstract concept (e.g., preparation ) and a typical example of an analogy—one might, for instance, construct an analogy to describe preparation by comparing a student studying for a difficult examination to a long-distance runner training for a marathon (law student : studying :: marathon runner : training). Gick and Holyoak ( 1980 , 1983 ) refer to the perceivable properties typically associated with concrete concepts as surface features , while referring to those denoting the underlying relations between two or more terms of an analogy as structural features . Therefore, in our example, the two instances of preparation do not share surface features as there is no direct mapping between the perceptible features of a student studying for a law examination and a runner training for a marathon. However, the underlying relations between the analogies are consistent—i.e., both runners and students must engage in preparations in order to accomplish their goal. Thus, the structural properties of the two instantiations of preparation are shared, while the surface features of the two analogous scenarios are not.

A natural consequence of the human tendency to construct analogies is apparent when considering how metaphors are used to relate everyday notions to broader concepts. Lakoff and Johnson ( 1980 ), for instance, argue that almost all human conceptual thought relies on metaphor. Consider, for example, how Westerners conceive of competitive and/or contentious interactions as war-like—rap battles , fighting disease, culture warriors , etc. As Lakoff and Johnson point out, this framework is a cultural artifact, grounding concepts in a familiar, concrete context. Furthermore, assert the authors, such a framework influences how individuals behave—e.g., an argument is seen as an attempt to defeat another person’s position; as such, one approaches an argument to leave his or her opponent in a state of defeat . Thus, if an alternative, non-quarrelsome metaphor was used to characterize competitive exchanges, the nature of the interactions would change, as agents behave in a manner relative to the underlying framework. For example, consider an argument in the framework of a cooperative game—rather than striking a blow to an opponent’s argument, one might, instead, take a turn in order to advance toward a common goal . Similarly, Boroditsky ( 2011 ) considers the notion of time within such a culturally derived framework, summarizing several experiments that demonstrate how time is understood relative to culturally and linguistically informed constraints. For instance, time is often mapped onto spatial dimensions which vary according to how a given language describes temporal movement (e.g., time is up/down, forward/backward) and how that language is written (horizontally/vertically), providing an additional means to ground abstract concepts. Native speakers of Mandarin Chinese, for instance, regularly describe time as occurring vertically; English speakers almost exclusively refer to time as if it exists on a horizontal plane, advancing from left to right (Boroditsky & Gaby, 2010 ). Speakers of both English and Mandarin, therefore, conceive of time in a way that agrees with how their language is written. Thus, the culturally derived metaphors ubiquitous in natural languages may influence how individuals think about their worlds as they map specific concepts onto broader notions grounded in concrete ideas, such as spatial dimensions.

In addition to Lakoff and Johnson’s assertion that a metaphorical framework influences how concepts are understood, a related though contentious literature argues that physical states directly influence how abstract metaphors are understood. Consistent with the Western notion of the trajectory of time, one study found that individuals tended to lean forward when thinking about the future and backward when considering the past (Miles, Nind, & Macrae, 2010 ). Another study found that individuals who had recently recalled a situation where they were socially excluded guessed that the room they were in was colder than did individuals who had recalled a socially inclusive experience, suggesting that being ostracized (i.e., “treated coldly”) literally evokes a cold sensation (Zhong & Leonardelli, 2008 ). These outcomes suggest that human beings may understand abstract concepts by mapping them onto concrete objects or physical sensations with which they are able to directly perceive or experience. Barsalou and Weimer-Hastings ( 2005 ) suggest that abstract representations rely on internal states to derive meaning across situations—e.g., when seeing or hearing the word justice a feeling of strength and relief is experienced in the body. This suggests that abstract verbs are associated with actions and feelings, involving an exchange between one or more agents, and informed by the context—interaction of mental, physical, and environmental cues—in which they occur.

Due to the evidence considered thus far, we would predict that, at the systems level, abstract concepts are associated with distributed neural representations grounded in contextual—social, linguistic, affective, spatial, and sensorimotor—regions of cortex, at least in terms of processing the semantic meaning of the associated terms. Additionally, evidence suggests that the left frontal polar region is a key structure for integrating the structural properties between disparate analogous relationships—i.e., abstract concepts (Bunge, Wendelken, Badre, & Wagner, 2005 ; Green, Fugelsang, Kraemer, Shamosh, & Dunbar, 2006 ; Green, Kraemer, Fugelsang, Gray, & Dunbar, 2010 ). Therefore, we would expect this region to be active when one assesses a given scenario to determine whether or not it fits the criteria of an abstract concept—e.g., is the Boston Tea Party in fact an example of justice ? Consistent with these ideas, after controlling for resting state and linguistic activity, Wilson-Mendenhall, Simmons, Martin, and Barsalou ( 2013 ) found increased activation in neural regions associated with social cognition and mentalizing (medial prefrontal cortex, posterior cingulate, orbital frontal cortex, and superior temporal sulcus) while participants computed the meaning of convince , and increased activation in regions associated with mathematical processing (intraparietal sulcus, superior parietal cortex) while participants computed the meaning of arithmetic. Importantly, after averaging the brain activity for the two concrete and abstract concepts, these context-specific distributed representations vanished, suggesting that averaging across concepts may misconstrue activation patterns unique to the concepts they represent. These data imply a great deal of variance in the neural foundations of abstract concepts—consistent with the idea that the conceptual representations that form the neural basis of relational abstract ideas are context dependent and supported by dynamic patterns of activity over distributed networks.

Predictive coding and Hebbian learning

Evidence conferring a computational advantage for contextually rich concrete concepts (Xiao et al., 2012 ) adheres to the predictive coding (PC; e.g., Barsalou, 2013 ; Friston, 2005 ; Summerfield et al., 2006 ) account of conceptual retrieval. In essence, predictive coding describes a hierarchical process by which sensory signals sent from bottom-up perceptual systems in the brain converge with top-down signals, or models. Such models are derived from data collected over repeated exposure to a given concept or situation—insofar as models are unable to account for all situational variance, error signals are necessary for updating inaccurate predictions at each stage in the hierarchy. Thus, predictive coding is predicated on the notion that cortico-cortical circuits: 1) are arranged hierarchically and 2) are comprised of feedforward (ascending) and feedback (descending) connections between subcortical structures and cortex, 3) include both driving and modulatory connections, and 4) interact such that cortical neurons are able to model corporeal states and subsequently modulate sensorimotor neurons based on feedback loops producing error at each level in the hierarchy (Friston, 2003 ). Thus, correlating with incoming sensory data, cortical regions predict external conditions based on models derived from prior experience; this top-down model is compared with perceptual input and updated via modulatory interneurons at each layer in the hierarchy until the model closely matches the sensory data, thereby reducing error in the signal (Friston, 2012 ).

According to Friston, the predictive coding paradigm aims to minimize entropy (e.g., uncertainty) in the system via statistically aggregated, Bayesian inference models. It is no surprise, then, that concrete knowledge is modeled with greater efficiency than abstract knowledge given its relative stability across contexts. In other words, although concrete object concepts such as door can take on many forms, the functionality and central features of a door (e.g., opens and closes, serves to divide two adjacent areas when closed and connect them when open) are consistent. On the other hand, abstract concepts such as justice are both context dependent (e.g., one may be considered just or unjust when stealing, depending on the circumstances) and relational (multiple agents are required for an act of justice to take place). Thus, the features correlated with a circumstance in which one might encounter an example of justice are not as stable across contexts as those correlated with door —I know that when I walk into a new building that I am extremely likely to find a door and I will certainly know how to use it, but whether or not I experience justice and how the concept will play out in a given context is radically different from one situation to the next, depending on both subjective judgment (e.g., how unjust is it for a sick individual to avoid paying a medical bill she cannot afford to pay?) and cultural norms (see Borghi & Cimatti, 2009 , Boroditsky, 2011 ). Thus, abstract concepts, compared to concrete-object concepts, cannot be easily captured by a predictive model due to the amount of error inherent in such a model. As a result, a larger computational burden is likely placed on hierarchical cortico-cortical networks while processing abstract concepts, as top-down and bottom-up circuits work to interpret contextual variability.

Abstract concepts rely on data spanning a number of unique circumstances. The word compassion , for instance, is not directly correlated with a movement or sensory representation. Nonetheless, the PC model is applicable to this and other concepts, and several theories (e.g., Barsalou & Weimer-Hastings, 2005 ; Martin, 2016 ) address this discrepancy. Barsalou and Weimer-Hastings, for instance, contend that abstract concepts are evoked in the presence of an applicable situation; thus, when an individual witnesses a college student helping an elderly man with his groceries, she assigns the label compassion to the relationship between agents in a situational context. According to the PC model, a high-level representation of an abstract idea is generated when witnessing an applicable example—an episodic event comprised of social and contextual features which may be referenced when encountering future instantiations of the concept. As incoming sensory data conflict with the model’s expected outcome, the model is updated to accommodate new information. For example, if after helping the elderly man one sees the college student being paid for his assistance, the current model must be updated, as compassion does not include selfish motives. Hence, we expect to see an alteration in the neural representation of the concept following this update, as the model is revised. Therefore, while abstract concepts are capable of being modeled within a predictive coding hierarchy, we propose that such models are highly volatile and are thus inconsistent predictors of the semantic features of a given concept.

Similar accounts pervade the literature. For example, theories attributing conceptual knowledge to distributed cell assemblies (e.g., Martin, 2016 ; Pulvermüller, 2013 ) propose that disparate sets of neurons representing distinct modality-specific properties (color, form, sound, texture, etc.) aggregate in convergence zones (Damasio, 1989 ) located near the center, or hub, of neural circuits where divergent regions converge to bind the features of a given conceptual representation. Convergence zones are likely candidates for the high-level conceptual models within the PC framework—low-level feature circuits intersect in CZs where they inform and/or update the current representation based on error signals. Importantly, several studies (e.g., Hsu et al., 2012 ; Simmons et al., 2007 ) report real-time feedback between low-level perceptual areas in occipital cortex and higher-level conceptual areas in fusiform gyrus for color perception, providing direct evidence of PC mechanisms.

While PC provides a plausible mechanism for modeling concepts both online and offline, it is also necessary to discuss how divergent streams of information come to be assembled within CZs, producing Bayesian hierarchical models. Hebbian learning (e.g., long-term potentiation; LTP) offers a proven and parsimonious explanation of this phenomenon. According to LTP, when two or more seemingly distinct events occur simultaneously across a number of episodes, the synaptic connections encoding the representation of each event are strengthened to the degree that the neural firing associated with event A is enough to cause firing across the synapse associated with event B and vice versa (Hebb, 1949 ). Hebbian associations, therefore, are the building blocks of predictive models, as the features of events which typically co-occur are hardwired together across neural circuits. For instance, several papers (Glenberg & Gallese, 2012 ; Lee, Turkeltaub, Granger, & Rizada, 2012 ) hypothesize that language production shares a Hebbian association with language comprehension. This relationship may in fact account for language development—as the babbling infant learns to associate specific mouth movements with their correlated sounds, neurons in Broca’s Area form an association with those in auditory cortex. Thus, concurring with Lee and colleagues, novel mouth movements are associated with distinctive speech sounds and, therefore, hearing a word may activate motor areas recruited when speaking the word, leading to an understanding of the word via subsequent activation of associated modality-specific semantic networks. Ibáñez et al. ( 2013 ) provide direct evidence for this notion, evaluating the Action-Sentence Compatibility Effect (ACE; see Glenberg & Kaschak, 2002 ) in epilepsy patients. The ACE task requires patients to respond to a cue by moving in a direction either compatible or incompatible with directional information implied in a sentence (e.g., “John was moving on after the breakup” = forward movement). Previous studies have demonstrated robust interference, as evidenced by slower RTs, when the motion used to respond mismatches that implied in a sentence. Ibáñez et al. ( 2013 ) measured the ACE using electro-corticography in two epilepsy patients awaiting surgery by placing subdural electrodes on the surface of the left fronto-temporal and frontal cortex. When measuring both language and motor responses while the patients processed the final verb in a given sentence, a bi-directional effect was observed, evidenced by a negative evoked potential at 400 ms—a correlate of semantic processing—in premotor, motor, and language areas during incompatible trials. Presumably, the increased N400 response, typically associated with an unexpected stimulus (e.g., Fabbri-Destro et al., 2015 ), indicates incompatibility between the meaning derived from a sentence and the direction of the required response. This outcome suggests that both linguistic and motor content provides meaningful information to readers, as the motor system enhances language understanding while language understanding modulates the motor system.

Consistent with these principles, Barsalou’s ( 2013 ) Pattern Completion Inferences within Situated Conceptualizations (PCIwSC) theory suggests that associational learning mechanisms may augment or even replace extant theories of conceptual modeling. PCIwSC proposes that multiple neural networks representing disparate features are integrated to capture the totality of a concept by processing parallel streams of contextual, self-referential, social, and sensorimotor data simultaneously. For instance, according to this model, if I find myself in a restaurant where I am meeting a coworker typically dressed in a suit and he arrives in jogging pants and a t-shirt, it may take me a few extra seconds to recognize him. This delay may be due to the necessity of integrating novel input into and subsequently updating an erroneous model, given the fact that I have come to associate a set of visual features (how my coworker is dressed) with that person. Within my predictive model, for example, the features of the face match my prediction; however, because I am viewing my coworker from across the room, the face prediction is not entirely accurate and his clothing provides a mismatch with my current predictive model.

Thus, Hebbian learning connects the sensory, motor, and affective information constitutive of high-level models. Further, the PC theory provides a parsimonious explanation for concept formation, one which eschews the need for a modality-independent semantic system— a controversial idea that opposes what we know about the timescale for evolutionary development (for more on this, see Barsalou, 2008 ; Dehaene & Cohen, 2007 ). It is, therefore, not surprising that familiar objects, locations, and smells are able to evoke elaborate conceptual representations (the carnival or grandma’s house, for instance), given that a single feature is capable of evoking additional features constitutive of the entirety of the concept. Consistent with this notion, widespread bilateral neural activation patterns for both abstract and concrete concepts are associated with faster RTs in a word/non-word task, suggesting that highly distributed representations confer a retrieval advantage (Binder et al., 2005 ). With this advantage in mind, we propose a grounded theory of STEM learning based on predictive coding and Hebbian learning paradigms.

Neural representations of STEM concepts

Given the relatively short history of human scientific inquiry, beginning with astronomy less than 10,000 years ago (Ruggles, 1999 ), and because we know that it takes hundreds of thousands of years for distinct neural systems to develop (Dehaene & Cohen, 2007 ), it is highly unlikely that the human brain developed a distinct neural system dedicated to processing the type of information central to scientific conceptual understanding. Instead, it is likely that older neural systems have accommodated and influenced the trajectory of scientific thinking. For instance, Dehaene and Cohen ( 2007 ) point out the architectonic similarity between left hemispheric regions associated with perceiving natural objects, the fusiform face area (FFA) for example, and the visual word form area (VWFA; thought to specialize in the recognition of written language patterns)—another recently developed, culturally derived skill. These regions neighbor one another and follow a distinctive hierarchical trajectory, starting with cells specializing in perceiving primitive shapes in the occipital cortex, and ending in more anterior regions specialized for perceiving complex forms (e.g., words or faces). Thus, Dehaene and Cohen hypothesize that the human brain has co-opted the existing function of regions that evolved to perform tasks associated with our evolutionary lineage—e.g., recognizing natural objects in the environment. Accordingly, culturally dependent functions, such as word recognition and comprehension of symbolic number magnitude, bootstrap the hardware necessary for more primitive computations, such as object recognition and estimation of physical magnitudes (e.g., size, distance, quantity; but see Lyons, Ansari, & Beilock, 2015 ).

Supporting this assertion, Mason and Just ( 2016 ) used fMRI to map the neural representation of physics concepts in undergraduate and graduate students, and their results were consistent with those of the neuronal recycling theory of Dehaene and Cohen ( 2007 ), in addition to theories rooted in grounded cognition and predictive coding. The authors divided physics concepts into four discrete categorical factors—causal motion, periodicity, algebraic equation representation, and energy flow (also controlling for word length as a fifth factor)—discovering that each factor was not only discernable based on activation patterns, but also associated with activation in regions of the cortex underlying primitive processes, such as spatial and sensorimotor cognition. For instance, principles of causal motion (e.g., gravity and torque) relied upon the left IPS and left MTG, regions associated with perceiving and visualizing motion; when considering periodicity (e.g., wavelength frequency) activation was seen in regions associated with biorhythms (e.g., dancing, rhythm and meter in music, etc.) and terrestrial cycles (e.g., tidal patterns), including the dorsal PMC, bilateral parietal, and somatosensory cortex. These data suggest that, concurring with theories of grounded cognition, abstract scientific concepts are comprehended based on embodied visuospatial representations mapped onto corresponding cortical structures. Furthermore, these maps, grounded in sensorimotor codes, are distributed and comprised of features originating in disparate regions of the cortex, which suggests that a higher order organizational system is needed to assemble bottom-up features into a coherent conceptual representation—in other words, these data support the predictive coding theory of cortical organization.

STEM learning interventions based in grounded cognition

Concepts in STEM learning range from those that can be readily experienced—e.g., if I jump, gravity forces me back to the ground—to ideas derived completely from mathematical equations, such as the enigmatic force known as dark matter. Given the accounts of abstract knowledge representations described above, we predict that concepts taught in STEM classrooms are better understood when they are initially grounded in hands-on learning activities. Given the nature of abstract concepts—variability across contexts (Granito et al., 2015 ) and the reliance on situational information (Barsalou & Weimer-Hastings, 2005 )—grounding scientific and mathematical concepts in sensorimotor representations provides students with a useful tool for placing abstractions in a readily accessible, concrete conceptual framework. For instance, college students learning about angular momentum, the physical force keeping moving objects on a steady trajectory, can physically experience the concept by manipulating an apparatus on which this force was exerted (e.g., a bicycle wheel spinning on an axle held by the student). In a study that examined the advantages of using such a hands-on demonstration, students who actively engaged in the task demonstrated a greater understanding of the concept relative to students who had learned the same concept by merely observing the demonstration (Kontra et al., 2015 ). Additionally, fMRI data from the same participants demonstrated robust activation differences between the hands-on learning group and the observation group. Regions in the premotor, motor, sensory, and parietal cortex were more active when the active group answered questions about angular momentum. Moreover, the authors performed a mediation analysis to demonstrate that the activation in the primary motor cortex accounted for the between-group performance difference on the test of concept understanding (Kontra et al., 2015 ). According to embodied theories rooted in Hebbian learning and predictive coding, these results are a consequence of multimodal (kinesthetic, visual, proprioceptive, affective, etc.) associations integrated into a high-level conceptual representation at the time of learning. In other words, the students in the hands-on group are able to retrieve a rich, sensorimotor representation, which in turn facilitates their understanding of the abstract concept of angular momentum, as evidenced by the neural and behavioral data.

A number of studies (e.g., Brooks, Ouh-Young, Battert, & Kilpatrich, 1990 ; Han & Black, 2011 ) have observed similar results. Han and Black used virtual learning environments, enabling elementary students to develop visual, auditory, and haptic representations of mechanical principles. The authors concluded that the addition of the haptic dimension improved students’ performance. While these data concur with the idea that contextual features are absent in abstract representations, they suggest an important role for the hands-on experience. Perhaps, as proposed by radical embodied theories (e.g., Wilson & Golonka, 2013 ), motorically acting upon the world confers knowledge otherwise unavailable. Another plausible hypothesis is an extension of Dehaene and Cohen’s ( 2007 ) theory of neuronal recycling. Processes such as arithmetic and writing co-opt information processing mechanisms in regions of the cortex evolutionarily optimized for motor and spatial functions, such as bilateral IPS and left OTC, taking advantage of abilities, such as numerical quantity differentiation (e.g., three bananas is more than one banana) seen in monkeys, rats, and other altricial species. Thus, the concept of numerical magnitude is understood using the mechanisms of the visual/haptic system that evolved to process magnitude in a physical sense (e.g., estimating distance to an object one intends to grasp). In a similar vein, haptic experiences may enable human beings to represent concepts in motor cortex as described above.

In addition to hands-on activities, traditional learning materials (e.g., schematic pictures and symbol representations) are effective insofar as they are easily generalizable, and thus must be included in the STEM curriculum (Fyfe, McNeil, Son, & Goldstone, 2014 ). A curriculum which begins by teaching concepts via hands-on, concrete activities before moving into more abstract materials may be best suited to teach complex scientific and mathematical information. Fyfe and colleagues propose a curriculum that orients students with concepts by first using hands-on techniques and then gradually moving to abstract materials traditionally associated with mathematics and science—e.g., equations and illustrative models. In other words, when initially learning a concept, it is useful to constrain knowledge to sensorimotor referents before placing it in a context divorced from one’s first-hand experiences. In such a learning framework, confusing abstract ideas are first related to familiar, concrete objects, which aids in recall when ambiguous abstract symbols are insufficient—eventually, however, it is beneficial to strip such concepts down to their fundamental core. However, as evidenced by prior research (e.g., Barsalou & Weimer-Hastings, 2005 ; Binder et al., 2005 ), abstract concepts may eventually become left lateralized and generalizable, providing students with a neural scaffolding and enabling conventional teaching methods to be more easily understood. It is therefore important that students learn to apply such concepts independently of the context in which they were initially learned, allowing them to easily generalize across disciplines (e.g., geometry to physics). This sort of scaffolding may attenuate the variability in the effectiveness of laboratory-based activities, which can often be attributed to confusing, overwhelming, or boring laboratory procedures which have vague or ambiguous connections to the concepts they are intended to convey (Kirschner, Sweller, & Clark, 2006 ; Prince, 2004 ).

There have been conflicting reports concerning the benefits of haptic experience on learning outcomes, as some studies (e.g., Reiner, 1999 ) suggest that physical sensation itself improves learning, while others (e.g., Klahr, Triona, & Williams, 2007 ; Triona & Klahr, 2003 ; Olympiou & Zachariah, 2012 ) dispute this notion, demonstrating that activities performed in virtual learning environments result in similar benefits. In this vein, Klahr and colleagues (Klahr et al., 2007 ; Triona & Klahr, 2003 ) and Olympiou & Zachariah ( 2012 ) argue that the degree to which learners are actively engaged in the learning process—rather than physical activity per se—determines the outcome of learning. Accordingly, virtual learning, they argue, can benefit students as much as physical laboratory-based learning when it comes to conceptual understanding. In other words, virtual activities may improve students’ understanding of conceptual knowledge as much as physical activities—instead the relevant variable is whether the students can actively manipulate the materials (virtual or physical) in the process of learning. However, in these examples the virtual laboratories use components that strongly resemble familiar physical materials (e.g., glass beakers and digital thermometers used to study changes in temperature of various materials). In other words, these virtual laboratories may already be somewhat grounded in physical experience. It remains unknown, therefore, to what degree the learning gains in the virtual task rely on the neural representations of familiar physical representations, which would not be observed for laboratories that involve unfamiliar materials. Similarly, the targeted concepts in these studies (e.g., heat transfer and the mechanical properties of springs) did not specifically produce phenomena that were surprising or counter-intuitive. It remains unknown whether learning in such situations would be facilitated or impeded by experiencing only a virtual simulation versus a hands-on laboratory.

Suggestions for using neuroimaging to study STEM learning

One potential way of understanding how the human brain learns complex scientific concepts is to examine learning outcomes based on neural markers (e.g., Cross et al., 2009 ; Davachi, Maril & Wagner, 2001 ; Kontra et al., 2015 ; Mason & Just, 2016 ) in addition to behavioral measures. The studies described above that reveal the neural basis of abstract concept representations in physics (Kontra et al., 2015 ; Mason & Just, 2016 ) provide neural markers for conceptual learning that can be used in addition to more traditional paper-and-pencil tests of learning. What remains to be seen is whether combining the neural data with the traditional tests adds unique explanatory power in predicting which students will retain their conceptual understanding and be able to use it appropriately at a later time, say 6 months or a year later. For example, future studies can take an active learning paradigm such as the one used by Kontra et al. ( 2015 ) for teaching about the concept of angular momentum and follow up with both a unit test from a textbook, as well as a computerized task that taps conceptual understanding while students undergo fMRI to record brain activity. Then students can return to the laboratory at some later time (e.g., after 6 months) for another paper-and-pencil test on the same topic, or even for a behavioral test of knowledge retention and transfer; for example, a demonstration of conceptual knowledge in which the student makes predictions about the outcome of a laboratory experiment. The key question would be whether performance on the follow-up test would be best predicted by the original paper-and-pencil test, or by the fMRI data, or by both combined. Such a demonstration would not only confirm that we have successfully identified sensitive neural markers of conceptual learning, it would also open the door for the use of fMRI in conjunction with traditional methods when testing the efficacy of a new instructional approach or laboratory procedure.

Another challenge in teaching students to identify abstract concepts from observable scientific data is that, by definition, abstract concepts are not consistently associated with invariable observable physical features across different contexts. Therefore, we must continue investigating the influence of context on abstract conceptual representation. One way of doing this is by directly comparing neural activity across ambiguous descriptions of object concepts when different contexts are provided. For instance, evoking a context by giving instructions to point out objects fitting a given description—e.g., a functional unit positioned at the end of a support arm—might result in similar neural patterns when identifying functionally dissimilar objects—e.g., a hammer and a street light. Such comparisons made across categories, including functional, visual, and haptic similarity, could inform the current debate between amodal and sensorimotor theorists. Thus, if a hammer and a light pole are represented similarly in the cortex while individuals focus on the visual features of the objects, but differently while they focus on functional characteristics, it would be clear that contextual cues influence conceptual knowledge. Accordingly, we would expect to see distinct activation patterns when these principles are applied to abstract versus concrete concepts (Borghi & Cimatti, 2009 ; Granito et al., 2015 ). Asking a participant to determine the likely air temperature, for instance, would lead an observer to direct their gaze towards a scene as a whole searching for key indicators, which could be measured via eye tracking; conversely, evaluating the structural integrity of an engineering apparatus (e.g., a truss) might lead one to focus his or her gaze on specific features within the object itself (e.g., joint fixtures). Further, it is possible to infer which objects in a scene are important for understanding a given concept and at what point attention is directed towards a given object or the scene as a whole, for example, using multi-voxel pattern analysis (MVPA), in addition to eye tracking and correlated with measures of behavioral performance across subjects. This type of analysis can also examine the potential facilitating effect of top-down instructions (e.g., search cues) provided at various points in the instruction process. Research on multimedia instruction has identified several best practices for reducing cognitive load as well as pitfalls to avoid that lead to cognitive overload (Mayer & Moreno, 2003 ). The addition of neural markers of attention and of conceptual comprehension will aid in further determining when a student is attending the appropriate details of the lesson. This information can then be used to modify instruction accordingly, adjusting factors such as when and how to introduce new facts and visual details in a multimedia lesson. In this way, we will have an opportunity to design new methods of teaching STEM concepts, taking advantage of our understanding of the neural basis of conceptual understanding.

Finally, future work should also aim to understand the importance for conceptual learning of individual differences in specific cognitive abilities (e.g., verbal and visual working memory; spatial visualization ability), prior knowledge (e.g., earlier classes in the same content domain), and habits of thought (e.g., visual versus verbal cognitive style). Each of these factors may play an important role in determining which students are likely to adopt an effective learning approach in the context of a specific lesson, or who may need additional support to fully comprehend the new material. As noted above, cognitive overload is likely to occur when working memory capacity is surpassed in a given modality (Mayer & Moreno, 2003 ), such as when text and pictures appear onscreen simultaneously. One’s threshold for information overload that impairs task performance is dependent on the individual’s working memory capacity (Unsworth & Engle, 2007 ), and this capacity is also known to be somewhat separable across verbal versus visuospatial domains (Shah & Miyake, 1996 ). Thus, this type of cognitive ability difference can lead to variability in which students will most effectively learn from a specific lesson. Moreover, neural indices of working memory demand (e.g., Barber, Caffo, Pekar, & Mostofsky, 2013 ) can further refine our ability to detect cognitive overload as it is occurring during a specific task or instructional lesson, allowing for a careful analysis of the contribution of this individual difference factor to successful learning.

Similarly, individual differences in domains of cognitive ability are well established (Carroll & Maxwell, 1979 ; Cattell, 1963 ; Horn & Cattell, 1966 ) and correlate with performance in academic domains (Deary, Strand, Smith, & Fernandes, 2007 ; Shah & Miyake, 1996 ; Wai, Lubinski, & Benbow, 2005 , 2009 ). Of particular interest to the current discussion, spatial abilities predict performance in STEM domains (Wai et al., 2005 , 2009 ). Surprisingly, little research has investigated whether there are benefits to differentiating performance or study strategies based on measures of visual, verbal, and spatial abilities , which is an intriguing area of focus for future work. Instead of considering these domain-specific cognitive abilities, much attention has been given to ideas such as Gardner’s theory of Multiple Intelligences (Gardner, 1993 ), and the related idea of visual and verbal learning styles, in which self-described “visual learners” would prefer to learn from pictures rather than words, and “verbal learners” would prefer words over pictures. However, to date, no evidence exists to support these theories of preferences in terms of improving learning outcomes based on individuating instruction in this way (Pashler, McDaniel, Rohrer, & Bjork, 2008 ; Visser, Ashton, & Vernon, 2006 ). On the other hand, there does seem to be some support for self-report measures of cognitive style—consistencies in how an individual processes information across contexts (e.g., Kozhevnikov, Hegarty, & Mayer, 2002 ; Messick, 1984 )—which correlate with verbal, spatial, and object (visual but non-spatial) domains (for a review, see Kozhevnikov, 2007 ). These dimensions of cognitive style correlate with some measures of ability (Blazhenkova & Kozhevnikov, 2009 ; Kozhevnikov, Kosslyn, & Shephard, 2005 ) as well as choice of career; for example, engineering majors are likely to rate more highly on the spatial visual style dimension, whereas artists are more likely to rate highly on the object visual style dimension. However, it is unclear to what degree cognitive styles overlap with cognitive abilities or whether they represent consistent but flexible task approaches or strategies that can affect task performance, but are somewhat malleable or amenable to changes in instructions.

In this vein, work in our laboratory has demonstrated distinct neural signatures for habits of thought corresponding to representing information in a verbal versus a visual modality (Hsu, Kraemer, Oliver, Schlichting, & Thompson-Schill, 2011 ; Kraemer, Hamilton, Messing, DeSantis, & Thompson-Schill, 2014a , Kraemer, Rosenberg, & Thompson-Schill, 2009 ). These propensities for task strategies (i.e., verbal and visual cognitive styles) have been shown to correspond to which types of information participants encode when only visual information is presented during a task (Kraemer et al., 2016 ). These individual differences in cognitive style have also been shown to correlate with what type of information is successfully encoded and recalled (Kraemer et al., 2016 ), performance on a visual feature retrieval task (Hsu et al., 2011 ), and decisions made by participants in ambiguous situations (Amit & Greene, 2012 ). Importantly, two of these studies have revealed that these task approaches are somewhat flexible given changes in task instructions (Kraemer et al., 2016 ) and task context (Hsu et al., 2011 ), indicating that these individual differences represent malleable factors that can potentially be leveraged to improve cognitive processing in a given context. At this point, more research is needed to determine whether and how these differences impact STEM learning specifically.

Conclusions

Embodied theories of cognition have reshaped the landscape of cognitive science, providing a rich literature that has not only changed the way we look at the human mind, but also inspired innovative learning interventions. As American students continue to struggle in the STEM fields, it is imperative that scientists search for novel ways to improve the scientific pedagogy. Here, we propose that embodied exercises improve STEM learning by situating abstract concepts in a concrete context, thus correlating intangible ideas with corporeal information. In doing so, rich multimodal distributed neural representations are forged, giving students a better chance at succeeding in the sciences.

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Hayes, J.C., Kraemer, D.J.M. Grounded understanding of abstract concepts: The case of STEM learning. Cogn. Research 2 , 7 (2017). https://doi.org/10.1186/s41235-016-0046-z

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• Published: 20 October 2022

Abstract and concrete concepts in conversation

• Caterina Villani 1 ,
• Matteo Orsoni 2 ,
• Luisa Lugli 1 ,
• Mariagrazia Benassi 2 &
• Anna M. Borghi 3 , 4  

Scientific Reports volume  12 , Article number:  17572 ( 2022 ) Cite this article

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Concepts allow us to make sense of the world. Most evidence on their acquisition and representation comes from studies of single decontextualized words and focuses on the opposition between concrete and abstract concepts (e.g., “bottle” vs. “truth”). A significant step forward in research on concepts consists in investigating them in online interaction during their use. Our study examines linguistic exchanges analyzing the differences between sub-kinds of concepts. Participants were submitted to an online task in which they had to simulate a conversational exchange by responding to sentences involving sub-kinds of concrete (tools, animals, food) and abstract concepts (PS, philosophical-spiritual; EMSS, emotional-social, PSTQ, physical-spatio-temporal-quantitative). We found differences in content: foods evoked interoception; tools and animals elicited materials, spatial, auditive features, confirming their sensorimotor grounding. PS and EMSS yielded inner experiences (e.g., emotions, cognitive states, introspections) and opposed PSTQ, tied to visual properties and concrete agency. More crucially, the various concepts elicited different interactional dynamics: more abstract concepts generated higher uncertainty and more interactive exchanges than concrete ones. Investigating concepts in situated interactions opens new possibilities for studying conceptual knowledge and its pragmatic and social aspects.

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Introduction

Concepts allow categorizing objects and entities, making inferences based on previous experiences, and preparing to act 1 . Many have distinguished concepts into concrete and abstract concepts (from now, CCs and ACs) (e.g., “table” vs. “justice”, but also “stop” and “maybe” 2 ). Recent views see CCs and ACs as neither dichotomously opposed nor as representing a continuum. Instead, different concepts would be points within a multidimensional space, defined by various dimensions 3 . Studies have identified some of these dimensions, none of which is exhaustive or necessary. Compared with more CCs, ACs generally include more heterogeneous members (density 4 ); they are more detached from the five senses, less imageable 5 , and evoke more frequently inner experiences (interoception, emotions) 6 , 7 . Furthermore, they refer to relations rather than single objects 8 , are less iconic 2 , and more variable across contexts 9 , 10 . The words expressing them are typically acquired later and through language rather than through perception 11 . Language and social interaction are crucial for their acquisition and representation 12 , 13 , 14 . Because of their complexity, ACs might lead to higher uncertainty, less confidence in their meaning, and stronger involvement of metacognition and inner speech 15 .

Importantly, some have recently acknowledged that ACs come in different kinds 16 , for which different dimensions are relevant 17 . Thus, emotional and aesthetic ACs evoke more interoception and emotions 18 , numerical ACs elicit more sensorimotor experiences linked to finger counting 19 . All ACs, just as CCs, activate sensorimotor brain areas. A meta-analysis demonstrated that numerical, emotional, morality, and theory of mind ACs also engage specific brain areas 20 . To date, converging evidence supports the existence of different types of ACs. They span in a multidimensional space that includes interoceptive, sensorimotor, affective, social, and linguistic features to a different extent (review 21 ).

Understanding how ACs are represented constitutes a challenge for both embodied theories, according to which the body influences and constrains cognition, and distributional theories, according to which we get meaning through word associations. Hybrid, multiple representation views represent a promising alternative 22 , 23 , 24 . They relate the differences between CCs and ACs and their kinds to the different weights sensorimotor, interoceptive, linguistic, and social experiences play.

Crucially, the emergent ways to conceive concepts impose the adoption of new methods 25 , 26 , 27 . Most studies so far have focused on single words or simple sentences. Typical methods are ratings of different dimensions (imageability, contextual availability, Age of Acquisition, Modality of Acquisition, Emotionality) 28 , 29 , and feature listing and definitions 30 , 31 . Among the implicit tasks, the most common are lexical decision, recall, recognition, and property verification tasks (e.g., 32 , 33 , 34 ). Brain imaging studies typically use words or simple sentences differing in imageability or abstractness 23 , 35 , 36 . Conversely, studies on natural conversations 37 rarely focus on ACs and CCs. In one of the few studies on interaction, participants had to explain the meaning of CCs and ACs, avoiding using the word themselves (taboo game) ( 38 ; see also 39 ).

An effective approach for investigating conceptual representation is to infer it from the use of concepts in interaction. According to some recent theories, social interaction is crucial for abstract concepts. Because their referents are not objects and the members of abstract categories are very heterogeneous, other people are particularly crucial to help us acquire them. In addition, we have proposed elsewhere that other people might be particularly important also during abstract concepts processing. Through interaction, other people can facilitate our processing of abstract words, either helping us understand the meaning of the words or negotiating the word's meaning with us 40 , 41 . Because of the crucial role social interaction might play for abstract concepts, it becomes pivotal to investigate them through interactive tasks. The present work aims at exploring how abstract and concrete concepts are used in conversation. In our study, participants engaged in exchanges starting from different kinds of concepts. We presented three kinds of CCs, the most common in the literature (tools, animals, and food), and three kinds of ACs, derived from a previous rating study, i.e., philosophical-spiritual, PS (e.g., “value”), physical-spatio-temporal-quantitative, PSTQ (e.g., “mass”), emotional-mental states and social concepts, EMSS (e.g., “anger”) 17 , 28 . The previous norming study 28 showed that PS concepts resulted as more abstract, EMSS characterized mostly by inner experiences, and PSTQ more based on sensorimotor experience than other ACs. In this study, we asked participants to simulate a conversational exchange with another person, responding to a written sentence focused on a concrete/abstract concept (e.g., I made a cake/a judgment). We then used the Interpersonal Reactivity Index (IRI 40 ) questionnaire to explore whether individual differences in empathy influenced the responses, especially for those prompted by ACs that may rely heavily on emotions and affective states 7 , 32 . Since we hypothesize that, with abstract concepts, people rely more on others 41 we intended to explore whether there is a relationship between conceptual abstractness and the level of empathy demonstrated by participants.

We hypothesized that responses to sentences involving ACs would differ from those involving CCs and intended to explore the dimensions distinguishing concept kinds. Notably, some of these dimensions allow inferring how concepts are represented from the conversational pattern they evoke. We outline below the hypotheses we pre-registered and tested; for each of the six hypotheses, we also explored eventual differences between kinds of ACs and CCs.

Conversation: Uncertainty expressions, Number of questions, and Target word repetitions. We expected (directional hypothesis) more uncertainty expressions (e.g., “mmm…”, “I am not sure” , “What do you mean?”) and signs of uncertainty, like questions and repetitions of the target word, with ACs than CCs (moderate to strong evidence).

Conversation: Turn-taking (directional hypothesis). With ACs participants should be more uncertain on the word meaning and need to rely on others more 15 , 41 ; hence, with ACs, participants should more often continue the discussion assigning a further turn to the other either by asking questions or by using expression signaling the willingness of knowing more details (e.g., “Tell me more”, “Explain it to me”), thus eliciting a social interaction dynamic (moderate to strong evidence).

Conversation: Point of views and General Statements. We investigated whether the participants made a general statement (e.g., “Dream is important”, “Revenge is a human feeling”) or whether s/he considers the other’s perspective (e.g., “You were right”, “Don’t give up”). We referred to this with Points of View. Specifically, we distinguished between 1st, 2nd, and 3rd person Point of View and the coupling of 1st and 2nd person (i.e., interpersonal Point of view). We expected more general statements with ACs than CCs.

Number of evoked contexts. Because ACs meaning is more context-dependent 9 , we expected that participants would refer to more contexts with ACs than CCs (moderate to strong evidence).

Produced features. We expected (directional hypothesis) that (a) ACs are more focused on internal situational elements 12 , 25 , i.e., leading to the production of beliefs (e.g., “I think that…” “I should do that”, “It was necessary”), evaluative (not perceptual, e.g., “it is useful!”, “great”, “It was correct”), emotional, (e.g., “I am happy/glad/nervous”, “I hope it works”), introspective (e.g., “I remember when…”, “I often feel ashamed when I speak in public”), and metacognitive properties (e.g., “I am good at memorizing”, “I am not able to cook”); (b) CCs evoke more external sensorimotor and contextual properties, i.e., evaluative (perceptual) properties experienced through five senses and thematic spatial and temporal relations, particularly those related to action/agency (moderate to strong evidence). We also expected emotional concepts to activate more emotional and interoceptive features than other concepts.

Conversation: Kind of questions (how, why, where, what, when, who). We predicted more “why” questions with ACs, the meaning of which generates more uncertainty and more questions related to external situational elements (“what”, “where”, and “when” questions) with CCs (moderate to strong evidence).

Bayesian Generalized linear mixed models were applied to estimate the probability of different models' hypotheses, separately for each variable in ACs and CCs, and their subcategories (see “Analysis” below). The Bayes Factor (BF) for the Kind of concepts (i.e., emotional EMSS, philosophical PS, quantitative PSTQ, Animals, Food, Tool) and the Type of sentences (i.e., abstract, concrete) and Null Hypothesis models have been calculated for the model selection to each hypothesis (see Table 1 ). The subsequent analyses have been carried out according to the BF values. Table 2 shows a summary of the results. We report below the contrast analysis. This method allows us to compare the level of the factor on hypothesized differences between the ACs and CCs kinds. For each kind of concept (i.e., emotional EMSS, philosophical PS, quantitative PSTQ, Animals, Food, Tool) the frequency of coding variables is displayed in polar plots grouped according to our hypotheses on: conversation, turn-taking (Fig.  1 ), sensorimotor grounding (Fig.  2 ), inner grounding (Fig.  3 ). Polar plots showing the frequency of other variables are reported in Supplementary Materials .

Polar plots for sentences including animals ( A ), tools ( B ), food ( C ), PS, philosophical-spiritual ( D ), EMSS, emotional-social ( E ), and PSTQ, physical-spatio-temporal-quantitative ( F ) concepts, showing the row frequency count of the variable number of questions (dodger blue), the percentage of the other coded variables concerning conversation i.e., uncertainty expressions (dark orange), target word repetition (pink), why (magenta), how (dark turquoise), where (medium orchid), who (deep sky blue), when (lawn green), and what (light salmon) questions. A polar plot showing the percentage of the turn-taking variable ( G ) for sentences including animals (blue), tools (light green), food (orange), PS, philosophical-spiritual (aqua), EMSS, emotional-social (gold), PSTQ, physical-spatio-temporal-quantitative (violet) concepts.

Polar plots for sentences including animals ( A ), tools ( B ), food ( C ), PS, philosophical-spiritual ( D ), EMSS, emotional-social ( E ), and PSTQ, physical-spatio-temporal-quantitative ( F ) concepts, showing the percentage of sensorimotor grounding dimensions, i.e., hearing (coral), touch (aquamarine), vision (light green), material (gold), taste (medium purple), smell (light pink).

Polar plots for sentences including animals ( A ), tools ( B ), food ( C ), PS, philosophical-spiritual ( D ), EMSS, emotional-social ( E ), and PSTQ, physical-spatio-temporal-quantitative ( F ) concepts, showing the percentage of inner grounding dimensions, i.e., emotion (lime), interoception (fuchsia), belief (cyan), metacognition (blue), introspection (yellow).

Hypothesis 1. Conversation: uncertainty, number of questions, and target word repetitions

ACs elicited more Uncertainty Expressions than CCs. In particular, we found moderate evidence that PS sentences elicited more Uncertainty expressions than other ACs (1.4%; 95% CI [0.6, 2.5]. However, by inspecting the mean number of posterior observations major to zero (PP, Posterior Probability) we are 100% confident that the Uncertainty Expressions in PS sentences are more frequent compared to all other sentences. We found inconclusive evidence in support of the hypothesis that ACs evoke a higher Number of Questions than CCs. However, contrast analysis showed strong evidence that PS sentences elicited more questions than EMSS and PSTQ sentences (0.11; 95% CI [0.05, 0.17]; PP = 99.9%). We found inconclusive evidence for the hypothesis that Target Word Repetitions were more frequent with ACs than CCs. However, contrast analysis showed strong evidence that Target Word Repetitions were more frequent with the most abstract PS sentences compared to the other ACs (i.e., EMSS, PSTQ) (2.5%; 95% CI [1.1, 4.3]; PP = 99.9%).

Hypothesis 2. Conversation: turn-taking

We found moderate/strong evidence that ACs generate more Turn-Taking (i.e., further interactions, turn of words) than CCs. Within ACs, we found strong evidence that turn-taking was more frequent with the most abstract PS sentences than EMSS and PSTQ sentences (12.7%; 95% CI [8.4, 17.1]; PP = 100%).

Hypothesis 3. Conversation: Point of views and general statements

We found strong evidence for the hypothesis that ACs elicit more General Statements than CCs. In addition, contrast analysis showed strong evidence that General Statements were more frequent with PS sentences compared to PSTQ and EMSS sentences (2.9%; 95% CI [0.7, 5.5]; PP = 99.5%). Concerning the Points of view of participants, we found strong evidence that 1st or 2nd person Points of View are more frequent with EMSS and PS sentences than PSTQ sentences (4.9%; 95% CI [0.9, 8.9]; PP = 99.1%), and moderate evidence that 1st and 2nd person Points of Views were more frequent with EMSS and PS sentences than PSTQ sentences (0.6%; 95% CI [− 1.6, 2.6]; PP = 69.3%). We found strong evidence that 1st Point of View was more frequent with food and animal sentences than tool sentences (6.5%; 95% CI [4.0, 9.1]; PP = 100%) and moderate evidence that the 3rd Point of View was more frequent with ACs than CCs. According to the Bayes Factor (BF, see Table 1 ), it was not possible to demonstrate the effect of sentences on overall Point of View and on 2nd Point of View.

Hypothesis 4. Number of evoked contexts

We found strong evidence that the Number of Evoked Contexts was higher with ACs than CCs. In addition, contrast analysis showed inconclusive evidence for the hypothesis that PS and PSTQ sentences evoked a higher number of contexts than EMSS sentences (− 0.48, 95% CI [− 2.29, 1.18]; PP = 29%); and actually, it was more plausible that the effect was in the opposite direction as compared to the predicted one.

Hypothesis 5. Produced features

Sensorimotor grounding.

We found strong evidence that CCs elicited more Sensory (i.e., vision, touch, hearing, taste, smell) and Material features than ACs (see Fig.  2 ).

Within CCs, contrast analysis showed inconclusive evidence that Visual features were more frequent with animal and food sentences than tool sentences (− 0.02; 95% CI [− 2.4, 1.8]; PP = 41.7%); moderate evidence that Tactile features were more frequent with tool sentences than other CCs (i.e., animal, food) (0.2%; 95% CI [− 0.2, 1]; PP = 85%), inconclusive evidence that Tactile features were more frequent with tool and food sentences compared to animals sentences (− 0.3%; 95% CI [− 1.2, 0.1]; PP = 6.2%); strong evidence that Auditory features were more frequent with animal and tool sentences than food sentences (0.5%; 95% CI [0.1, 1.2]; PP = 100%), and strong evidence for the hypothesis that, within CCs, Material features were more frequent with food and tools sentences compared to food sentences (4.2%; 95% CI [2.8, 5.7]; PP = 100%).

Within ACs, contrast analysis showed strong evidence for the hypothesis that the more concrete PSTQ sentences elicited more Visual features than other ACs (i.e., EMSS, PS) (0.6%; 95% CI [− 0.1, 1.4]; PP = 95.4%).

Thematic relations: space, time, events, and actions

Spatial features were more frequent with CCs than ACs. In addition, contrast analysis showed strong evidence for the hypothesis that Spatial features were more frequent with tool sentences than other CCs (4.9%; 95% CI [3.1, 7]; PP = 100%). Within ACs, we found inconclusive evidence that Spatial features were more frequent with the most concrete PSTQ sentences compared to the other ACs (i.e., EMSS, PS) (− 0.02%; 95% CI [− 0.5, 0.6]; PP = 47.3%).

We found inconclusive evidence for the hypothesis that ACs elicit more Temporal features than CCs. However, contrast analysis showed strong evidence for the hypothesis that, within ACs, the most concrete PSTQ sentences evoke more Temporal features than other ACs (1.3%; 95% CI [− 0.2, 2.9]; PP = 95.9%). Within CCs, we found inconclusive evidence for the hypothesis that animal sentences evoke more Temporal features compared to other CCs (− 4.3%; 95% CI [− 6.1, − 2.6]; PP = 0%), and actually, it was more plausible that the effect was in the opposite direction as compared to the predicted one. We found inconclusive evidence in support of the hypothesis that Events were more frequent with ACs than CCs.

Concrete actions were more frequent with CCs than ACs. Within CCs, contrast analysis showed strong evidence that Concrete Actions were more frequent with food and tools sentences than animal sentences (4.3%; 95% CI [0.3, 8.1]; PP = 98.3%). Within ACs, contrast analysis showed strong evidence for the hypothesis that PSTQ sentences elicit more Concrete Actions than other ACs (i.e., EMSS, PS) (5.3%; 95% CI [3.4, 7.6]; PP = 100%).

Abstract Actions were more frequent with ACs than CCs. In addition, contrast analysis showed inconclusive evidence in support of the hypothesis that Abstract Actions were more frequent with PSTQ sentences than PS and EMSS sentences (− 2.1%; 95% CI [− 4.21, 0]; PP = 2.4%), and actually, it was more plausible that the effect was in the opposite direction. Consistently, we found strong evidence that Abstract Actions were more frequent with PS and EMSS sentences than PSTQ sentences (2.1%; 95% CI [0, 4.21]; PP = 97.6%).

Inner grounding: interoception, emotions, metacognition, beliefs, and introspections

We found inconclusive evidence in support of the hypothesis that Interoceptive features were more frequent with ACs than CCs. However, contrast analysis showed moderate/strong evidence that, within ACs, Interoceptive features were more frequent with EMSS sentences than PS and PSTQ sentences (0.14%; 95% CI [0, 0.6]; PP = 88.3%). Within CCs, we found strong evidence that Interoceptive features were more frequent with food sentences compared to tool and animal sentences (1.6%; 95% CI [0.7, 2.7]; PP = 100%).

Overall, we found strong evidence that ACs evoke more Emotions, Metacognition features, Beliefs, and Introspection than CCs (see Fig.  3 ).

Within ACs, contrast analysis showed strong evidence for the hypothesis that Emotions were more frequent with EMSS sentences than other kinds of ACs (i.e., PS, PSTQ) (8%; 95% CI [5.6, 10.6]; PP = 100%) ; inconclusive evidence for the hypothesis that the most abstract PS sentences evoke more Metacognitive features than other ACs (i.e., EMSS, PSTQ) (− 0.05%; 95% CI [− 1.13, 0]; PP = 2.3%); strong evidence for the hypotheses that the most abstract PS sentences evoke more Beliefs than the EMSS and PSTQ sentences (2.6%; 95% CI [0.2, 5.6]; PP = 96.3%), and that Beliefs were more frequent with PS and EMSS sentences compared to the less abstract PSTQ sentences (3.4%; 95% CI [0.7, 6.2]; PP = 99.3%). Finally, we found strong evidence that PS sentences elicit more Introspective states than EMSS and PSTQ sentences (0.7%; 95% CI [− 0.1, 1.8]; PP = 95.2%), and that Introspection was more frequent with PS and EMSS sentences than the less abstract PSTQ sentences (0.6%; 95% CI [− 0.2, 1.5]; PP = 94.1%).

Other: associations, subordinates, and non-perceptual evaluations

We found strong evidence that Associations were more frequent with ACs than CCs. However, contrast analysis showed inconclusive evidence for the hypothesis that, within ACs, Associations were more frequent with PS sentences compared to PSTQ and EMSS sentences (− 0.4%; 95% CI [− 1.3, 0.4]; PP = 15.6%). We found inconclusive evidence in support of the hypothesis that Subordinates were more frequent with ACs than CCs (− 0.8%; 95% CI [− 1.6, 0.1]; PP = 1%), and strong evidence that Non-perceptual evaluations were more frequent with ACs than CCs.

Hypothesis 6. Conversation: kinds of questions

Why questions were slightly more frequent with ACs than CCs. In addition, contrast analysis showed inconclusive evidence for the hypothesis that, within ACs, PS and EMSS sentences evoke more Why Questions than PSTQ sentences (− 6.2; 95% CI [− 8.7, − 4.1]; PP = 0%), and actually it was more plausible that the effect was in the opposite direction as compared to the predicted one. Consistently, we found strong evidence that Why Questions were more frequent with PSTQ sentences compared to PS and EMSS sentences (6.2%; 95% CI [4.1, 8.7]; PP = 100%).

Who questions were more frequent with ACs than CCs. In addition, contrast analysis showed strong evidence for the hypothesis that, within ACs, EMSS sentences evoke more Who Questions than PS and PSTQ sentences (10%; 95% CI [7.5, 12.9]; PP = 100%).

What questions were more frequent with ACs than CCs. Instead, W here and When questions were more frequent with CCs than ACs. Within CCs, contrast analysis showed strong evidence that Where Questions were more frequent with animal and tools sentences than food sentences (9.4%; 95% CI [7.4, 11.5]; PP = 100%).

How questions were not more frequent with CCs than ACs as we assumed. However, contrast analysis showed strong evidence that, within CCs, How Questions were more frequent with food and tool sentences than animal sentences (5.2%; 95% CI [3.1, 7.3]; PP = 100%). Within ACs, contrast analysis showed inconclusive evidence for the hypothesis that How Questions were more frequent with EMSS sentences compared to PS and PSTQ sentences (− 1%; 95% CI [− 3.3, 1.3]; PP = 19%); and that PSTQ sentences evoke more How Questions than EMSS and PS sentences (− 2.2%; 95% CI [− 4.5, 0]; PP = 2.7%). Consistently, we found strong evidence that PS sentences evoke more How Questions compared to the other kinds of ACs (3.3%; 95% CI [0.9, 5.9]; PP = 99.6%).

Exploratory analyses on empathy

We ran correlation analyses to explore the relationship between the results of the conversational task and the dispositional empathy of participants, detected using the IRI scale 42 . Among the four subscales included in the IRI survey, we focused on the perspective-taking (PT) and empathic concern (EC) subscales that tap separate facets of empathy particularly relevant for our purposes. The PT subscale measures the reported tendency to adopt the psychological point of view of others in everyday life (e.g., "I sometimes try to understand my friends better by imagining how things look from their perspective"). In contrast, the EC subscale assesses the tendency to experience feelings of sympathy and compassion for unfortunate others (e.g., "I often have tender, concerned feelings for people less fortunate than me").

Specifically, we tested whether the main PT and EC scores of each participant correlated with the following variables: Turn-Taking, Agreement, Emotions, Points of View (1st, 2nd, 3rd, and 1st and 2nd person), Why and How questions. Finally, we tested whether the number of words produced by participants varied across different sentences. We investigated whether such correlations are different in CCs and ACs, and their sub-categories.

Exploratory analyses results

Because of the frequency nature of our dependent variables, we conducted our analysis using Spearman’s Rho correlation. Table 3 shows a summary of the results for each variable.

EC sub-scale, more emotionally connoted, correlated mostly with ACs and their kinds. In particular, the positive correlation between EC subscale and EMSS sentences for emotions, how-questions, and 1st and 2nd points of view suggests that more empathic individuals may be more sensitive to emotions and keener to adopt others’ perspectives asking questions about how they feel. Within CCs, the EC sub-scale correlated with tools in first-person pronouns; hence, such a personal involvement concerns tools, likely because of their link with action.

PT sub-scale mainly correlated with CCs and their subkinds. The positive correlation of the PT scale with the further turn-taking for concrete sentences concerns mainly animals. Unsurprisingly, the level of agreement correlated negatively with the PT subscale for food sentences, for which participants frequently express their own tastes. In addition, the negative correlation between PT subscale and why-questions for animals and foods suggest that people who adopt the psychological point of view of others are less prone to inquire about common actions involving everyday objects/entities. Consistently, we found a negative correlation between the PT subscale in 1st person and a positive correlation in 2nd person and 1st and 2nd person combined, especially with tools and foods, possibly due to their link with interactive and joint actions. However, these findings do not allow drawing strong conclusions because the Spearman’s correlation indicates only weak relationship.

Finally, we used the Wilcoxon Signed-Ranks test to explore the Number of Words produced by participants. We found no differences between the produced texts to ACs and CCs ( Z  = 1.666, p  = 0.067). However, according to Friedman's test the sub-categories of ACS and CCs differ significantly in the produced numbers of words (χ 2 F (5) = 20.03, p  < 0.001). Participants tend to produce longer responses to sentences including food items ( Mdn  = 3.84) and EMSS concepts ( Mdn  = 3.86) than animals ( Mdn  = 2.81) ( Z  = − 1.056, p  = 0.007; Z  = − 1.038, p  = 005, respectively). No other pairwise comparison reached the significance.

The results are in line with the predictions. CCs and ACs differ along various dimensions. CCs evoke more sensorimotor properties, ACs elicit more inner properties (emotions, beliefs, etc.). Crucially, CCs and ACs also differ in the conversational dynamics they elicit. Furthermore, exploratory analyses confirmed the differences between the various concept kinds. We will first summarize the main differences between CCs and ACs, and then between concept kinds.

Compared to CCs, ACs generate more uncertainty expressions, a higher number of cases of 1st and 2nd person point of views, and evoke a higher number of contexts. Furthermore, they yield more associations, more inner processes (emotions, beliefs, introspections), more general statements. In addition, they evoke more why and who questions. Finally, the correlation between the Empathic Concern subscale and ACs, especially EMMS, for emotions and how-questions suggests a higher personal engagement with ACs, confirmed by the correlations with 1st and 2nd point of view. Some of these dimensions have been identified in previous norming, ratings, and feature production studies: for example, Villani et al. 17 , 28 and Barsalou et al. 25 stressed the role of inner grounding; Kousta et al. 32 the importance of emotions; Barsalou and Wiemer-Hastings 30 and Barca et al. 43 , the role of free associations, and introspections; Schwanenflugel et al. 9 , the association with various contexts. However, some dimensions are completely new since they characterize ACs in a simulated direct interaction. As predicted, ACs generate more uncertainty, as testified by the correspondent expressions, and, possibly as a consequence of this, lead to more interactive exchanges, assessed through the higher presence of 1st and 2nd person point of views and the higher numbers of turns. They elicit more generalizations and questions linked to possible underlying mechanisms (why) and agents (who).

Compared to ACs, CCs yield more sensory properties, materials, spatial expressions, and concrete actions. These dimensions confirm that CCs are firmly grounded in the sensorimotor system; unlike previous studies, we find these properties using a simulated interaction rather than isolated words. Crucially, we also find that CCs elicit more questions about the spatial and temporal context (what, where, and when). Finally, the finding that the CCs and the Perspective Taking subscale correlated positively for 1st and 2nd point of views and correlated negatively for why-questions and 1st person perspective is an index that CCs are rooted on common ground knowledge that requires fewer specifications.

Kinds of ACs

While some dimensions, like metacognition, do not differ across the kinds of ACs, a major opposition exists between the more abstract PS and the more concrete PSTQ concepts. Consistently, compared to the two other kinds of ACs, PS concepts elicit more uncertainty in conversation (uncertainty expressions, questions, and repetitions) and a more interactive dialogue (more turns). Inner grounding is stronger (more beliefs and introspections), and the tendency to produce general statements is more marked. PS concepts evoke more points of view and abstract actions than PSTQ ones. In contrast, PSTQ concepts evoke more visual properties and concrete actions than other abstract kinds. Curiously, they also elicit more temporal features and why-questions, but this is likely a matter of the content they convey, being often scientific concepts. EMSS concepts are in the middle in terms of abstractness. Consistently, they evoke fewer points of view than PS, and more abstract actions than PSTQ concepts. Crucially, they are characterized for interoception (e.g., 6 , 17 ), and they yield more who-questions, testifying the interest for the person who experiences emotions.

Kinds of CCs

Our results allow us to frame the kinds of CCs in a novel way. The personal involvement, as testified by 1st person statement, is stronger with animals and food, while where-questions interest more animals and tools, likely because of the possible variety of their contexts. Notably, food evokes both perceptual (materials) and bodily experiences (interoception); animals and tools elicit auditive properties.

Conclusions

This study allows drawing three main conclusions.

The first is that the abstract/concrete distinction is an important one. CCs and ACs cannot be characterized as extremes of a continuum but as a collection of different points in a multidimensional space. Some of these dimensions have been intensively investigated. For example, classical theories emphasized the role of imageability for CCs and the higher number of contexts for ACs; recent views underlined the importance of emotions and inner grounding for ACs. But, crucially, other dimensions we identified are entirely novel, deriving from concepts’ use in a simulated conversational exchange. ACs induce higher conversational uncertainty, elicit why and who questions, and more interactive exchanges (use of the second person); conversely, CCs yield more what, where, and when questions.

The second is that ACs and CCs are not holistic categories but incorporate differently characterized kinds. Within ACs, the most concrete PSTQ oppose to the most abstract PS concepts. We identified both the content of each kind and the specificities linked to the use of the corresponding word. EMSS concepts differ from other kinds, both in terms of their content—the strong role of interoception—and their role in the conversation.

It should be noted that our study did not aim to compare the distinction of abstract-concrete concepts as a continuum vs. discrete categories. However, we believe that by showing that different dimensions, both semantic and pragmatic, weigh differently depending on the kind of concepts/sentences, we demonstrate that concepts are not arranged along a continuum between the two extremes defined only by the concreteness/abstractness dimension. Moreover, our results further corroborate the idea that concepts are multidimensional and multifaceted constructs. When considered as broad categories, concrete concepts are primarily grounded in sensorimotor experiences, and abstract concepts are mostly grounded in inner and social-linguistic experiences. However, at the more fine-grained level, the role of some dimensions overlaps between different types of concepts. For example, interoceptive contents characterized both food and emotional concepts, sensorimotor proprieties (visual and actions-related) are associated with tools and abstract physical-quantitative concepts.

The third is that investigating concepts in everyday use allows detecting their richness and flexibility. A possible limit in generalizing our results is that they concern a simulated conversational exchange. We should design new methods to investigate concepts in real-time interactions, benefiting from insights from pragmatics and using social interactive tasks. The absence of studies adopting interactive methods in the investigation of abstract concepts is particularly striking in light of the spread of interest in the role of social interaction in a variety of fields. For example, research on basic cognitive processes has recently highlighted that some specific effects on spatial representation and attentional processing emerged only when participants are sharing a task (e.g., 44 , 45 , 46 , for a review, see 47 ). The last 20 years have seen a proliferation of studies on sensorimotor communication and signalling (review 48 ) and on various forms of synchronization occurring during conversations (e.g., 49 ), intended as a form of joint action 50 , 51 . In neuroscience, the development of new, sophisticated techniques, like hyperscanning, has allowed the focus on interactive aspects during conversation (meta-analysis 52 ). Curiously, this interest in interactive aspects has not included the investigation of conceptual representation (for exceptions see 38 , 39 ). In a recent paper, Barsalou et al. 25 argued that most studies in this field used single, decontextualized words and stated that we need to study concepts in situated action. We agree and think we need to go even further and start investigating concepts in situated interactions.

The hypotheses, experimental procedures, and data analysis have been specified in a pre-registration available at https://osf.io/6mkc7 . Data has been collected after the pre-registration.

Stimuli consisted of 60 sentences composed of a verb and a concept noun. Concept nouns included 30 abstract concepts and 30 concrete concepts used in a previous study 17 . The set of stimuli consisted of 3 sub-categories of concrete concepts, i.e., ten tools (e.g., “hammer”, “umbrella”, “fork”), ten animals (e.g., “lion”, “dog”, “cow”), and ten food items (e.g., “banana”, “tomato”, “carrot”), and three sub-categories of abstract concepts, i.e., ten philosophical-spiritual (PS, e.g., “moral”, “destiny”, “salvation”), ten physical-spatio-temporal-quantitative (PSTQ, e.g., “area”, “number”, “acceleration”), and ten emotional-social concepts (EMMS, e.g., “shame”, “joy”, “conflict”). We controlled the frequency of use of target nouns. Specifically, the subgroups of abstract and concrete words were balanced for classical psycholinguistic variables, including the absolute frequency (concrete food, tools, animals: F (2,27) = 0.536; MSE  = 3758.196; p  = 0.591; ƞp2 = 0.038; abstract PS, EMSS, PSTQ: F (2,27) = 1.855; MSE  = 72,376.078; p  = 0.18; ƞp2 = 0.121) and relatively frequency (concrete food, tools, animals F (2,27) = 0.694; MSE  = 178.537; p  = 0.508; ƞp2 = 0.049; abstract PS, EMSS, PSTQ: F (2,27) = 1.817; MSE  = 4541.619; p  = 0.18; ƞp2 = 0.119) based on CoLFIS, a lexical database of written Italian 53 (further details of psycholinguistic variables of target nouns are available at https://osf.io/rx85h/ , and Villani et al . 28 database).

For each of the selected concepts, we created a sentence in the Italian language. Each sentence was constructed by pairing a verb in present perfect tense with the concept noun (e.g., Ho fatto una torta/I made a cake; Ho pensato al destino/I thought about destiny). All sentences were declarative statements in the first person, balanced for definitive and indefinite articles and length (from min. 20 to max. 26 letters). See Supplementary Materials for the full list of sentences.

Participants

The choice of our sample size was guided by reference to a previous study in literature in which similar measurements and statistical analyses are used (N = 62 38 ). We recruited 92 native Italian speakers through Qualtrics survey software among students of the Cognitive Psychology course and researchers of the University of Bologna, who were asked to disseminate the survey to colleagues or acquaintances. Participants with incomplete data were excluded (n = 12). The final sample consisted of 80 participants (59 female, Mage = 26.3, SDage = 5.9). The study was approved by the Ethical Committee of the University of Bologna and fulfilled the ethical standard procedure recommended by the Italian Association of Psychology (AIP) and conformed to the Declaration of Helsinki. All participants were naïve as to the purpose of the experiment and gave their informed consent to participate in the study.

The study was implemented as an online questionnaire in Qualtrics and consisted of three parts: (1) conversational task, (2) debriefing ratings, and (3) the Interpersonal Reactivity Index (IRI 40 ; Italian version, see 54 ).

In the conversational task, 60 written sentences were presented in random order. Participants were asked to respond through written language production, simulating a conversational exchange with another person. Specifically, participants saw a list of sentences; for each sentence they were asked to imagine a natural conversation with a familiar person who uttered the sentence and to write their own response as naturally as possible. Participants were invited to avoid focusing on a single person or situation during the task. No character limit was imposed. Figure  4 reports both the instructions provided to participants and the Qualtrics interface used in the experimental task.

Instructions provided to the participants ( A ) and the Qualtrics interface with examples of four sentences used in the study ( B ).

In the debriefing ratings, participants were asked to rate their general comprehension of the task and how much they felt involved in a real conversation using 7-point scales ranging from 1 = “not at all” to 7 = “extremely”. Finally, they indicated which sentences they had had more doubts about answering.

In the last part of the questionnaire, participants completed the IRI survey, a 28-item self-report measure of empathy. It consists of four subscales with seven items measured on a 5-point scale ranging from 0 = “does not describe me well” to 4 = “describes me very well”. Each subscale measured different dimensions of dispositional empathy: the Perspective Taking (PT) assesses cognitive empathy, or the tendency to adopt the psychological point of view of others spontaneously; the Empathic Concern (EC) assesses emotional empathy, or the other-oriented feelings of sympathy and concern for unfortunate others; the Fantasy (FS) taps the respondent's tendency to transpose oneself into feelings and actions of fictional situations imaginatively; and the Personal Distress (PD) measures the tendency to experience anxiety and unease in response to other’s suffering 40 .

Sentences coding

37 category codes (see Supplementary Materials ) were used to best capture the type of features the participants produced with each sentence. Coding categories were adapted from Barsalou and Wiemer-Hastings 30 and Zdrazilova et al . 38 . Two independent researchers, one of whom blind to the aims of the study, coded the produced text. Reliability among the coders was 96%. The cases of disagreement were solved through consensus after discussion together with a third judge.

Debriefing responses

On average, participants declared to have correctly understood the task (M = 3.5; SD = 1) and to have simulated a real conversation (M = 3.7; SD = 0.8). Most of the sample showed moderate (34%) and good comprehension of the sentences used in the task (31%), 17% extreme, and only 16% poor comprehension. Half of the sample (51%) reported being very involved in natural speech, 27% moderately involved, 12% extremely involved, and only 9% felt slightly involved. Finally, participants reported more uncertainty when responding to sentences related to abstract concepts (40%) compared to concrete ones (20%). Within concrete concepts, they had major doubts about sentences related to animals (27.5%). Notice that the last percentages on uncertainty were calculated based on the responses to an open-ended question, in which participants were free to express doubts about one or more sentences used in the task.

Bayesian Generalized linear mixed models were applied to estimate the probability of different models' hypotheses. The Bayesian approach determines the probability that a model’s parameters take on different values, given the observed data. According to Bayes’ theorem, this is the combination of our prior expectations and the likelihood that we would have observed our data given different parameter values. Thus, functions describing the prior and the likelihood are combined to create a posterior density function. This is then sampled, and the resulting sample can be used to establish the 95% credible intervals: the range of values with a 95% probability of containing the true value for a given parameter. When a given parameter’s credible interval does not include zero, we consider it significantly different from zero and worth interpreting.

The Bayes factor (BF) is the ratio of the probabilities of the data in models 1 and 2 and indicates how much the prior odds change, given the data 55 . Conventionally, for converting the magnitude of the BF to a discrete decision about the models is that there is “substantial” evidence for model 1 when the BF exceeds 3.0 and, equivalently, “substantial” evidence for model 2 when the BF is less than 1/3 55 . In the present study, before proceeding with the inferential analyses, for each output variable, we selected the model with the highest Bayes Factor among those having substantial evidence. This procedure allows us to compare different models and different hypotheses and to choose the one for which we have the greatest evidence. Bayesian Generalized linear mixed models were applied separately on each outcome variable: uncertainty expressions, number of questions, repetitions of the target words, turn-taking, point of view (1st, 2nd, 3rd, 1st or 2nd, 1st and 2nd person perspective), number of evoked contexts, general statements, perceptual evaluations on vision, touch, hearing, smell, taste, materials/components, space, time, events, concrete actions, abstract actions, interoception, emotion, metacognition, belief/intentions, introspection, associations, subordinates, non-perceptual evaluations, why-questions, who-questions, where-questions, what-questions, when-questions, how-questions. A total of 37 analyses were developed.

In each model, the predictors were the Type of sentences (Abstract vs. Concrete) and the Kind of concepts. We had three kinds of ACs—Philosophical-Spiritual (PS), Physical-Spatio-Temporal-Quantitative (PSTQ), and Emotional-Mental State-Social (EMSS), three kinds of CCs—Tool, Food, Animals. We decided to use either the Kind of concepts or the Type of sentences as a predictor because these two factors are strongly correlated: it was therefore not possible to include both in the same model. In detail, when the Kind of concepts had zero or very few elements, the Type of sentences was preferred as a predictor. See Table 2 . Further details of the model’s convergence and suitability of effective sample size are available as Supplementary Materials .

The Type of sentences and the Kind of concepts were the within-subject factors. Models included random subject intercepts. Random effects help generalize results beyond a particular set of subjects; accounting for subject-level variation (see 38 ). Bayes factor and credible interval were used to make inferences. Regarding the variables “number of questions” and “number of evoked contexts” that had a count response outcome, models with Poisson distribution with logistic link function were developed, whereas for all other variables models with Binomial distribution with logit link function were carried out. Therefore, the results referring to “number of questions” and “number of evoked context” have been reported as count frequency values while all the other variables, being dichotomous, could be analyzed as percentage.

For models with Type of sentences and Kind of concepts factors, analyses were run computing four sampling chains, each with 10,000 iterations. For each chain, the first 4000 iterations are treated as warmups, resulting in 24,000 posterior samples. In addition, for a better sampler’s behavior, adapt_delta and max_treedepth parameters were set to 0.99 and 15, respectively. For the Null Hypothesis model or only intercept model, analyses were run computing four sampling chains, each with 5000 iterations. For each chain, the first 2000 iterations are treated as warmups resulting in 12,000 posterior samples. In addition, for a better sampler’s behavior, adapt_delta and max_treedepth parameters were set to 0.99 and 10, respectively.

Due to the lack of previous literature on the topic, models were fit using flat priors for fixed and random effects. All models were seen as reliable, reaching convergence with an R.hat that is the potential scale reduction factor on split chains equal to 1.00 and with suitable effective sample size measures evaluated with Bulk_ESS and Tail_ESS. Finally, we used a contrast method to explore the hypnotized differences between the ACs and CCs kinds. The analyses were carried out using R (version 4.0.3 56 ); data processing was also carried out in part using ‘openxlsx’ 57 , ‘dplyr’ 58 , ‘lattice’ 59 , ‘brms’ 60 ; this package allows fitting Bayesian mixed-effects models using the Stan programming language; ‘bayesplot’ 61 ; ‘gridExtra’ 62 and ‘repmod’ 63 . The bar charts on polar axis graphs were carried out by using Python language (version 3.8) and Matplotlib and Numpy libraries.

Data availability

All data and scripts are available at https://osf.io/mzaxw/ .

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Acknowledgements

The authors would like to thank Laura Barca, Chiara Fini, Claudia Mazzuca, and Luca Tummolini for comments and discussions.

A.M. Borghi was supported by H2020-TRAINCREASE-From social interaction to abstract concepts and words: toward human centered technology development (Proposal n. 952324) and by Sapienza Excellence Projects - 2022-24 “Concepts in interaction with others and with ourselves: Abstractness in social interaction, metacognition and mind wandering” (Grant N. RG12117A5D1EB0B3).

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C.V., L.L., and A.B. conceived and designed the study. C.V. performed data collection. C.V. and M.O. coded all the data separately, solving the ambiguities together with A.B. M.O. analyzed and interpreted the data under the supervision of M.B. C.V. and A.B. wrote the main manuscript text, and L.L. provided critical revisions. All authors reviewed and approved the final version of the manuscript for submission.

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Villani, C., Orsoni, M., Lugli, L. et al. Abstract and concrete concepts in conversation. Sci Rep 12 , 17572 (2022). https://doi.org/10.1038/s41598-022-20785-5

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In psycholinguistics, concepts are considered abstract if they do not apply to physical objects that we can touch, see, feel, hear, smell or taste. Psychologists usually distinguish concrete from abstract concepts by means of so-called concreteness ratings . In concreteness rating studies, laypeople are asked to rate the concreteness of words based on the above criterion. The wide use of concreteness ratings motivates an assessment of them. I point out two problems: First, most current concreteness ratings test the intuited concreteness of word forms as opposed to concepts. This ignores the ubiquitous phenomenon of lexical ambiguity. Second, the criterion of abstract concepts that the instruction texts of rating studies rely on does not capture the notion that psychologists working on abstract concepts are normally interested in, i.e., concepts that could reasonably be sensorimotor representations. For many concepts that pick out physical objects, this is not reasonable. In this paper, I propose a characterization of concrete and abstract concepts that avoids these two problems and that may be useful for future studies in psychology.

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1 Introduction

Paradigmatic abstract concepts like ART, Footnote 1 DEMOCRACY or ETERNITY are usually difficult to make sense of even though we generally find it easy and natural to employ them in ordinary conversations. Sentences like “this is an interesting piece of art” or “Germany is a democracy” tend to be appropriately used and reacted to without requiring much contemplation. This ease of use of abstract concepts and words is puzzling considering that when asked to elaborate on what exactly we mean by these terms we are often unable to produce a satisfying answer. In contrast, paradigmatic concrete concepts like BALL, TABLE, WATER or DOG seem to be much easier to make sense of. If asked what we mean by ‘ball’ or ‘table’, it is usually sufficient to point at instances of the respective category or list typical properties in order to be understood.

Because paradigmatic abstract and concrete concepts are often experienced so differently, it is natural to hypothesize that they are of psychologically fundamentally different kinds. At first glance, the vast majority of research in psychology seems to support this intuition. It is now widely accepted that abstract expressions, i.e., expressions associated with abstract concepts, are more difficult to recognize (James 1975 ; Strain et al. 1995 ), recall (Jefferies and Lambon Ralph 2006 ; Nelson and Schreiber 1992 ) and understand (Holmes and Langford 1976 ; Schwanenflugel and Shoben 1983 ) than concrete words. Moreover, it has been argued that abstract concepts tend to be acquired much later than concrete concepts (Brown 1957 ; Schwanenflugel 1992 ) and that they store fundamentally different kinds of information (Barsalou and Wiemer-Hastings 2005 ).

The question that has occupied many theoretical and experimental psychologists is why abstract concepts and concrete concepts and words are processed so differently (see e.g., Schwanenflugel 1992 and Kiefer and Pulvermüller 2012 for reviews). Three theories have been primarily discussed. According to dual coding theories (Paivio 1991 ; Barsalou et al. 2008 ), abstract concepts, because they do not refer to physical entities, are associated with fewer sensorimotor-introspective representations and rely more on language-like, dis-embodied representations. Concrete concepts, on the other hand, are thought to be represented both by linguistic and sensorimotor-introspective representations. Dual coding theorists assume that this difference in format and number of kinds of representations (abstract concepts are supposed to only have the dis-embodied type of representation) makes concrete words faster to process.

According to competing theories like the context-availability theory (especially Schwanenflugel 1992 ), abstract concepts are more difficult to process, not because they are represented by means of fewer sensorimotor representations but because it is more difficult to retrieve general world knowledge that could characterize abstract words sufficiently, especially if presented outside of sentence and discourse context. According to a third influential view championed by Lakoff and Johnson ( 1999 ), abstract concepts are primarily represented via metaphorical mappings to concrete concepts. They are thus more difficult to process and acquire because they ultimately rely on a rich understanding of the concrete concepts that they are allegedly understood in terms of.

To test theories of abstract concepts, psychologists usually compare the cognitive processes underlying the use of abstract and concrete words. When generating stimuli for their studies, they normally rely on so-called “concreteness ratings”. Concreteness ratings enjoy a growing interest in psychology and constitute the foundation of most of the studies that claim to have found a processing difference between abstract and concrete concepts. To illustrate this trend, note that when using the concreteness ratings collected in the easily accessible and highly popular MRC database, Footnote 2 researchers are asked to cite either Wilson ( 1988 ) or Coltheart ( 1981 ). According to Google Scholar, both were cited only 37 times in 2000, 279 times in 2015, 247 times in 2016, 260 times in 2017, 520 times in 2018 and 469 times in 2019. Footnote 3

The growing interest and use of concreteness ratings call for a discussion of how they work and what exactly they measure. I point out two problems for current concreteness ratings like the ratings collected in the MRC database. First, nearly all current concreteness rating studies present subjects with lexical expressions or “lexical forms”, i.e., physical shapes that are conventionally associated with a meaning or concept. Lexical forms are usually highly ambiguous, and their different meanings may differ with respect to their concreteness or abstractness. For example, the word form ‘table’ is rated as highly concrete in the MRC database receiving a concreteness rating of 604 out of 700 even though the same lexical form may not only apply correctly to physical tables but to more abstract representations of distributions, e.g., in a PowerPoint presentation. I call this property of words and lexical expressions “concreteness ambiguity”.

A second problem with current concreteness ratings is that the characterization of the notion of abstract concept that most concreteness rating studies rely on, e.g., when formulating their instruction texts, does not seem to capture the notion that psychologists are normally interested in when studying the difference between abstract and concrete words. Normally, they are interested in concepts that cannot reasonably be grounded or couched in sensorimotor representations (cp., Löhr  2019 ). The instruction texts, however, construe concepts as abstract if they do not apply to physical objects that we can touch, see, feel, hear, smell or taste. The problem with this construal is that many concepts that apply to concrete physical objects are not easy to learn or represent in terms of sensorimotor representations alone. For example, the concept of atom (which received a rating of 481 out of 700, i.e., rated as being rather concrete) applies to a physical object, but to learn what the word ‘atom’ means, more is required than pointing at concrete instances or models of atoms.

To overcome both problems, I propose conditions that take lexical ambiguity into account and that better capture the notion of abstract concept under investigation. The result is not a definition in the sense of necessary and jointly sufficient conditions but a kind of heuristic or characterization that may help psychologists select their stimuli and paradigm examples on a more principled basis. Furthermore, it may provide them with reasons based on which they can justify these selections and develop better instruction texts for future concreteness ratings.

2 What Abstract Concepts Are Not

To understand what is usually meant by ‘abstract concept’ in the relevant areas of psychology, especially psycholinguistics, it will help to first understand what is not meant. Initially, one might assume that the term ‘abstract concept’ simply picks out concepts that denote abstract objects, where ‘abstract object’ is understood in the sense relevant for metaphysics and epistemology (see e.g., Kousta et al. 2011 , for this construal). Paradigm examples of abstract objects are the number ‘2’ or the letter ‘A’. A paradigm example of a concrete object is the computer I am writing on (Rosen 2017 ). Footnote 4 A second common way one might understand the notion of abstract concept is in terms of the notion of abstraction in the sense of generalization. According to this notion, concepts that are more general (‘animal’ is more general than ‘dog’) are also more abstract because they “abstract away” from details in order to derive a more general category.

Both notions of abstract concept – as concepts of abstract objects or as abstractions – would classify most lexicalized concepts as abstract. Even paradigmatic concrete concepts like TABLE do not refer just to a single concrete table but can be used to think about tables in general. Similarly, TABLE is more abstract, in the sense of more general, than the concept DINING TABLE, although, intuitively, both are equally concrete (Bolognesi et al. 2020 ). Moreover, while the notion of generality is clearly important for psychology, it is a different question whether a concept is difficult to process because it is more general or because it lacks physical interaction with a referent (see Bolognesi et al. 2020 for empirical evidence for the distinction between generality and concreteness). The literature discussed here is concerned with the latter but not the former question. Footnote 5

Furthermore, we should distinguish the notion of abstract concept from related notions, in particular the notions of complex concept, vague concept or difficult concept. While DINING TABLE is syntactically more complex than the concept TABLE, it is again a different question whether a concept or word is more difficult to process or learn if it is syntactically complex compared to the question of whether it is more difficult to process if it lacks a physical referent. Similarly, the concept HEAP, unlike the concept TRIANGLE is vague (Russell 1923 ; Williamson 1994 ). While it cannot be determined when a collection of, say, sand, begins to be a heap of sand, the concept of triangle is clearly defined. This does not mean that HEAP is more abstract than TRIANGLE. Similarly, while DEMOCRACY is normally experienced as a “more difficult” concept than DOG, the question is whether – from the perspective of psychology – this is actually the case and whether the reason for the experienced difficulty is that DEMOCRACY does not pick out concrete physical entities.

In this paper, I am interested in a notion of abstract concept that is particularly relevant to current research in psychology and psycholinguistics on how we process language and thought. Concretely, I am interested in a notion of abstract concept that allows us to test whether concepts that could in principle be represented in terms of sensorimotor-introspective representations alone have a processing advantage. Footnote 6 7 The way this notion is currently characterized is not satisfactory. Thus, in the fourth section of this paper, I characterize a notion of abstract concept that captures what makes abstract concepts so interesting from the perspective of psychology. In the next section, I point out two problems with the way concreteness is currently determined in the majority of psychological experiments.

3 Concreteness Ratings

To assess whether a concept is concrete or abstract, psychologists typically start with the rough distinction between concepts that do and concepts that do not refer to concrete physical entities. This rough distinction is based on what I call “the current characterization”, which will be discussed in subsection 3.2 . In addition, psychologists tend to give some examples of what they take to be paradigmatic instances of each category (BALL versus DEMOCRACY, TABLE versus ETERNITY, for example). However, in order to more objectively determine the concreteness of a concept, most psycholinguists rely on rating studies. These rating studies vary in methodology but almost all concreteness rating studies have in common that an often rather small number of subjects (often not more than 30 undergraduate students) are asked to judge the perceived concreteness of a number of lexical forms on a 1–7 Likert scale. The lexical forms are almost exclusively presented visually without sentence or discourse context.

The currently most popular and widely used concreteness ratings are the ones collected in the MRC database. The MRC database is an easily accessible online tool with a user-friendly interface that helps scientists to determine 26 psycholinguistically relevant variables of a large number of single lexical forms (4292 lexical forms in the case of concreteness). These variables include the number of letters, phonemes and syllables of a word as well as its frequency, familiarity, imageability, meaningfulness, age of acquisition and concreteness. The data is taken from a number of different rating studies and are collected in a single database, which allows the researcher to simply click on the variables they are interested in. The researcher will then instantly receive a list of words for which ratings are available in the database.

In the case of concreteness, the MRC database merged data from three bodies of concreteness norms: Toglia and Battig ( 1978 ); Gilhooly and Logie ( 1980 ) and an unpublished extension of Pavio et al. ( 1968 ). According to Wilson ( 1988 ), the different ratings correlate highly and were merged by adjusting both the means and standard deviations before averaging. The result of the merging process is expressed as integer values between 100 (being highly abstract) and 700 (being highly concrete). ‘Boat’, e.g., received a concreteness rating of 637, while ‘superfluity’ received a rating of 237. Most nouns, especially abstract nouns, received a rating between 250 and 550. ‘Democracy’ for example received a rating of 298, while ‘fact’ received a rating of 332. Most importantly, the different rating studies are all based on the following widely used instructions based on Spreen and Schulz ( 1966 , 460):

Nouns may refer to persons, places and things that can be seen, heard, felt, smelled or tasted or to more abstract concepts that cannot be experienced by our senses. The purpose of this experiment is to rate a list of words with respect to “concreteness” in terms of sense-experience. Any word that refers to objects, materials or persons should receive a high concreteness rating; any word that refers to an abstract concept that cannot be experienced by the senses should receive a low concreteness rating. Think of the words “chair” and “independence.” “Chair” can be experienced by our senses and therefore should be rated as high concrete; “independence” cannot be experienced by the senses as such and therefore should be rated as low concrete (or abstract.)

In the next two subsections, I will argue that there are two serious problems with this instruction text. First, it does not capture the importance of lexical ambiguity, which generates the problem of concreteness ambiguity – the phenomenon that the same lexical expression can be associated with two or more concepts that have different degrees of concreteness. Second, I argue that it does not capture the notion of concreteness that psychologists who study concreteness are usually interested in. The current characterization of this notion is extensionally insufficient as it excludes concepts from the class of concrete concepts that we intuitively identify as concrete and it excludes concepts from the class of abstract concepts that we intuitively identify as abstract.

3.1 Concreteness Ambiguity

It is commonly assumed that concreteness ratings assess the concreteness of words and concepts. However, what participants of concreteness rating studies are actually presented with are not concepts but visual de-contextualized lexical forms or shapes. To understand why this is a problem, we need to understand the important distinction between a lexical form, i.e., a certain sound or shape and that which the form is conventionally associated with, i.e., a certain meaning or concept. This meaning or concept can be abstract or concrete. For example, the shape ‘bank’ may be a single lexical expression or form that is used to express two different concepts with different lexical entries (i.e., a homonym), one denoting financial institutions and one denoting riverbanks. Current concreteness rating studies brush over this important distinction by testing the concreteness of lexical forms (almost exclusively shapes on a computer screen) as opposed to word meanings or concepts.

The problem with presenting participants of a rating study with single de-contextualized lexical forms is that especially abstract expressions tend to be associated with a number of different concepts or meanings (Schwanenflugel et al. 1988 ; Hoffman et al. 2011 ), which makes it difficult to determine which concepts or meanings participants had in mind when reporting their ratings. It can also not be ruled out that participants represent two different meanings or senses associated with a lexical expression and report the average of their assessment. This would explain the high variance of subjects’ ratings (Pollock 2018 ). For example, when presented with the word ‘bank’, subjects may be confused as to whether they are supposed to rate the concreteness of the abstract financial institute or the concrete building that hosts the institute or the riverbank.

The phenomenon of linguistic ambiguity is especially problematic if the same expression is associated with distinct concepts that differ with respect to their degree of concreteness. I call this property of lexical forms “concreteness ambiguity”. Examples of expressions that have this property are homonyms like ‘chair’. The form ‘chair’ refers to the concrete object we can sit on as well as the more abstract category head of a committee . The problem also applies to polysemes like ‘school’ that can refer both to the building of the school as well as the more abstract educational institution (cp. Vicente 2018 ; Löhr 2021a ). Finally, concreteness ambiguity applies to general concepts that can be applied to both physical and abstract objects. For example, ‘art’ applies both to abstract conceptual art and concrete paintings. ‘Border’ applies both to fences, rivers and mountains (natural borders) as well as abstract borders between two countries.

To appreciate the problem of presenting subjects merely with single word forms consider that the practice of de-contextualizing words is highly artificial. For example, participants do not usually see the word form ‘truth’ without any discourse or sentence context. This artificiality is especially problematic for studying abstract concepts and words. While one can have a clear sense of what ‘ball’ means even without the expression being embedded in a sentence, this is not as easy for words like ‘impossible’ or ‘love’. That thinking about the meaning of words out of context is a potential confound was already suggested by the philosopher Gottlob Frege ( 1884 ) who argued that presenting words without a sentence context makes it more likely to construe meaning in terms of conscious mental images.

The problem of single-word studies with respect to accessibility of conscious mental images is of course only a problem for concreteness ratings if imageability is to be distinguished from concreteness. Imageability is another psycholinguistic variable that is often used in order to identify abstract concepts in psychology, especially by dual coding theorists. The degree to which a word is imaginable is also assessed via ratings, using the following instruction text (based on Cortese and Fugett 2004 ):

Words differ in their capacity to arouse mental images of things or events. Some words arouse a sensory experience, such as a mental picture or sound, very quickly and easily whereas other words may do so only with difficulty (i.e., after a long delay) or not at all. The purpose of this experiment is to rate a list of words as to the ease or difficulty with which they arouse mental images.

One might argue that imageability ratings usually do not have the same ambiguity problem as do concreteness studies. This is only the case, however, if the respective researcher is merely interested in the degree to which a given lexical expression arouses imagery. Most psycholinguists will be more interested in whether the form ‘table’, understood as correctly applying to the physical object, arouses more imagery than the same expression, understood as correctly applying to the arrangement of data in rows and columns. Current imageability ratings that are based on the above instructions therefore face the same problem as concreteness ratings. Lexical expressions do not only have the property of concreteness ambiguity but also the property of imageability ambiguity .

Moreover, even though imageability and concreteness ratings are highly correlated (Schwanenflugel 1992 ; Koutas et al. 2011 ), both notions should not be conflated. A word like ‘eternity’, for instance, is a clear example of an abstract concept for which it is relatively easy to form images (think of a lemniscate for example). Unsurprisingly, the word ‘eternity’ received an MRC concreteness rating of 300 out of 700 (again, 700 being highly concrete, 100 being highly abstract) but an imageability rating of 399 out of 700 (700 being highly imageable, 100 arousing very little mental imagery). Similarly, the word ‘etiquette’ received a concreteness rating of 252, but an imageability rating of 388. Participants seemed to agree that ‘etiquette’, even though it is rated as rather abstract, generates a lot of concrete mental imagery.

Furthermore, Kousta et al. ( 2011 ) show that imageability and concreteness ratings are differently distributed. Their analysis of ratings of more than 4000 words (derived from the MRC database) shows that imageability has a unimodal frequency distribution, while concreteness is bimodally distributed. This suggests that concreteness is a categorical property with two poles in the frequency distribution, while imageability is a graded property with only one pole. This also fits the observation that the description of imageability in the psycholinguistic literature picks out a vague property without clear boundaries, while the description of concreteness does arguably only ask for whether a concept does or does not denote a physical object.

So, the category of words whose referents are highly imageable is an interesting psychological category in its own right and ought to be distinguished from the category of words whose referents we can directly be perceived. More importantly, concreteness is not on the same explanatory level as other psycholinguistic variables, such as imageability. To construe them on the same level is to commit what philosophers call a “category mistake” (Magidor 2013 ; Ryle 1949 ). Concreteness ratings tell us whether a given concept or word refers to a physical object or not. Assuming the finding of a real “concreteness effect” from a well-controlled study, i.e., a real difference in processing between abstract and concrete words, we can then ask: what about the lack of physical referents makes concepts more difficult to process or what explains this concreteness effect? Lack of imageability could offer such an explanation Footnote 7 8 but an answer in terms of lack of physical referent would be circular. Footnote 8

To sum up, one major problem of concreteness ratings is that they measure the intuited concreteness of lexical forms when they are supposed to measure the concreteness of concepts. The best, or perhaps only, way to avoid the problem of concreteness ambiguity is to test the concreteness of words not by presenting mere lexical forms, but by presenting lexical shapes in different sentence contexts. For example, in order to test the concreteness of ‘school’ one might present a subject with two sentences “John enters the school after a break” and “The school celebrated its first anniversary”. To test the concreteness of ‘border’, one might present a subject with a sentence “John jumped over the border” and a sentence like “the border between Germany and France is open again”. To test the concreteness of ‘bank’, one might present the participant with the sentences “John sold the bank” and “John entered the bank”.

3.2 Problems with the Current Characterization

Another major problem for current concreteness ratings is that most instruction texts for participants not only ignore the problem of lexical ambiguity – in particular concreteness ambiguity – but also fail to capture the relevant notion of abstract concept. The main reason why abstract concepts are interesting for the study of the mind and language is a long-standing interest in the question of whether it makes an interesting cognitive difference whether a concept refers to an entity that we can directly touch, see, hear, feel, smell and taste (Löhr 2019 ). The intuition shared by many psychologists is that direct interaction with physical objects makes cognition generally easier and that children are faster at learning words of objects that they can directly touch and see (for a review see e.g., Schwanenflugel 1992 ). The most basic condition for being an abstract concept is thus that it does not refer to physical objects that we can directly (i.e., without the help of special instruments) touch, feel, hear, smell, taste or see.

Unfortunately, the condition of lack of directly perceivable physical object that current concreteness ratings rely on (see the instruction text mentioned above) is not adequate to pick out the class of concepts that may allow us to study the impact of direct interaction on cognition. The main problem is that many concepts that we intuitively construe as abstract apply to entities that we can directly touch and see. For example, the concept of manager is intuitively rather abstract (a child will not learn this concept by seeing a manager) even though it usually applies to human beings we can directly touch and see (I can literally see and touch a manager). The same goes for SECRETARY OF STATE, PRESIDENT or BACHELOR.

Second, some paradigmatic abstract concepts like ATOM or UNIVERSE refer to physical material things that we can, in a sense, directly see or touch (I can literally see and touch a table made of atoms just as I am literally sitting inside the universe). Both are, however, far too small or far too large for us to conceptualize anything as atoms or universes by means of direct perception alone and without additional education. While touching a middle-sized object, say a ball, is arguably sufficient for us to think about something as a ball, touching a table is not sufficient to conceptualize this table as a collection of atoms or as a constituent of the universe. The current characterization of the meaning of ‘abstract concept’ that most concreteness ratings rely on cannot capture the abstractness of very large and very small physical objects.

Third, as argued above, many concepts apply both to physical objects as well as more abstract ideas (what I called “concreteness ambiguity”). For example, the concept of border applies both to fences as well as immaterial borders between countries. The concept of art applies both to concrete paintings, conceptual art, as well as the more general abstract idea of art. The expression ‘book’ applies not only to the physical object but also its informational content (Falkum 2011 ; Liebesman and Magidor 2017 ). Thus, it is not clear whether we should judge a concept that applies to a physical directly perceivable object as concrete if it also applies to other entities that are more abstract.

A fourth problematic class of concepts for the current characterization of ‘abstract concept’ are simple adjectives like ‘red’ or ‘tall’ as well as verbs like ‘running’ or ‘climbing’. Intuitively, they seem rather concrete but relying on the current notion of concrete concept as referring to physical objects that we can directly touch and see, neither RED nor RUNNING should be considered concrete. This is a problem for research on the impact of direct perceptual access on cognition because, again, RED and RUNNING seem to be exactly the kind of concepts that could, prima facie at least, be acquired, represented and applied by means of perceptual representations alone.

A fifth challenging class of concepts for the current notion of abstract concepts are superordinate concepts like VEGETABLE, FRUIT or ANIMAL. Superordinate concepts often refer to concrete objects we can touch and see and should thus be rated as highly concrete based on our initial characterization above. On the other hand, since superordinate categories are still highly general, more is needed to acquire such categories than seeing instances of fruits or animals. Unlike subordinate and basic-level categories, superordinate categories are impossible to acquire from seeing or touching typical instances because the perceptually accessible properties of superordinate categories are, by definition, not diagnostic of the category (Rosch 1978 ; Lakoff 1987 ). Consider for example the category FRUIT. Even typical fruits, such as apples, bananas and pineapples, have superficially little in common from which we could perceptually derive the relevant superordinate category (Löhr 2017 ).

So, the main problem for the current construal of abstract concepts is that the ability to directly touch or see the physical objects that these concepts apply to alone is extensionally insufficient. Atoms are too small and the universe is too vast for us to conceptualize something in terms of their respective concepts, at least by means of perceptual interaction alone. Being taught what an electron is simply by being presented with a concrete representation of an electron (e.g., a ball) helps little to understand what is meant by ‘electron’ (Schurz 2015 ). On the other hand, some concepts like RED or RUNNING seem to be rather perceptually accessible without them referring to physical objects.

In order to capture a notion of abstract concepts that allows us to investigate the role of direct physical and perceptual interaction on concept acquisition, representation and processing, more is needed than the current characterization provides us with. What we need is a characterization that captures a class of concepts for which it is at least reasonable to hypothesize that their acquisition, representation and applications (e.g., in categorization) can, at least to a substantial degree, be explained entirely by means of sensorimotor and introspective representations alone. Thus, we want a characterization that picks out only those concepts for which the following is at least plausible: if it is at all true that some concepts could, at least in theory, be represented and applied sufficiently by means of sensorimotor-introspective representations alone, then it should be those concepts that we call “concrete”. Footnote 9

4 A Characterization of ‘Abstract Concept’ for Psychology

The aim of the characterization I propose in this section is to capture a certain notion or idea. This description captures that which interests us about certain concepts, i.e., properties that make abstract concepts interesting for the study of the mind. It will allow psychologists to more clearly identify at least paradigmatic instances of abstract concepts taking lexical ambiguity into account and without conflating concreteness with generality, difficulty, vagueness, complexity or imageability. In other words, the aim is to give a theoretically motivated characterization of the phenomenon under investigation that is useful for psychologists to select paradigm cases on a principled basis and to formulate a better instruction text for future concreteness studies. Moreover, it will provide psychologists with potential justifying reasons for their methodological choices (e.g., with respect to selecting stimuli).

I propose that the following characterization of abstract and concrete concepts captures the requirements mentioned above.

Concrete concept: A concept is concrete if

it applies to events, actions, properties, relations, or objects whose diagnostic features are perceptually, motorically or introspectively directly accessible and

it is reasonable that representing these features is sufficient for the possession of the concept.

Abstract concept: A concept is abstract if

it applies to events, actions, properties, relations, or objects that do not share diagnostic features that are perceptually, motorically or introspectively directly accessible or

it is reasonable that representing the diagnostic features is not sufficient for the possession of the concept.

The here proposed characterization differs from the old characterization in three ways. First, in order to avoid the problem caused by lexical ambiguity and concreteness ambiguity, it does not refer to words or lexicalized forms but concepts. Second, it includes concepts that refer to entities that are not objects. This is motivated by the assumption that certain events and actions like running may be just as concrete, in the relevant sense, as tables or dogs. Third, it gives a prominent role to diagnostic features, which is a familiar notion in psychology that is fundamental to theories of categorization like prototype theory (Rosch 1978 ; Lakoff 1987 ). The notion that some features may be more or less diagnostic of a category also plays an important role in popular theories of concept acquisition in philosophy of psychology (Fodor 1998 ; Margolis 1998 ; Prinz 2002 ; Margolis and Laurence 2011 ; Löhr 2021b ). The idea that these philosophical theories have in common is that representing certain diagnostic features of a category may suffice for the possession of the corresponding concept, i.e., without having to represent necessary and sufficient conditions that pick out the corresponding referent. Put differently, I argue that whether or not a concept should be considered as being concrete has to do with whether its diagnostic sensorimotor features may reasonably suffice for concept possession rather than whether it has a physical referent. Footnote 10

The notion of concrete concept characterized here applies to concepts like BALL, TABLE and RUNNING, but not to concepts like ATOM, UNIVERSE or DEMOCRACY. Again, in order to acquire and apply BALL it is at least reasonable that all we require is to touch and see balls. This is because balls are not only physical objects we can directly see and touch but also because that which we can directly see and touch is highly diagnostic of the category ‘ball’. It is at least reasonable that what we can see and touch is sufficient to acquire and apply the respective concept. Similarly, even though RUNNING does not pick out a physical object, it still seems reasonable to assume that all we need to do to acquire and apply this concept is to see someone making certain movements assuming the movements are highly diagnostic of the category.

To make the basic idea underlying the proposed characterization more vivid, I consider concepts concrete for which it makes sense to assume that merely pointing at instances (without much knowledge of practices and customs of the respective linguistic community) suffices to be able to learn what the respective word means (cp. Schurz 2015 on the notion of ostensive learnability). Paradigmatic examples of concrete concepts are then EAR, HAIR, WIND, SUN, MOON, NOSE, WATER, TABLE, HAMMER, i.e., everything that a person who is a total stranger to a linguistic community may have less trouble picking up as one of their first words. To acquire the concept of democracy, on the other hand, arguably more is needed than merely seeing people put paper in a box. Similarly, in order to acquire the concept ATOM, more is needed than seeing a ball-like representation of an atom. In order to successfully acquire and apply ATOM we seem to require the information that atoms are incredibly small and that which constitutes all objects we are familiar with (see again Schurz 2015 ).

Importantly, the characterization proposed here provides a plausible reason to exclude superordinate concepts from the class of concrete concepts. For example, apples share a number of superficial, i.e., perceptually directly accessible properties that are highly diagnostic of the category. The class of apples is relatively homogenous, which allows us, at least in principle, to acquire the category from seeing a few instances (cp. Margolis 1998 , Prinz 2002 ). This ceases to be the case for superordinate categories like ‘fruit’ that are more heterogeneous. Typical fruits like apples share few immediately visible features with other typical fruits like bananas or pineapples. Thus, in order to teach a child the concept of fruit, we arguably need to do more than show them a number of typical fruits. For example, what could be sufficient in addition is to learn practices associated with fruits. In other words, only basic-level and sub-ordinate concepts (Rosch 1978 ) can be concrete concepts.

Regarding words that are ambiguous with respect to their concreteness (cases of concreteness ambiguity), such as ‘border’ or ‘art’, we can now say that even though a border can be instantiated concretely, the word ‘border’ is a more general term that applies to many different kinds of borders. This is not the case for a concrete word like ‘fence’ which always applies to concrete physical objects. So, it makes sense to hypothesize that we can acquire FENCE simply by seeing, touching and interacting with fences, while this is more difficult for a more general concept like BORDER, which requires more than mere physical interaction even though there can be concrete instances of borders. In the case of expressions that refer both to abstract and concrete objects (such as ‘book’), this issue can arguably be resolved by hypothesizing that a single word is associated with both abstract and concrete concepts.

Finally, with respect to intuitively concrete concepts like RED or ROUND, such concepts can be viewed as primitive in the sense that we can classify something as red without this being mediated by other representations, such as beliefs of what conditions a thing has to meet in order to be red. It is possible that children immediately classify things as red and round simply by means of unlearned innate capacities and it is plausible that especially these concepts can be represented and applied by sensorimotor symbols alone. In this sense, RED refers to a property that we can directly see (we can directly see things as red) and instances of red are homogeneous and diagnostic of the category of red not because of the intrinsic properties of those things, but because certain things simply look red to us (see Fodor 1998 for the discussion on the semantics of color concepts). Footnote 11

I argue that the proposed characterization of abstract and concrete concept captures well what we want the notion of abstract concept to capture in psychology and cognitive science. In other words, it is extensionally superior to the current characterization. Again, I assume that the aim is to investigate the impact of direct sensorimotor interaction with a referent on concept and language acquisition and processing. The proposed characterization of concrete concept picks out concepts for which it is at least reasonable that their acquisition, representation and application (e.g., in categorization) can be fully explained by sensorimotor representations. Most importantly, the characterization does not conflate concreteness with imageability, complexity, difficulty and generality, even though many abstract concepts are also highly general, complex, difficult and score low on imageability ratings.

Finally, it should be noted, that the here proposed characterization is both more restrictive and relaxed than the notion of abstract concept that has been used in psycholinguistic experiments where psychologists rely on a less theoretically motivated distinction and the above discussed concreteness ratings. It is more restrictive because it acknowledges the important relation between superficial and diagnostic properties that excludes superordinate categories from being concrete. It is more relaxed in the sense that I do not restrict the notion to physical objects. Most importantly, compared to the way concreteness is currently estimated – mostly by looking at expressions and not concepts – the here proposed way to distinguish abstract from concrete concepts is far more fine-grained.

To illustrate the advantage of the here proposed characterization, take Sadoski et al.’s ( 1997 ) investigation of the concreteness effect, i.e., the question of whether there is a processing difference between concrete and abstract words. The authors followed Reynolds and Paivio ( 1968 ) and classified the expressions ‘library’, ‘prisoner’, ‘picture’, ‘hotel’, and ‘mother’ as concrete words and ‘crime’, ‘science’, ‘mind’, ‘fun’ and ‘death’ as abstract words. It is not a clear-cut matter whether these expressions are concrete or abstract especially if all we have available is a characterization that does not acknowledge lexical ambiguity. For example, the word ‘death’ is commonly thought of as abstract referring to a supposedly abstract “idea”, but the same expression also refers to the impersonation of death, which is highly iconic and concrete. A characterization based on the idea that concrete concepts refer to physical concrete objects fails to acknowledge that the concept of death is usually easily applied to physical objects and accompanied by properties that are highly diagnostic of the category. It is not implausible that a non-linguistic child and even a non-linguistic animal could be able to distinguish dead from living animals by means of vision and smell alone. Prima facie, DEATH is a highly abstract concept but on a closer look it seems less clear whether it may not be rather concrete in the sense relevant for research in cognitive science, especially psycholinguistics.

The here proposed characterization provides a more useful, sensible and principled assessment of the expressions selected by Sadoski and colleagues. First, unlike the old characterization, the here proposed characterization emphasizes that the properties concreteness and abstractness apply to concepts as opposed to lexical forms. This means that it may be that a word like ‘mother’ may be both abstract and concrete depending on which concept or meaning the subject retrieves first. Second, it emphasizes diagnosticity as crucial determiner of concreteness. For this reason, ‘library’, ‘prisoner’, ‘picture’ and ‘mother’ may all be concrete based on my characterization if, for example, in the respective culture those categories may have clear perceptually accessible diagnostic properties. It is not unreasonable that a person who is new to a linguistic community and has never seen a library before may acquire the meaning of the word ‘library’ just by visiting a distinctive looking place with books. Footnote 12

To give another example, consider two concepts of mother, one being PRIMARY FEMALE CAREGIVER, the other being THE WOMAN WHO IS THE SOURCE OF ONE’S MITOCHONDRIAL DNA. Intuitively, the former concept seems significantly more concrete than the latter. It is not unreasonable that this impression can be captured by the notion that the former may sufficiently be acquired, represented and applied by sensorimotor representations alone (representing always being around , nurturing ). The sensorimotor experience made with the primary female caregiver is so diagnostic of the category that it is plausible, at least from a theoretical perspective, that it suffices to possess this concept of mother (cp. Margolis 1998 ; Löhr 2021b ). More is needed to possess the more demanding scientific concept involving scientific concepts like DNA.

Note that I do not claim that the here proposed characterization is a definition in the sense of necessary and jointly sufficient conditions. This also means that the characterization does not entail that the distinction between abstract and concrete concepts is binary or clear-cut. There may be many concepts that do not clearly fit either classification. I also do not claim that it is already suitable for concreteness rating studies. In fact, it might still be difficult for laypeople and researchers to determine which words or concepts meet the conditions. The question of what part of society will be best suited to judge which concepts meet the conditions cannot be answered here as it requires further empirical and methodological research. Again, the hope is that the here proposed characterization and the previous remarks will at least help to improve future instruction texts and concreteness studies. Moreover, the main focus here is on providing a better description of what kind of concepts we are in fact interested in.

Crucially, I do not claim that a concreteness study based on the here proposed characterization necessarily generates different stimuli assuming that future studies take concreteness ambiguity into consideration. It would of course be a relief if a set of paradigm examples that would be selected based on the here proposed characterization strongly overlaps with the set of examples generated by currently available studies. A strong overlap would support previous findings of a concreteness effect especially if such findings are based on stimuli generated by stimuli with extreme ratings and low standard deviation as proposed e.g., by Pollock ( 2018 ) and Verheyen et al. ( 2020 ). In fact, at least many of the words in the MRC database rated as clearly concrete (600–700) and the words rated as highly abstract (100–300) also meet the conditions of concreteness and abstractness proposed here. The difficult words are the ones that received ratings between 300 and 600, such as ‘essay’ (527) or ‘evangelist’ (500) both of which possess a high degree of concreteness ambiguity (essays and evangelists are, in a sense, physical objects but cannot merely be acquired ostensibly).

The aim of the characterization proposed here was to provide psycholinguists with a characterization or description of what especially these paradigm examples may have in common that make them scientifically interesting. My hope is that if stimuli are presented within sentence context and with an instruction text based on the characterization provided here, subjects of rating studies will make at least more specific and accurate classifications than the current characterization allows them to. Moreover, I hope that it will allow psychologists to identify paradigmatic instances of abstract concepts on more principled grounds beyond mere intuition.

5 Conclusion

There has been a growing interest in concreteness ratings in recent years. This growing interest motivated this critical discussion of what exactly concreteness ratings are and what they measure. I pointed out two problems. First, most current concreteness ratings do not measure the concreteness of words or concepts but of lexical forms. Second, the current characterization that the instruction texts of most concreteness studies are usually based on does not capture the relevant notion of abstract concept. I argued that in order to alleviate the first problem, stimuli should be presented in a sentence or even discourse contexts that can draw out the relevant senses of the word. In order to alleviate the second problem, I proposed a characterization of a notion of concreteness that avoids the problems of the current understanding. This characterization may be used to design more fruitful concreteness rating studies that avoid the problem of concreteness ambiguity.

I use capital letters as names of concepts, single quotation marks to denote words, and italics to denote properties. As is common in the philosophical literature on concepts (e.g., Fodor 1998 ), I sometimes use the expression ‘the concept of table’ instead of using the name TABLE. The terms ‘concept’ and ‘notion’ are used synonymously.

https://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm

Note that the MRC database contains more than just concreteness ratings. It also includes familiarity ratings, imageability or meaningfulness ratings, as well as measures with respect to the phonetic form of words taken from a variety of rating studies. Thus, the increase of popularity of the database may be due to the increase of use of the other measures and not of concreteness. However, a quick google search reveals that most of the recent papers who cite the MRC database also contain the keyword ‘concreteness’. Note also that most experimental studies rely on a number of different measures including concreteness along with, say, frequency and imageability.

One of the main differences between abstract and concrete objects is that while two abstract objects can be instantiated in the very same concrete object (a ball can be both round and red), concrete objects like my computer cannot be at the very same location as another computer unless it is identical to it or its constituent.

One might also understand the notion of abstract concept in terms of the countability/mass distinction in linguistics. However, as Zamparelli ( n.d. ) convincingly shows, abstract and concrete concepts can both be mass and countability terms. This speaks against Borghi et al.’s ( 2017 ) recent proposal that abstract concepts can be individuated by means of boundedness.

I do not take a stance in this paper on whether introspection should be included or not (see Connell et al. 2018 ). If this is what we are interested in with respect to abstract concepts, then it should. Otherwise not.

For example, it was proposed that mental imagery is more directly connected to or identical with the conceptual system and can therefore allow concrete concepts to be processed more directly (Glaser 1992 ), assuming that we have more mental imagery of physical objects we can directly see and touch.

Imageability, too, is an explanandum. It raises the question of why the lack of imageability would have a cognitive effect. But, again, this question is on a different explanatory level as the explanandum of concreteness. The same is the case for other psycholinguistic variables, such as age of acquisition. We can explain age of acquisition for example in terms of difference in concreteness, familiarity or frequency.

This of course leaves open the empirical possibility that even concrete concepts require more than just sensorimotor introspective representations.

Some readers may be reminded of a proposal by Della Rosa et al. ( 2010 ) who tests the mode of acquisition (MOA) of concepts, i.e., whether a word is acquired merely by means of language. However, MOA is a difficult measure with the same flaws that were pointed out earlier given as subjects are again only presented with single words.

Note that even though it may be more difficult to learn which concept fits to the use of the lexical form ‘red’ than it is to learn the meaning of ‘ball’ (Werning 2010 ; Gärdenfors 2019 ), this does not necessarily mean that for a baby to possess the concept of red or to adequately distinguish red from non-red things, we need language or amodal representations (although it might still be possible that this is the case, it is rather unlikely). The so-called “complex first problem” applies to language and not to concepts.

Note that ‘library’, too, is highly polysemous. It may refer to the building as well as the institution. It may also apply to a set of data or information used by computer programs. Thus, a child who learns to use the word as applied to the institution and building associated with books may not possess the concept relevant for computer scientists.

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Acknowledgements

I thank Gabriella Vigliocco, Markus Kiefer, Edouard Machery, James Hampton, Lewis Pollock, Markus Werning, Albert Newen, Juan Loaiza, Dimitri Mollo as well as the editor and the two reviewers for their very helpful comments on earlier drafts.

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Löhr, G. What Are Abstract Concepts? On Lexical Ambiguity and Concreteness Ratings. Rev.Phil.Psych. 13 , 549–566 (2022). https://doi.org/10.1007/s13164-021-00542-9

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Abstract Writing: A Step-by-Step Guide With Tips & Examples

Table of Contents

Introduction

Abstracts of research papers have always played an essential role in describing your research concisely and clearly to researchers and editors of journals, enticing them to continue reading. However, with the widespread availability of scientific databases, the need to write a convincing abstract is more crucial now than during the time of paper-bound manuscripts.

Abstracts serve to "sell" your research and can be compared with your "executive outline" of a resume or, rather, a formal summary of the critical aspects of your work. Also, it can be the "gist" of your study. Since most educational research is done online, it's a sign that you have a shorter time for impressing your readers, and have more competition from other abstracts that are available to be read.

The APCI (Academic Publishing and Conferences International) articulates 12 issues or points considered during the final approval process for conferences & journals and emphasises the importance of writing an abstract that checks all these boxes (12 points). Since it's the only opportunity you have to captivate your readers, you must invest time and effort in creating an abstract that accurately reflects the critical points of your research.

With that in mind, let’s head over to understand and discover the core concept and guidelines to create a substantial abstract. Also, learn how to organise the ideas or plots into an effective abstract that will be awe-inspiring to the readers you want to reach.

What is Abstract? Definition and Overview

The word "Abstract' is derived from Latin abstractus meaning "drawn off." This etymological meaning also applies to art movements as well as music, like abstract expressionism. In this context, it refers to the revealing of the artist's intention.

Based on this, you can determine the meaning of an abstract: A condensed research summary. It must be self-contained and independent of the body of the research. However, it should outline the subject, the strategies used to study the problem, and the methods implemented to attain the outcomes. The specific elements of the study differ based on the area of study; however, together, it must be a succinct summary of the entire research paper.

Abstracts are typically written at the end of the paper, even though it serves as a prologue. In general, the abstract must be in a position to:

• Describe the paper.
• Identify the problem or the issue at hand.
• Explain to the reader the research process, the results you came up with, and what conclusion you've reached using these results.
• Include keywords to guide your strategy and the content.

Furthermore, the abstract you submit should not reflect upon any of  the following elements:

• Examine, analyse or defend the paper or your opinion.
• What you want to study, achieve or discover.
• Be redundant or irrelevant.

After reading an abstract, your audience should understand the reason - what the research was about in the first place, what the study has revealed and how it can be utilised or can be used to benefit others. You can understand the importance of abstract by knowing the fact that the abstract is the most frequently read portion of any research paper. In simpler terms, it should contain all the main points of the research paper.

What is the Purpose of an Abstract?

Abstracts are typically an essential requirement for research papers; however, it's not an obligation to preserve traditional reasons without any purpose. Abstracts allow readers to scan the text to determine whether it is relevant to their research or studies. The abstract allows other researchers to decide if your research paper can provide them with some additional information. A good abstract paves the interest of the audience to pore through your entire paper to find the content or context they're searching for.

Abstract writing is essential for indexing, as well. The Digital Repository of academic papers makes use of abstracts to index the entire content of academic research papers. Like meta descriptions in the regular Google outcomes, abstracts must include keywords that help researchers locate what they seek.

Types of Abstract

Informative and Descriptive are two kinds of abstracts often used in scientific writing.

A descriptive abstract gives readers an outline of the author's main points in their study. The reader can determine if they want to stick to the research work, based on their interest in the topic. An abstract that is descriptive is similar to the contents table of books, however, the format of an abstract depicts complete sentences encapsulated in one paragraph. It is unfortunate that the abstract can't be used as a substitute for reading a piece of writing because it's just an overview, which omits readers from getting an entire view. Also, it cannot be a way to fill in the gaps the reader may have after reading this kind of abstract since it does not contain crucial information needed to evaluate the article.

To conclude, a descriptive abstract is:

• A simple summary of the task, just summarises the work, but some researchers think it is much more of an outline
• Typically, the length is approximately 100 words. It is too short when compared to an informative abstract.
• A brief explanation but doesn't provide the reader with the complete information they need;
• An overview that omits conclusions and results

An informative abstract is a comprehensive outline of the research. There are times when people rely on the abstract as an information source. And the reason is why it is crucial to provide entire data of particular research. A well-written, informative abstract could be a good substitute for the remainder of the paper on its own.

A well-written abstract typically follows a particular style. The author begins by providing the identifying information, backed by citations and other identifiers of the papers. Then, the major elements are summarised to make the reader aware of the study. It is followed by the methodology and all-important findings from the study. The conclusion then presents study results and ends the abstract with a comprehensive summary.

In a nutshell, an informative abstract:

• Has a length that can vary, based on the subject, but is not longer than 300 words.
• Contains all the content-like methods and intentions
• Offers evidence and possible recommendations.

Informative Abstracts are more frequent than descriptive abstracts because of their extensive content and linkage to the topic specifically. You should select different types of abstracts to papers based on their length: informative abstracts for extended and more complex abstracts and descriptive ones for simpler and shorter research papers.

What are the Characteristics of a Good Abstract?

• A good abstract clearly defines the goals and purposes of the study.
• It should clearly describe the research methodology with a primary focus on data gathering, processing, and subsequent analysis.
• A good abstract should provide specific research findings.
• It presents the principal conclusions of the systematic study.
• It should be concise, clear, and relevant to the field of study.
• A well-designed abstract should be unifying and coherent.
• It is easy to grasp and free of technical jargon.
• It is written impartially and objectively.

What are the various sections of an ideal Abstract?

By now, you must have gained some concrete idea of the essential elements that your abstract needs to convey . Accordingly, the information is broken down into six key sections of the abstract, which include:

An Introduction or Background

Research methodology, objectives and goals, limitations.

Let's go over them in detail.

The introduction, also known as background, is the most concise part of your abstract. Ideally, it comprises a couple of sentences. Some researchers only write one sentence to introduce their abstract. The idea behind this is to guide readers through the key factors that led to your study.

It's understandable that this information might seem difficult to explain in a couple of sentences. For example, think about the following two questions like the background of your study:

• What is currently available about the subject with respect to the paper being discussed?
• What isn't understood about this issue? (This is the subject of your research)

While writing the abstract’s introduction, make sure that it is not lengthy. Because if it crosses the word limit, it may eat up the words meant to be used for providing other key information.

Research methodology is where you describe the theories and techniques you used in your research. It is recommended that you describe what you have done and the method you used to get your thorough investigation results. Certainly, it is the second-longest paragraph in the abstract.

In the research methodology section, it is essential to mention the kind of research you conducted; for instance, qualitative research or quantitative research (this will guide your research methodology too) . If you've conducted quantitative research, your abstract should contain information like the sample size, data collection method, sampling techniques, and duration of the study. Likewise, your abstract should reflect observational data, opinions, questionnaires (especially the non-numerical data) if you work on qualitative research.

The research objectives and goals speak about what you intend to accomplish with your research. The majority of research projects focus on the long-term effects of a project, and the goals focus on the immediate, short-term outcomes of the research. It is possible to summarise both in just multiple sentences.

In stating your objectives and goals, you give readers a picture of the scope of the study, its depth and the direction your research ultimately follows. Your readers can evaluate the results of your research against the goals and stated objectives to determine if you have achieved the goal of your research.

In the end, your readers are more attracted by the results you've obtained through your study. Therefore, you must take the time to explain each relevant result and explain how they impact your research. The results section exists as the longest in your abstract, and nothing should diminish its reach or quality.

One of the most important things you should adhere to is to spell out details and figures on the results of your research.

Instead of making a vague assertion such as, "We noticed that response rates varied greatly between respondents with high incomes and those with low incomes", Try these: "The response rate was higher for high-income respondents than those with lower incomes (59 30 percent vs. 30 percent in both cases; P<0.01)."

You're likely to encounter certain obstacles during your research. It could have been during data collection or even during conducting the sample . Whatever the issue, it's essential to inform your readers about them and their effects on the research.

Research limitations offer an opportunity to suggest further and deep research. If, for instance, you were forced to change for convenient sampling and snowball samples because of difficulties in reaching well-suited research participants, then you should mention this reason when you write your research abstract. In addition, a lack of prior studies on the subject could hinder your research.

Your conclusion should include the same number of sentences to wrap the abstract as the introduction. The majority of researchers offer an idea of the consequences of their research in this case.

Your conclusion should include three essential components:

• A significant take-home message.
• Corresponding important findings.
• The Interpretation.

Even though the conclusion of your abstract needs to be brief, it can have an enormous influence on the way that readers view your research. Therefore, make use of this section to reinforce the central message from your research. Be sure that your statements reflect the actual results and the methods you used to conduct your research.

Good Abstract Examples

Abstract example #1.

Children’s consumption behavior in response to food product placements in movies.

The abstract:

"Almost all research into the effects of brand placements on children has focused on the brand's attitudes or behavior intentions. Based on the significant differences between attitudes and behavioral intentions on one hand and actual behavior on the other hand, this study examines the impact of placements by brands on children's eating habits. Children aged 6-14 years old were shown an excerpt from the popular film Alvin and the Chipmunks and were shown places for the item Cheese Balls. Three different versions were developed with no placements, one with moderately frequent placements and the third with the highest frequency of placement. The results revealed that exposure to high-frequency places had a profound effect on snack consumption, however, there was no impact on consumer attitudes towards brands or products. The effects were not dependent on the age of the children. These findings are of major importance to researchers studying consumer behavior as well as nutrition experts as well as policy regulators."

Abstract Example #2

Social comparisons on social media: The impact of Facebook on young women’s body image concerns and mood. The abstract:

"The research conducted in this study investigated the effects of Facebook use on women's moods and body image if the effects are different from an internet-based fashion journal and if the appearance comparison tendencies moderate one or more of these effects. Participants who were female ( N = 112) were randomly allocated to spend 10 minutes exploring their Facebook account or a magazine's website or an appearance neutral control website prior to completing state assessments of body dissatisfaction, mood, and differences in appearance (weight-related and facial hair, face, and skin). Participants also completed a test of the tendency to compare appearances. The participants who used Facebook were reported to be more depressed than those who stayed on the control site. In addition, women who have the tendency to compare appearances reported more facial, hair and skin-related issues following Facebook exposure than when they were exposed to the control site. Due to its popularity it is imperative to conduct more research to understand the effect that Facebook affects the way people view themselves."

Abstract Example #3

The Relationship Between Cell Phone Use and Academic Performance in a Sample of U.S. College Students

"The cellphone is always present on campuses of colleges and is often utilised in situations in which learning takes place. The study examined the connection between the use of cell phones and the actual grades point average (GPA) after adjusting for predictors that are known to be a factor. In the end 536 students in the undergraduate program from 82 self-reported majors of an enormous, public institution were studied. Hierarchical analysis ( R 2 = .449) showed that use of mobile phones is significantly ( p < .001) and negative (b equal to -.164) connected to the actual college GPA, after taking into account factors such as demographics, self-efficacy in self-regulated learning, self-efficacy to improve academic performance, and the actual high school GPA that were all important predictors ( p < .05). Therefore, after adjusting for other known predictors increasing cell phone usage was associated with lower academic performance. While more research is required to determine the mechanisms behind these results, they suggest the need to educate teachers and students to the possible academic risks that are associated with high-frequency mobile phone usage."

Quick tips on writing a good abstract

There exists a common dilemma among early age researchers whether to write the abstract at first or last? However, it's recommended to compose your abstract when you've completed the research since you'll have all the information to give to your readers. You can, however, write a draft at the beginning of your research and add in any gaps later.

If you find abstract writing a herculean task, here are the few tips to help you with it:

1. Always develop a framework to support your abstract

Before writing, ensure you create a clear outline for your abstract. Divide it into sections and draw the primary and supporting elements in each one. You can include keywords and a few sentences that convey the essence of your message.

2. Review Other Abstracts

Abstracts are among the most frequently used research documents, and thousands of them were written in the past. Therefore, prior to writing yours, take a look at some examples from other abstracts. There are plenty of examples of abstracts for dissertations in the dissertation and thesis databases.

3. Avoid Jargon To the Maximum

When you write your abstract, focus on simplicity over formality. You should  write in simple language, and avoid excessive filler words or ambiguous sentences. Keep in mind that your abstract must be readable to those who aren't acquainted with your subject.

4. Focus on Your Research

It's a given fact that the abstract you write should be about your research and the findings you've made. It is not the right time to mention secondary and primary data sources unless it's absolutely required.

Conclusion: How to Structure an Interesting Abstract?

Abstracts are a short outline of your essay. However, it's among the most important, if not the most important. The process of writing an abstract is not straightforward. A few early-age researchers tend to begin by writing it, thinking they are doing it to "tease" the next step (the document itself). However, it is better to treat it as a spoiler.

The simple, concise style of the abstract lends itself to a well-written and well-investigated study. If your research paper doesn't provide definitive results, or the goal of your research is questioned, so will the abstract. Thus, only write your abstract after witnessing your findings and put your findings in the context of a larger scenario.

The process of writing an abstract can be daunting, but with these guidelines, you will succeed. The most efficient method of writing an excellent abstract is to centre the primary points of your abstract, including the research question and goals methods, as well as key results.

Interested in learning more about dedicated research solutions? Go to the SciSpace product page to find out how our suite of products can help you simplify your research workflows so you can focus on advancing science.

The best-in-class solution is equipped with features such as literature search and discovery, profile management, research writing and formatting, and so much more.

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FOCUSED REVIEW article

Embodied cognition, abstract concepts, and the benefits of new technology for implicit body manipulation.

This article mentions parts of:

How Body Balance Influences Political Party Evaluations: A Wii Balance Board Study

• Read original article

• 1 Department of Psychology, Erasmus University Rotterdam, Rotterdam, Netherlands
• 2 Department of Psychology, Open University, Heerlen, Netherlands
• 3 Department of Child and Adolescent Psychiatry, VU University Medical Center, Amsterdam, Netherlands

Current approaches on cognition hold that concrete concepts are grounded in concrete experiences. There is no consensus, however, as to whether this is equally true for abstract concepts. In this review we discuss how the body might be involved in understanding abstract concepts through metaphor activation. Substantial research has been conducted on the activation of common orientational metaphors with bodily manipulations, such as “power is up” and “more is up” representations. We will focus on the political metaphor that has a more complex association between the concept and the concrete domain. However, the outcomes of studies on this political metaphor have not always been consistent, possibly because the experimental manipulation was not implicit enough. The inclusion of new technological devices in this area of research, such as the Wii Balance Board, seems promising in order to assess the groundedness of abstract conceptual spatial metaphors in an implicit manner. This may aid further research to effectively demonstrate the interrelatedness between the body and more abstract representations.

Introduction

Imagine you are reading a story in which someone turns up the volume on his car radio while in real life you are closing the top of a soda bottle. Would these two things (reading about an action and performing a very similar action) influence each other? Research suggests they would. When you are closing the bottle, you are likely to read the words “turn up” faster than if you were opening the bottle ( Zwaan and Taylor, 2006 ). It appears that a rotation of your hand that is congruent with an implied rotation in a sentence facilitates the speed with which relevant parts of the sentence are being processed.

This happens because readers make an elaborate mental representation of what they read that is similar to their experience in real life. How close is this connection between actions on one hand and cognitive processes, such as reading, on the other? Does it only apply to identical actual and implied movements, or does the connection extend beyond these mappings, for example to abstract concepts that do not imply movement at all? According to the embodied cognition approaches such connections also exist.

KEY CONCEPT 1. Abstract concepts Abstract concepts refer to entities that have no physical or spatial constraints because they have no direct representation in the physical world. It does not exist at a particular time or place but as a type of thing. Examples of abstract concepts are emotions, metaphors, and abstract actions (e.g., thinking).

KEY CONCEPT 2. Embodied cognition Cognition is shaped by aspects of the body, like the motor system, the perceptual system and interactions with the environment. Cognitive concepts have specific neural underpinnings meaning that the reconstruction of an earlier experience involves activation of the same brain systems as those during the original experience. This reactivation process is called “sensori-motor simulation.”

Theories on embodied cognition are gaining importance in the field of psychology and beyond ( Pecher et al., 2011 ; Wilson-Mendenhall et al., 2011 ; Dijkstra and Zwaan, 2014 ; Glenberg et al., 2014 ). In contrast to earlier theories on cognition that consider processing and storage of incoming information to take place in an abstract, symbolic manner, embodied cognition theories focus on the body as being central to shaping the mind ( Wilson, 2002 ). Specifically, cognitive processes are presumed to depend on the sensory-motor system in the brain that reactivates earlier experiences, a process called sensory-motor simulation ( Barsalou, 1999 ). When such an experience is retrieved, neural states are re-enacted from the systems that were relevant for the original experience, such as action and perception systems. Cognition is therefore grounded through simulation ( Barsalou et al., 2003 ; Dijkstra and Zwaan, 2014 ).

Based on the available empirical evidence that has accumulated over the past decade or so, this link between actions and representations of concrete concepts has been well established. Recently, more critical points of view have been articulated regarding the specificity of the embodied cognition approach and the boundaries of phenomena that can be explained with this approach. One argument is that embodied cognition research has merely demonstrated that “thoughts and actions go together” but not that the body is essential in carrying out cognitive tasks ( Mahon and Caramazza, 2008 ; Wilson and Golonka, 2013 ). Another argument is that any effects of grounding are taken as positive evidence for embodiment even if they are different or oppose one another ( Willems and Francken, 2012 ). Rather than making general predictions regarding the involvement of sensory-motor systems in cognitive processes that back all findings, the hypotheses should be more specific and the explanation should focus more on underlying mechanisms of embodiment. A third argument concerns support for the claim that similar connections exist between actions and representations of abstract and concrete concepts ( Mahon and Caramazza, 2008 ; Pecher et al., 2011 ; Maglio and Trope, 2012 ). This claim has been challenged because abstract concepts, in contrast to concrete concepts, refer to entities that have no physical or spatial constraints, hence a direct mapping of an abstract concept, such as “democracy,” with a sensory-motor domain is problematic. If abstract concepts without a direct representation in the physical world cannot be physically interacted with, how can they ever be represented through simulation ( Mahon and Caramazza, 2008 )?

KEY CONCEPT 3. Concrete concepts Concrete concepts refer to something that is present in the physical world, such as a tree in a forest. This means that these concepts have physical or spatial constraints. A tree can grow in a forest but not on the moon. Concrete concepts include but are not limited to physical objects in the world. Concrete actions, such as kicking or smiling, are also examples of concrete concepts.

Addressing these arguments with research involving specific hypotheses regarding the role of the body in the way we think and how abstract concepts are grounded is essential in order to be able to “take the next step” in embodied cognition research. The role and impact of the body on cognitive processing have to be specific enough in order to test falsifiable hypotheses. One way to do this, is to specify in each study when and how embodiment occurs ( Willems and Francken, 2012 ). Moreover, it is important to determine the role of the body in cognitive processes in as much detail as possible. This can be done by asking questions, such as: Are sensory-motor processes necessary for cognitive processing, sufficient for cognitive processing, neither necessary nor sufficient or are sensory-motor processes only needed for deep conceptual processing ( Fischer and Zwaan, 2008 )? The answers may differ depending on the task being used, and the frame of mind an individual has on a given point in time ( Maglio and Trope, 2012 ). The groundedness of abstract concepts can be evaluated with empirical evidence from studies that have examined abstract concepts as instantiations of concrete concepts in a situation ( Lakoff and Johnson, 1980 , 1999 ).

Abstract concepts, such as “democracy” are considered to have an indirect basis in the sensory-motor system as representations of situations that are created from different individual experiences, such as “voting in a voting booth” ( Barsalou and Wiemer-Hastings, 2005 ). Thus, abstract concepts can be understood in terms of concrete concepts through metaphorical associations with concrete domains of experiences. Research has provided empirical evidence for this mapping between abstract concepts and concrete experiences. Orientational metaphors provide a spatial orientation for an abstract concept, which can be vertical (down-up), horizontal (left-right) or sagittal (front-back). For some metaphors, there is a physical basis, for example “more is up” because stacking items vertically coincides with a higher quantity of those items. The metaphor “power is up” has a more indirect physical basis, as a result of experiences of statistical regularities that one encounters from infancy onward where power is exerted by someone with greater height (parent-infant, teacher-child).

KEY CONCEPT 4. Orientational metaphors Orientational metaphors are metaphors in which concepts are spatially related to one another. For example, when we speak of feeling “up” or “down,” or when we think of the future being “in front of” and the past “behind” us. Orientational metaphors are exceptionally useful in research because they are plentiful, used in a variety of ways, and easy to represent and manipulate in experiments.

Other mappings between abstract concepts and concrete experiences have been established by bodily manipulations as well. The conceptual metaphors “right is more” and “up is more” were activated by having participants move in a chair along x- and y- axes with higher numbers being generated when moving left-to-right and upwards ( Dehaene et al., 1993 ; Hartmann et al., 2012 ). Other studies examined how orientational metaphors could be combined with the way emotions are represented as “positive is up” ( Crawford et al., 2006 ; Casasanto and Dijkstra, 2010 ). In those cases, the mapping between the abstract concept and concrete domain is even more complex, because it is based on an association of emotional life experiences and vertical motion. Participants who moved marbles upward or downward with their hands activated the metaphor of “positive is up” and “negative is down” by retrieving positive memories when moving upward and negative when moving downward ( Casasanto and Dijkstra, 2010 ). The “positive is up,” “negative is down” representation of the conceptual emotional metaphor has its origin in life experiences, where we cheer when we are happy and sit down with our head down in our hands when we are sad. It is therefore remarkable that this metaphor is still activated when an unrelated movement (depositing marbles upward or downward in a container) is being performed.

KEY CONCEPT 5. Conceptual metaphors Abstract concepts are understood in the context of concrete experiences. For example, the metaphor “Life is a journey” connects the abstract concept of life to experiences during which one went on a journey and knowing that it has an element of time and destination to it. Because of such a concrete experience, the sensory-motor system forms the basis of the representation of the abstract concept.

Another abstract concept for which complex mappings with concrete experiences exist is “time.” Time has been represented along the horizontal axis with the past being associated with left and the future with right ( Santiago et al., 2007 ; Ulrich and Maienborn, 2010 ). Santiago et al. (2007) found that people are faster to categorize words as belonging to the past or the future when words are categorized as the past with a left-hand and future words with a right-hand response. This time metaphor can be modulated by cultural differences in language representations. For example, speakers of Mandarin use vertical terms to talk about time and therefore responded more quickly to vertical representations of time compared to speakers of English who think about time in horizontal spatial terms ( Boroditsky, 2001 ). Abstract concepts may not always be directly grounded through interactions with the world but have their basis in instantiations of concrete experiences and co-occurrences with a certain representation. These mappings between abstract concepts and their more concrete representations are dynamic and can not only be learned over time but also change when co-occurrences change.

This discussion of research that demonstrates how abstract concepts are grounded in action and perception provides insight into the what and when conditions under which grounding occurs. All studies conducted from an embodied cognition perspective, demonstrated an effect of the body (whole body or hand movement) on task performance with the response following the body manipulation, not the other way around. The question remains, however, if there are boundaries with regard to the groundedness of abstract concepts. It is feasible that the sensory-motor system becomes less involved with a higher abstractness of a concept ( Clark, 1999 ). We will address this issue in more detail by reviewing several studies on a particularly abstract conceptual metaphor that may pose a challenge to demonstrate effects of embodiment with: the spatial political metaphor.

Spatial Orientation, Body Manipulation, and Political Metaphors

The representation of politics along a horizontal axis originates from the way the French Legislative Assembly (established in 1791) was spatially organized in the assembly room with conservatives situated on the right and liberals on the left. This spatial organization has resulted in the construction of the abstract political concept of the right equaling the conservative end of the spectrum and the left as the liberal or progressive end. The abstractness of this metaphor may differ depending on the country it is being used in. In the United States, political debates are broadcasted with liberals on the viewer's left and conservatives on the viewer's right. In the Netherlands, on the other hand, the political left/right distinction is represented as a continuum of several parties, suggesting a more subtle spatial array of left to right. Therefore, even as the actual left-right seating arrangements have largely been abandoned the metaphor for political left and right remains in most western countries ( Goodsell, 1988 ). Can this abstract political concept with such an obscure experiential basis still be activated with a manipulation of the body?

Several studies have examined the possibility of this activation in several countries that vary in the way in which political parties are represented ( Oppenheimer and Trail, 2010 ; Van Elk et al., 2010 ; Dijkstra et al., 2012 ; Farias et al., 2013 ). Oppenheimer and Trail (2010) demonstrated the activation of the political metaphor in three experiments with different body manipulations (squeezing a hand-grip with the right or left hand, sitting on a chair tilted to the left or right, and clicking on visual targets on the left or right side of a screen). A manipulation to the left resulted in a higher agreement with Democrats on political issues but a manipulation to the right did not result in higher agreement with Republicans. Van Elk et al. (2010) found support for the groundedness of political metaphors in the Netherlands, where not two, but ten political parties are represented in the political landscape, indicating a true continuum of left to central, and from central to rightwing parties. Participants were manipulated to use their left or right hand to respond to acronyms of political parties and Dutch broadcasting companies, or to respond with the same hand and to stimuli presented on the left or right side of the monitor. Overall, the authors found that participants were faster to respond with their hand that was congruent to the political affiliation of a shown acronym of a political party than with their incongruent hand. However, the effects varied across experiments, sometimes demonstrating congruency effects only for rightwing and sometimes only for leftwing parties. In other words, the association between spatial orientation and politics affected online judgments of political acronyms but the effects were not consistent across experiments. In a third study, researchers demonstrated that the political conceptualization of left to right is also apparent when tested with an auditory measure ( Farias et al., 2013 ). Participants judged conservative words to be louder to the right ear than to the left ear and socialist words to be louder to the left ear than to the right ear.

KEY CONCEPT 6. Political metaphors “Politics” is an abstract concept that can be understood in the context of more concrete experiences. For example, the terms “left” and “right” are used in politics as equivalent of liberalism (or progressiveness) and conservatism respectively. This representation of politics on a horizontal axis originates from the way the French Legislative Assembly was spatially organized in the assembly room.

These studies all demonstrated an association between the abstract concept of the political right and left and the concrete concept of spatial right and left. Although the studies support the idea that the relationship between abstract concepts and concrete domains is integrated in multiple modalities, a problematic element in several of the experiments was that the congruency effects were not consistent for both leftwing and rightwing affiliations. Could this be indicative of a boundary limitation of the groundedness of abstract concepts? Not necessarily. Effects were demonstrated, even if they were asymmetrical. Also, an alternative explanation of these findings could be that participants were aware of the manipulation, which could have affected their response. The use of the hands, ears, and visual fields could have been obvious to them as left/right manipulations and thus have given away what the experimenters were after. Another possibility is that political metaphors are more complex in countries without a political dichotomy, such as the Netherlands, because the political landscape consists of multiple parties that form a left-to-right continuum, rather than a left and a right pole. The political metaphor may still be activated then but constitute a more complex mapping with parties in the continuum and therefore yield more inconsistent results.

Given these problematic aspects of the studies discussed above, a better, more subtle way to manipulate the body, is needed. A more implicit manipulation of the body may yield more consistent results and hide the true purpose of the task at the same time. A particularly effective way to do that is to make use of devices that enable such implicit manipulations. A promising line of investigation in this respect is research that uses new technology to measure changes in the body in a surreptitious manner. This technology can overcome some of the problems associated with the earlier studies on political metaphors and provide an effective tool to both manipulate and assess the activation of conceptual metaphors.

The Wii Balance Board as a Tool to Implicitly Manipulate the Body

New technologies used for leisure time activities at home, such as the LEAP-motion (i.e., an infrared device that detects and reacts upon hand movements; Weichert et al., 2013 ), the Wiimote (i.e., a wireless input device that uses Bluetooth technology), and the Wii Balance Board (WBB), have become increasingly popular among researchers to empirically assess behavior in the lab. Their popularity is based on the facts that most people are already familiar with them, they are relatively inexpensive, and provide precise and useful data as outcome measures.

Of these new technologies, the WBB has been used the most in research so far (see Figure 1 ). The board is 20.1 inches wide, 12.4 inches long, and weighs approximately eight pounds. The four transducers, one in each corner of the board, are used to assess weight distribution and to detect even very small changes in the distribution of participants' center of pressure (COP). Data are sampled at a rate of 33 Hz and the WBB connects to a PC via Bluetooth. The measurements of participants' COP produced by the WBB are as reliable and valid as those produced by other platforms commonly used to assess posture ( Clark et al., 2010 ). In contrast to these platforms, the WBB is far less expensive, and portable, and therefore an attractive alternative for researchers interested in measuring posture. The WBB data contain the COPs of every sample point in the form of X (right-left) and Y (front-back) coordinates. Positive X and Y coordinates indicate that the COP is more to the right and front than the middle of the WBB, while negative X- and Y-values indicate that the COP is more to the left and back. Depending on what a researcher wants to measure, these coordinates can be used to calculate a dependent measure in a program such as Excel or SPSS. For example, if one wants to know whether participants lean to a side, one can look at the corresponding X or Y coordinates and compare COPs across experimental conditions. Another possibility is to see whether people move more in a certain condition (stability of or sway in posture) by measuring the shifts in direction of change in COPs along one of the axes (X-axis for left-right, Y-axis for front-back).

Figure 1. Wii Balance Board set-up .

There are several ways in which the WBB has been utilized in behavioral research. These studies vary in the population being tested, from healthy younger adults (see discussion below) to healthy older adults ( Koslucher et al., 2012 ), individuals with Autism Spectrum Disorder ( Travers et al., 2013 ), and stroke victims ( Nijboer et al., 2014 ). They also vary in what is being measured, such as postural stability, sway, or the influence of posture on cognitive processes, such as the activation of conceptual metaphors ( Eerland et al., 2012 ).

Posture has been investigated as a WBB outcome measure in several studies ( Eerland et al., 2011 ; Zwaan et al., 2012 ; Schneider et al., 2013 ). In the study by Eerland et al. (2011) , participants moved sideways in reaction to an arrow that appeared on the screen. Additionally, the researchers examined whether people leaned more forward in reaction to pleasant pictures (approach behavior) and more backward in reaction to unpleasant pictures (avoidance behavior). This turned out to be the case. In another study ( Zwaan et al., 2012 ), participants moved sideways to indicate whether the sentence they just read was sensible or not. Some action sentences implied a forward-leaning body posture (e.g., The man petted the little dog), while other sentences implied a backward-leaning body posture (e.g., The boy looked up at the clock tower). The hypothesis that posture would be influenced by the described actions as assessed with the WBB was supported. More recently, the WBB was used in a study to assess the influence of ambivalence on body movements ( Schneider et al., 2013 ) based on the idea that when people have ambivalent feelings, they hold positive as well as negative evaluations of an object or issue. Indeed, participants were found to engage in side-to-side movements when experiencing ambivalence.

These results suggest that the WBB is a useful instrument as an outcome measure that also provides very precise data for analysis. Basically, any deviation from the COP is recorded and can be included in the data set, no matter how subtle this pressure shift is. Moreover, the WBB can be used to manipulate posture. This means that it is possible to trick people into believing that they hold a certain position while they are actually not. This is accomplished by having participants think that they are standing upright while in fact they are standing slightly tilted to the right or the left. This manipulation is so subtle that participants are not aware at all that they are standing sideways instead of upright.

The first study to demonstrate such an implicit activation of abstract concepts, built on the mental number line theory ( Restle, 1970 ) as an example that people mentally represent numbers along a line, with small numbers on the left and large numbers on the right. The idea behind the study was that having people lean slightly to the left or the right would activate this mental number line. Indeed, support was found for the idea that surreptitiously leaning to the left activated relatively smaller numbers than surreptitiously leaning to the right ( Eerland et al., 2011 ).

The second study examined the effect of an implicit body posture manipulation with the WBB, in a similar way as in the previous study but then with the political affiliation metaphor along a horizontal axis ( Dijkstra et al., 2012 ). Specifically, the subtle body manipulation on the WBB was examined to assess its effects on political party evaluations. This was done in a Dutch political party environment of 10 parties that can be placed on a left-right continuum. Just as in the Eerland et al. (2011) study, participants thought they stood upright on a WBB while in fact they stood slightly tilted to the left or the right. They were then asked to ascribe general political statements (that could not directly be attributed to one of the political parties in the Dutch House of Representatives) to one political party. The manipulated body position was expected to affect one's political attribution such that standing somewhat to the right would result in attribution of the statement to a political party on the right and an attribution to a party on the left when leaning to the left. The results (see Figure 2 ) indicated that there was an interaction of leaning direction with party attribution as expected, such that there was a congruence effect of leaning direction with left-wing party attribution.

Figure 2. Ascribed statements to left- and right-wing parties by leaning position .

This study is a good demonstration of how an abstract metaphor that has a complex mapping with concrete associations can be activated effectively with a sensori-motor manipulation of the body. The use of the Wii Balance Board facilitated the use of the whole body, rather than parts of the body, as was the case in earlier studies on political metaphors. Moreover, the manipulation was implicit because participants were under the impression that they were standing in the middle of the balance board even though they were not. They were not aware of the fact that their body posture could and would affect their evaluations and most likely did not consciously perceive proprioceptive feedback that they were leaning to the left or the right. Instead, they relied on the visual feedback from the screen. None of the participants noticed the manipulation. Since they were under the impression that they had to maintain their balance in the middle of the board. Such a manipulation makes the WBB an appropriate device for research on the implicit activation of concepts.

Another important component of the study was that the political statements were general and could be attributed to either conservative, liberal, or progressive parties. The attribution was affected therefore by the manipulation of the body, not by party-based content of the statements. The task was also very different from the ones in the previous studies on the political metaphor. In the Oppenheimer and Trail study ( 2010 ), the tasks involved a rating of the level of agreement with the leftwing or rightwing political party whereas the tasks in the experiments of Van Elk et al. (2010) involved response times for which the answers were either correct or incorrect. In contrast, the studies conducted by Farias et al. (2013) and Dijkstra et al. (2012) had more subtle task demands for which the answer clearly depended on the manipulation of the body and no answer could be correct. Moreover, pretests were conducted to create a reliable set of stimuli, socialist-referent words and conservative-referent words in the Farias et al. (2013) study, and a set of statements that were equally likely to be attributed to left-wing or right-wing parties in the Dijkstra et al. (2012) study. Filler questions regarding television programs were added in the latter study to steer the focus away from an exclusive political theme.

We can conclude that the Nintendo Wii-Balance Board (WBB) seems to be a promising tool to investigate left-to-right oriented linguistic metaphors, such as the political metaphor. A main advantage of the WBB is that people can lean one way or the other without even noticing it. This provides the opportunity to investigate other left-to-right metaphors such as emotional valence, the mental number line, or time, while participants are not aware of the fact that their posture is being manipulated. It is entirely possible that given neutral prompts, people might report more positive memories or judge pictures to be more positive when leaning to the right than when leaning to the left. It is conceivable, that these effects are bidirectional. Previous research has demonstrated changes in body posture when participants were primed with concepts, such as “pride” and “shame” ( Oosterwijk et al., 2009 ). Similarly, for the political metaphor, bidirectionality could be demonstrated if participants would lean to the right when being primed with statements reflecting right-wing political issues and to the left when prompted with statements reflecting left-wing issues.

The two studies on conceptual metaphors manipulating posture with the WBB not only provide evidence that this device can be used effectively to manipulate people's judgments, it also supports the idea that even abstract conceptual metaphors are activated when manipulating body position. Apparently, we understand abstract concepts both through concrete experiences and learned associations even if the experiential basis is limited and the mapping is complex. Subtle manipulations of the body without participants being aware of it may work for other conceptual metaphors as well that have no or a limited experiential basis.

Given the promising possibilities of the WBB to manipulate posture, further empirical research could address the issue of learning. If leaning to a side can influence judgments about left/right-statements because the metaphor is grounded ( Dijkstra et al., 2012 ), then it may also work in another direction. Perhaps learning ambiguous material can be directed a certain way by manipulating posture in a congruent direction. Leaning to the left could, for example, facilitate learning which political parties are left-wing parties. Research is needed to investigate this influence of posture on learning, because it might very well be that the subtle manipulation is too implicit to promote explicit learning. It is also interesting that the effect of posture manipulation was only found for the actual left and right positions of parties, and not for what people thought were left and right parties ( Dijkstra et al., 2012 ). The most logical explanation for this finding is that people had difficulties in reporting the position of the party on a complex grid (see Dijkstra et al., 2012 ). However, it could also mean that this is an issue that still has to be investigated. If it is important for the influence of posture what people do or do not explicitly know about a subject, it will probably have implications for studies using this manipulation to examine learning outcomes.

Conclusions

One of the main contributions of research on the groundedness of abstract concepts using new technologies such as the WBB, is that body manipulations can be implemented in a subtle manner that do not alert participants as to what is happening or is supposed to happen. The WBB provides very precise measurements of participants' center of pressure. This can be kept constant during a manipulation by having participants look at their body as a mark on the screen which helps them to keep themselves in the required location. This affords very specific and credible feedback to the participant that their body position is where it should be and how to remain in this position even though in fact their body is tilted to the right or the left. Inclusion of these technologies in future research may be valuable because the activation of other complex metaphors could be assessed this way, both as a tool to collect posture data (along the x- and y-axes), and as a tool to manipulate posture in different ways (along the sagittal axis or by creating imbalance).

We do not claim that these abstract concepts are grounded in the sense that motor activation is necessary for understanding the concept, but research with the WBB does show that it is more than just co-activation. When encountering an ambiguous situation, one uses all available resources to resolve the ambiguity. For instance, when reading an ambiguous political sentence (i.e., it is not clear to which party the statement belongs), people might use the unconscious proprioceptive feedback of their body when evaluating the statement and attributing it to a political party. The body clearly plays a role here, moving beyond the enrichment of those concepts by facilitating a choice within the relational context in which conceptual processing takes place.

How far do these effects go? According to Maglio and Trope (2012) , there are boundaries to such effects of embodiment. They stipulate that these effects only occur when a certain frame of mind is created in the participant. Participants who were manipulated to think at a higher, abstract level were less responsive to contextual bodily cues than when they were encouraged to think in a more concrete manner. The abstract thinking manipulation thus prevented the contextual proprioceptive feedback from affecting judgments. The issue we encountered in the discussion of research on political metaphors, however, was that the effects are not always consistent. The participant's frame of mind (abstract vs. concrete) did not seem to be the issue here. It was rather the effectiveness of the manipulation and possible awareness among participants that may have influenced some of the outcomes.

In our view, future research should therefore focus more on examining in detail how and when this activation takes place ( Willems and Francken, 2012 ), and for which tasks specifically. Possibly, effects are stronger when the whole body shifts to the left or the right instead of parts of the body. Future research should also investigate if these patterns replicate for similar manipulations but different concepts, or similar concepts but different tasks. The next step would be to do this for other metaphors with complex mappings. The outcomes of these studies should not be merely an addition to the current pile of evidence, but can instead bring us closer to a deeper insight into the mechanisms underlying the grounding of abstract concepts. Sensory-motor activation is as applicable to abstract concepts as to concrete concepts, particularly for tasks that involve a certain level of ambiguity. Sensory-motor processes do not seem to “tweak” the results of cognitive processing but are part of the decision process that leads to a response. So far, sensory-motor grounding has been reliably demonstrated for abstract concepts. Further research could reach the limitations of embodiment or support the view that sensory-motor representations are necessary and/or sufficient for cognitive processing. Either way, it will narrow down what the role of the body in conceptual processing is.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors wish to thank Diane Pecher for her feedback on an earlier version of this paper. More specific information regarding the conversion of data from the Wii Balance Board to an Excel file format can be obtained by contacting the authors.

Author Biography

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Keywords: embodied cognition, body manipulation, conceptual metaphors, abstract concepts, wii balance board

Citation: Dijkstra K, Eerland A, Zijlmans J and Post LS (2014) Embodied cognition, abstract concepts, and the benefits of new technology for implicit body manipulation. Front. Psychol . 5 :757. doi: 10.3389/fpsyg.2014.00757

Received: 10 April 2014; Accepted: 27 June 2014; Published online: 19 August 2014.

Reviewed by:

Copyright © 2014 Dijkstra, Eerland, Zijlmans and Post. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

An abstract is a short summary of your (published or unpublished) research paper, usually about a paragraph (c. 6-7 sentences, 150-250 words) long. A well-written abstract serves multiple purposes:

• an abstract lets readers get the gist or essence of your paper or article quickly, in order to decide whether to read the full paper;
• an abstract prepares readers to follow the detailed information, analyses, and arguments in your full paper;
• and, later, an abstract helps readers remember key points from your paper.

It’s also worth remembering that search engines and bibliographic databases use abstracts, as well as the title, to identify key terms for indexing your published paper. So what you include in your abstract and in your title are crucial for helping other researchers find your paper or article.

If you are writing an abstract for a course paper, your professor may give you specific guidelines for what to include and how to organize your abstract. Similarly, academic journals often have specific requirements for abstracts. So in addition to following the advice on this page, you should be sure to look for and follow any guidelines from the course or journal you’re writing for.

The Contents of an Abstract

Abstracts contain most of the following kinds of information in brief form. The body of your paper will, of course, develop and explain these ideas much more fully. As you will see in the samples below, the proportion of your abstract that you devote to each kind of information—and the sequence of that information—will vary, depending on the nature and genre of the paper that you are summarizing in your abstract. And in some cases, some of this information is implied, rather than stated explicitly. The Publication Manual of the American Psychological Association , which is widely used in the social sciences, gives specific guidelines for what to include in the abstract for different kinds of papers—for empirical studies, literature reviews or meta-analyses, theoretical papers, methodological papers, and case studies.

Here are the typical kinds of information found in most abstracts:

• the context or background information for your research; the general topic under study; the specific topic of your research
• the central questions or statement of the problem your research addresses
• what’s already known about this question, what previous research has done or shown
• the main reason(s) , the exigency, the rationale , the goals for your research—Why is it important to address these questions? Are you, for example, examining a new topic? Why is that topic worth examining? Are you filling a gap in previous research? Applying new methods to take a fresh look at existing ideas or data? Resolving a dispute within the literature in your field? . . .
• your research and/or analytical methods
• your main findings , results , or arguments
• the significance or implications of your findings or arguments.

Your abstract should be intelligible on its own, without a reader’s having to read your entire paper. And in an abstract, you usually do not cite references—most of your abstract will describe what you have studied in your research and what you have found and what you argue in your paper. In the body of your paper, you will cite the specific literature that informs your research.

When to Write Your Abstract

Although you might be tempted to write your abstract first because it will appear as the very first part of your paper, it’s a good idea to wait to write your abstract until after you’ve drafted your full paper, so that you know what you’re summarizing.

What follows are some sample abstracts in published papers or articles, all written by faculty at UW-Madison who come from a variety of disciplines. We have annotated these samples to help you see the work that these authors are doing within their abstracts.

Choosing Verb Tenses within Your Abstract

The social science sample (Sample 1) below uses the present tense to describe general facts and interpretations that have been and are currently true, including the prevailing explanation for the social phenomenon under study. That abstract also uses the present tense to describe the methods, the findings, the arguments, and the implications of the findings from their new research study. The authors use the past tense to describe previous research.

The humanities sample (Sample 2) below uses the past tense to describe completed events in the past (the texts created in the pulp fiction industry in the 1970s and 80s) and uses the present tense to describe what is happening in those texts, to explain the significance or meaning of those texts, and to describe the arguments presented in the article.

The science samples (Samples 3 and 4) below use the past tense to describe what previous research studies have done and the research the authors have conducted, the methods they have followed, and what they have found. In their rationale or justification for their research (what remains to be done), they use the present tense. They also use the present tense to introduce their study (in Sample 3, “Here we report . . .”) and to explain the significance of their study (In Sample 3, This reprogramming . . . “provides a scalable cell source for. . .”).

Sample Abstract 1

From the social sciences.

Reporting new findings about the reasons for increasing economic homogamy among spouses

Gonalons-Pons, Pilar, and Christine R. Schwartz. “Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage?” Demography , vol. 54, no. 3, 2017, pp. 985-1005.

Sample Abstract 2

From the humanities.

Analyzing underground pulp fiction publications in Tanzania, this article makes an argument about the cultural significance of those publications

Emily Callaci. “Street Textuality: Socialism, Masculinity, and Urban Belonging in Tanzania’s Pulp Fiction Publishing Industry, 1975-1985.” Comparative Studies in Society and History , vol. 59, no. 1, 2017, pp. 183-210.

Sample Abstract/Summary 3

From the sciences.

Reporting a new method for reprogramming adult mouse fibroblasts into induced cardiac progenitor cells

Lalit, Pratik A., Max R. Salick, Daryl O. Nelson, Jayne M. Squirrell, Christina M. Shafer, Neel G. Patel, Imaan Saeed, Eric G. Schmuck, Yogananda S. Markandeya, Rachel Wong, Martin R. Lea, Kevin W. Eliceiri, Timothy A. Hacker, Wendy C. Crone, Michael Kyba, Daniel J. Garry, Ron Stewart, James A. Thomson, Karen M. Downs, Gary E. Lyons, and Timothy J. Kamp. “Lineage Reprogramming of Fibroblasts into Proliferative Induced Cardiac Progenitor Cells by Defined Factors.” Cell Stem Cell , vol. 18, 2016, pp. 354-367.

Sample Abstract 4, a Structured Abstract

Reporting results about the effectiveness of antibiotic therapy in managing acute bacterial sinusitis, from a rigorously controlled study

Note: This journal requires authors to organize their abstract into four specific sections, with strict word limits. Because the headings for this structured abstract are self-explanatory, we have chosen not to add annotations to this sample abstract.

Wald, Ellen R., David Nash, and Jens Eickhoff. “Effectiveness of Amoxicillin/Clavulanate Potassium in the Treatment of Acute Bacterial Sinusitis in Children.” Pediatrics , vol. 124, no. 1, 2009, pp. 9-15.

“OBJECTIVE: The role of antibiotic therapy in managing acute bacterial sinusitis (ABS) in children is controversial. The purpose of this study was to determine the effectiveness of high-dose amoxicillin/potassium clavulanate in the treatment of children diagnosed with ABS.

METHODS : This was a randomized, double-blind, placebo-controlled study. Children 1 to 10 years of age with a clinical presentation compatible with ABS were eligible for participation. Patients were stratified according to age (<6 or ≥6 years) and clinical severity and randomly assigned to receive either amoxicillin (90 mg/kg) with potassium clavulanate (6.4 mg/kg) or placebo. A symptom survey was performed on days 0, 1, 2, 3, 5, 7, 10, 20, and 30. Patients were examined on day 14. Children’s conditions were rated as cured, improved, or failed according to scoring rules.

RESULTS: Two thousand one hundred thirty-five children with respiratory complaints were screened for enrollment; 139 (6.5%) had ABS. Fifty-eight patients were enrolled, and 56 were randomly assigned. The mean age was 6630 months. Fifty (89%) patients presented with persistent symptoms, and 6 (11%) presented with nonpersistent symptoms. In 24 (43%) children, the illness was classified as mild, whereas in the remaining 32 (57%) children it was severe. Of the 28 children who received the antibiotic, 14 (50%) were cured, 4 (14%) were improved, 4(14%) experienced treatment failure, and 6 (21%) withdrew. Of the 28children who received placebo, 4 (14%) were cured, 5 (18%) improved, and 19 (68%) experienced treatment failure. Children receiving the antibiotic were more likely to be cured (50% vs 14%) and less likely to have treatment failure (14% vs 68%) than children receiving the placebo.

CONCLUSIONS : ABS is a common complication of viral upper respiratory infections. Amoxicillin/potassium clavulanate results in significantly more cures and fewer failures than placebo, according to parental report of time to resolution.” (9)

Some Excellent Advice about Writing Abstracts for Basic Science Research Papers, by Professor Adriano Aguzzi from the Institute of Neuropathology at the University of Zurich:

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Home » Concept – Definition, Types and Examples

Concept – Definition, Types and Examples

Table of Contents

Definition:

Concept is a mental representation or an abstract idea that we use to understand and organize the world around us. It is a general notion that summarizes and simplifies complex information or experiences, making it easier to communicate and process.

For example, the concept of “love” is an abstract idea that represents a range of emotions and behaviors that people experience in their relationships with others. Similarly, the concept of “justice” represents a set of principles and standards that guide our sense of fairness and equality.

Types of Concept

Types of Concepts are as follows:

Concrete Concepts

These are concepts that refer to tangible objects or physical entities that can be perceived through the senses, such as a table, a car, or a flower.

Abstract Concepts

These are concepts that refer to ideas, qualities, or attributes that cannot be perceived through the senses, such as freedom, justice, or happiness.

Formal Concepts

These are concepts that are defined by specific rules or criteria, such as mathematical concepts like a triangle or a circle.

Natural Concepts

These are concepts that are based on our experience and interactions with the world, such as concepts like water, food, or family.

Social Concepts

These are concepts that are based on cultural or social norms, such as concepts like marriage, friendship, or etiquette.

Prototype Concepts

These are concepts that are based on typical or idealized examples of a category, such as a prototype concept of a bird that includes features like wings, feathers, and the ability to fly.

Exemplar Concepts

These are concepts that are based on specific examples or instances of a category, rather than on an idealized prototype.

Examples of Concept

Here are some examples of concepts:

• Love – a feeling of strong attachment or deep affection towards someone or something.
• Democracy – a system of government in which power is vested in the people and exercised through free and fair elections.
• Justice – the quality of being fair and impartial, particularly in the administration of the law.
• Equality – the state of being equal in status, rights, and opportunities.
• Freedom – the state of being free from coercion, constraint, or oppression.
• Creativity – the ability to produce original and imaginative ideas, works, or solutions.
• Sustainability – the ability to maintain ecological balance and meet the needs of the present generation without compromising the ability of future generations to meet their own needs.
• Globalization – the process of integration and interdependence among people, companies, and governments across the world.
• Diversity – the range of different cultures, ethnicities, genders, and other characteristics that exist within a group or society.
• Leadership – the ability to inspire and guide others towards a common goal or vision.

Applications of Concept

Applications of Concept are as follows:

• Education : Concepts play a crucial role in education, where they are used to help students develop a deeper understanding of various subjects. For example, in mathematics, concepts such as fractions, decimals, and geometric shapes are used to solve problems.
• Science : Concepts are used extensively in scientific research to help scientists understand and explain the natural world. For instance, concepts such as energy, matter, and gravity are used to describe and explain various phenomena.
• Business : Concepts such as marketing, branding, and customer service are essential for businesses to succeed. These concepts help businesses develop effective strategies to reach their target audience and improve customer satisfaction.
• Technology : Concepts are the foundation of many technological innovations. For example, the concept of artificial intelligence is used to develop intelligent machines that can perform tasks that would otherwise require human intervention.
• Philosophy : Concepts are a key aspect of philosophical inquiry, where they are used to analyze and understand complex ideas and arguments. For instance, concepts such as justice, ethics, and morality are used to explore ethical dilemmas and the nature of right and wrong.

Purpose of Concept

The purpose of a concept is to provide a mental framework or idea that helps us understand a particular topic or phenomenon. Concepts can range from simple ideas like “honesty” or “loyalty” to more complex ideas like “democracy” or “social justice.”

Concepts allow us to classify, organize, and analyze information, making it easier to understand and communicate. They also help us identify patterns, similarities, and differences between different ideas or things.

Concepts are essential for learning and intellectual development, as they provide a foundation for more advanced understanding and learning. They also allow us to build upon existing knowledge and make connections between different fields or areas of study.

Characteristics of Concept

There are several characteristics of a concept, including:

• Abstractness: A concept is an abstract idea that represents a class of objects, events, or phenomena. It is a mental construct that does not have a physical existence.
• Generalization : A concept represents a general idea that applies to a broad range of situations, objects, or events. It helps to identify commonalities among various things or phenomena.
• Mental Representation : A concept is a mental representation of an idea that we use to understand the world around us.
• Clarity : A concept should be clearly defined and understandable, so that others can comprehend it.
• Universality : A concept is universal and can be applied across different domains or contexts.
• Coherence : A concept should be logically consistent and coherent, so that it can be used to make sense of information and solve problems.
• Relevance : A concept should be relevant to the context in which it is used, and should have practical applications.
• Flexibility : A concept should be flexible enough to accommodate changes in our understanding of the world, and to adapt to new situations and contexts.
• Abstraction : A concept is an abstraction, meaning that it represents a simplified version of reality that is easier to understand and manipulate.

Advantage of Concept

Here are some advantages of concepts:

• Efficient Communication: Concepts provide a way to communicate efficiently by encapsulating complex ideas into simple, easily understandable units. For example, the concept of “love” represents a broad range of emotional experiences and allows us to communicate about this complex subject more easily.
• Problem-Solving: Concepts help us to solve problems by allowing us to identify patterns and similarities between different situations. This enables us to apply solutions that have worked in similar situations to new problems.
• Learning : Concepts provide a way to organize and structure new information, making it easier to learn and remember. By understanding the concept of “gravity,” for example, we can better understand the behavior of objects in the physical world.
• Decision Making: Concepts enable us to make more informed decisions by providing a framework for evaluating options and considering trade-offs. For example, the concept of “opportunity cost” helps us to weigh the benefits and drawbacks of different choices.

Limitations of Concept

Limitations of the Concept are as follows:

• Subjectivity : Concepts are inherently subjective, as they are based on individual experiences, beliefs, and cultural contexts. The meaning and interpretation of a concept may vary from person to person or culture to culture.
• Incompleteness : Concepts are often incomplete, as they represent a simplified version of reality. They may leave out important details or nuances, leading to misunderstandings or misinterpretations.
• Rigidity : Concepts can be rigid and inflexible, as they may not be able to accommodate new information or perspectives. This can lead to resistance to change or an inability to adapt to new situations.
• Overgeneralization : Concepts can also be overgeneralized, as people may apply a concept to situations where it does not apply or make assumptions based on incomplete or inaccurate information.
• Context dependence: The meaning of a concept can depend on the context in which it is used, making it difficult to apply the concept universally. This can lead to confusion or misinterpretation.

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3 Berkeley on Abstract Ideas and Idealism

Martha Brandt Bolton is Professor of Philosophy at Rutgers University. She works on the history of philosophy in the seventeenth through early nineteenth centuries, especially theories of metaphysical dependence, endurance, causality, perception, empirical knowledge, and mental representation. She is the author of articles on Locke, Leibniz, Berkeley, Cockburn, and Shepherd, among others. Her main current interest is Leibniz’s New Essays Concerning Human Understanding.

• Published: 14 February 2022
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According to Berkeley, philosophers maintain that a general name must stand for a single general idea that represents all members of a kind. To do that, they say, an idea needs to abstract the qualities that are shared by all members of a kind and exclude all those in which they differ. This chapter discusses three aspects of Berkeley’s opposition to such abstract ideas: his argument against the possibility of abstract ideas, his account of how a particular idea and name can come to stand for all things in the same kind, and how the attack on abstraction anticipates the design of establishing the idealist theory that sensible things cannot exist unperceived.

Abstract Ideas in the Context of Berkeley’s Philosophy

Berkeley’s opposition to abstract ideas takes up a large part of the Introduction to A Treatise Concerning the Principles of Human Knowledge (PHK), where it is said to “anticipate the design” of the work itself. A partial overview of this preliminary section (hereafter “Introduction”) helps to situate the topic in the Principle s’ broader aims. Abuses of language are the primary concern of the Introduction. It identifies them as the main source of the confusions, difficulties, and contradictions that hinder the advancement of knowledge. Most harmful, on this view, is the principle that a name in a language is significant only if it stands for exactly one thing or idea; otherwise, it has no clear signification (PHKI 18:36). 1 With this in mind, philosophers contend that in order to stand for a kind, an idea must include all qualities in respect of which all things in that kind are alike and exclude all qualities in which they differ. Berkeley contends that such abstract ideas are inconceivable and impossible. The point is important to him in part because it undermines an established theory of names which stands in the way of his linguistic theory of the natural order: 2 in nature, sensible things are not just mind-dependent particulars but also bearers of signification, related to other sensible things as signs are related to what they stand for in such a way as to form a language (see NTV 147:231; PHK 66:69–70; PHK 108:88–89).

Abstract ideas bring into view the traditional problem of universals—the problem of what collects, or unifies and distinguishes, all and only things in the same kind. 3 But its resolution is not Berkeley’s uppermost concern in the Introduction. He has a solution which is in some ways similar to Locke’s. Both of them hold that everything that exists is entirely particular, and that names and ideas which are particular in their being may take on a general signification. That is, they can be made to stand for many things by operations of the human mind. Locke maintains that particular ideas are set up to stand for many things by acts of abstraction. 4 Berkeley maintains that a particular idea is made to signify all things in its kind by a mind’s intending to use it for that purpose. However, why having this intention suffices to give it general signification is not explained in the Introduction. In this text Berkeley’s sights are set on exposing the fallacious source and utter falsity of the theory of abstract ideas, apparently in an effort to smooth the way for idealism.

Three parts of Berkeley’s view of abstract ideas are considered in this chapter: the argument against their possibility, how particular ideas come to signify all things in a kind, and how the attack on abstraction anticipates the design of establishing the idealist theory of sensible things.

Against the Possibility of Abstract Ideas

We learn what ideas are for Berkeley at the outset of the Principles . They are one of two fundamental sorts of beings we are said to know; minds, or spirits, are the other. Ideas are either (i) imprinted on the senses, or (ii) perceived by attending to the passions or operations of the mind, or (iii) formed by memory or imagination which either divide, compound, or merely represent ideas of the other two types (PHK 1:41). Sensible objects are then identified with collections of ideas given names such as books and trees. Ideas of type (i), those imprinted on the senses, are passively received by the mind (see PHK 29:53). They are co-extensive with Aristotle’s proper sensibles: light and color, which come from sight; hard, soft, hot, cold, motion, resistance, and the like, from touch; and so on for taste, smell, and hearing. Turning to ideas of type (ii), what sorts of ideas are perceived by attending to the passions and operations of the mind? It is difficult to be sure. 5 But because mental operations can be known by their effects, it would seem the ideas in view either result from the passive reception of ideas or are caused by mental operations. We might think of them as properties of ideas, being perceived (a perception), being remembered, imagined, or otherwise thought of. But Berkeley’s ideas are not properties of other ideas but parts that can belong to larger collections of ideas. If a mind has an idea of another type, a type (ii) idea comes along with it.

The third type of ideas differs from the other two in several ways. They are actively exerted by means of the imagination; they are secondary ideas- of previously acquired ideas which copy and represent them. Some, but not all, ideas are images, according to Berkeley. Whereas Descartes takes all ideas to be image-like in that they are intrinsically of objects, Berkeley holds this to be true only of ideas of type (iii) (see, e.g., PHK 87:78–79). Ideas imprinted by sense do not refer to anything, or introduce anything to thought. They are the objects of sensory knowledge, “real objects,” nothing less and nothing more. 6 When Berkeley calls them ideas “of” qualities—color, motion, etc.—the word “of” is used to specify what the idea in question is. 7 By contrast, ideas formed by imagination, “being less regular, vivid and constant, are more properly called ideas , or images of things , which they copy and represent” (PHK 33:54).

Berkeley wrote an unpublished introductory essay, given the title Manuscript Introduction by its modern editor Bertil Belfrage, which has to do with abuses of language and the doctrine of abstract ideas. 8 According to this essay, an idea represents a sensible thing in virtue of nothing but resembling it. 9 If an idea can represent a sensible thing only if it includes all the qualities of the thing, the impossibility of abstract ideas is evident. However, this account of ideal representation is not found in PHK or, to my knowledge, other published works of Berkeley. Although they say that an idea represents a thing only if it resembles that thing, they place no condition on the extent of resemblance (see PHK 143:106–107).

Four types of abstract ideas are mentioned in the Principles —two in the Introduction and two in the body of the work. (1) One sort has been dubbed “singling abstract ideas.” They would occur if, for example, a person saw an object that was extended, colored, and moving, and considering each part separately, she formed simple ideas such as color exclusive of extension, motion exclusive of extension, and so on (PHKI 7:27–28). 10 (2) Abstract ideas of kinds of sensible things, which have been called “generalizing ideas,” have the form previously described. For instance, the abstract idea of extension in general “is neither line, nor surface, nor solid, nor has any figure or magnitude, but is … entirely prescinded from all of these” (PHKI 8:28). In addition to general abstract ideas of qualities, there are those that purport to represent a man, an animal, a body, etc., in general.

(3) A third type not mentioned in the Introduction has to do with the existence of a sensible thing: “For can there be a nicer strain of abstraction than to distinguish the existence of sensible objects from their being perceived, so as to conceive them existing unperceived?” (PHK 5:42). To form this conception would be to form an abstract idea: “[I]t [is] perfectly unintelligible and involving all the absurdity of abstraction, to attribute to any single part of [the furniture of the world] an existence independent of a spirit” (PHK 6:43). This is akin to abstract ideas of qualities, but neither being perceived nor existing is a quality. Whereas qualities are proper sensibles, existing and being perceived are more like metaphysical attributes. It is important to try to clarify this notion of abstraction because it is a fulcrum for idealism. For Berkeley, perception is a relation that holds between a mind and ideas. He maintains that minds and relations are entirely different in kind from ideas; a mind can neither be perceived nor represented by an idea (PHK 25:51–52; PHK 27:52–53; PHK 89:79–80; PHK 142:106). So in the description of the illicit abstraction, the phrase “being perceived” cannot express the mind’s perceiving the object but rather only one term of this relation—the object’s mode of being, that is, being perceived. 11 I rank this as a type (ii) idea. Berkeley has it, then, that if a person perceives an object, she perceives not just the qualities it comprises but also their being things that are perceived, and separation of the latter from the former would allededly be an illegitimate abstraction.

(4) In the Principles and elsewhere, there is mention of a fourth type of abstract idea that includes ideas of being in general, entity, existence, and absolute existence. 12 These are abstract ideas that push generalization to the limit. Absolute being would be mere being, the being of nothing in particular. To Berkeley’s mind, it is null: “[T]he general idea of being appeareth to me the most abstract and incomprehensible of all other” (PHK 17:48). If the idea of being in general were conceivable, as it is not according to Berkeley, it would give significance to the names “being in general,” “absolute existence,” “a being,” and the like. As it is, according to Berkeley: “I own indeed, that those who pretend to the faculty of framing abstract general ideas, do talk as if they had such an idea, which is, say they, the most abstract and general notion of all, that is to me the most incomprehensible of all others” (PHK 81:75). In other words, they use a name that signifies nothing, believing their speech has a meaning.

Berkeley maintains that all four types of abstract ideas are inconceivable. Two ways he might argue for this claim come readily to mind. First, he might argue that abstract ideas purport to represent things that are impossible, assume that what is impossible is inconceivable, and conclude that allegedly abstract ideas are inconceivable. 13 Some scholars maintain that this is Berkeley’s only argument against abstract ideas. 14 There are several occasions on which Berkeley argues against the conceivability of a certain idea in this way; for instance, he argues that because the philosophers’ definition of matter, as an inactive substance that causes ideas in minds, contains a contradiction, he can form no idea of it (DHP 216: 232–233-3). This is not, however, an abstract idea. 15 Second, he might contend against the conceivability of abstract ideas because of what they themselves would need to be. This is far and away his most frequent approach. He repeatedly urges that in order to know whether a certain abstract idea is conceivable, we need only attend to our own ideas or inquire into our thoughts.

Before saying more about this, let’s get Berkeley’s use of Locke’s remark about the abstract general idea of a triangle out of the way. Both the remark and Berkeley’s use of it are more rhetorical than substantive. Locke says, “the general idea of a triangle … must be neither oblique nor rectangle, neither equilateral, equicrural, nor scalenon, but all and none of these at once. In effect, it is something imperfect that cannot exist, an idea wherein some parts of several different and inconsistent ideas are put together” (E 4.7.9; as quoted in PHKI 13:32). A literal reading of the parts Berkeley italicized allows him to charge the idea with the fault of being “made up of manifest, staring contradictions” (NTV 125:222). Yet, in the context of the Essay , it is clear enough that Locke wants only to convey that the idea represents all species of triangles regardless of their differences, but in an effort to highlight the skill needed to form the general idea, he uses a figure of speech. 16 In fact, when speaking of this passage elsewhere, Berkeley takes Locke to say that the differences are omitted from, not included in, the idea. 17 It is also clear that the overly inclusive idea does not conform to Berkeley’s profile of generalizing ideas, and is hardly relevant to the conceivability of them.

Most often, Berkeley attacks abstract ideas on the ground that attention to one’s own ideas shows the impossibility of forming them. 18 The reliability of a person’s cognitive access to her present mental states in general is questionable. But Berkeley was familiar with a tradition going back at least to Descartes in which philosophers found reasons to say we can be certain that at least some of our ideas are as we take them to be. 19 Berkeley maintains that qualities imprinted on the senses are present in awareness; perception is a sort of immediate knowledge of them which is enhanced by attention. Attention enables the perceiver to discriminate the various elements perceived together in a thing and consider the possibility of conceiving them apart. He might say the same about ideas a mind deliberately conceives or imagines. He claims not that this sort of knowledge is indubitable or completely unmistakable, but that it has epistemic value. In attending to her ideas, a person “cannot easily be mistaken” and “clearly and adequately” knows them (PHKI 22:39). To those who pursue knowledge, Berkeley’s advice is to endeavor to conduct their thoughts by attending to nothing but bare ideas, keeping words out of mind as much as possible. This provides assurance that they are not misled by words (PHKI 23:39). 20 It accords with this epistemic policy that Berkeley’s attack on abstract ideas consists of two probative processes: one consisting of attention to one’s ideas and another consisting of reasoning expressed in words. The one complements the other. 21

Abstract ideas are attacked by nothing but the experiential procedure in Introduction 10. The focus is on combinations of qualities perceived together in a thing. The conceivability of some without others is tested by imagination. This is as it should be; the proprietary operation of imagination is to form images that copy, represent, divide, and combine objects of perception (PHK 1:41). Berkeley reports that his efforts to conceive singling and generalizing abstractions are in vain, but he succeeds in imagining qualities separately in cases where they are parts of the same thing. It is possible for him to imagine the head of a man without the rest of the body, and the smell of a rose without the petals (PHK 5:43), but his efforts to separate, e.g., a color from an extension are so futile as to establish that it is not possible for them to be separated. In sum, he reports himself able to conceive “some particular parts or qualities” without others with which they are united in some body, provided they can exist apart: “But I deny that I can abstract one from another … those qualities which it is impossible should exist so separated; or that I can frame a general notion by abstracting from particulars in the manner aforesaid” (PHKI 10:30). The general description of what he can, and cannot, separate in thought is an inductive generalization based on the outcome of a certain number of trials.

As Margaret Atherton (1987 , 49–50) explains it, readers are invited to make a thought experiment. Starting from a cluster of same-sense ideas imprinted together, try to frame an idea that includes some and excludes the rest as an abstract idea is supposed to do. Berkeley anticipates that a reader will not just fail, but realize that she is utterly unable to effect it. This is due to no weakness that could be remedied, but to the structure of the assembled qualities. 22 The experience of an unalterable inability to effect the abstraction shows their inseparable metaphysical fusion; the inconceivability and the impossibility of separate existence are known at once. It might be objected that, after all, we need not imagine a determinable extension without some determinant because we can form non-sensory concepts of such things. The reply is in part that the extension in question is precisely sensible (see DHP 93–94).

An experience that shows certain qualities to be inseparable in thought might be explained. Atherton explains one such outcome this way: “If we take away all the ways in which expanse is colored, we have also taken away all the ways in which it can be visually extended, because the way to take up visual space is to be colored. You can’t remove the color and have any visual taking-up-space left” (1987, 50). The explanation adds a way of understanding the experimental result.

The thought experiment does not work in just the same way when it comes to generalizing abstract conceptions. To form such an idea requires conjuring something from nothing. The different colors displayed on a paint chart have no color in common. Bare color is not a color. The relevant thought experiment consists in trying to fabricate it without sensible ideas to work upon. In the case of an abstract indeterminate extension: “Now I do not find that I can perceive, imagine, or any wise frame in my mind such an abstract idea…. A line or surface which is neither black, nor white, nor blue, nor yellow, etc., nor long, nor short, nor rough, nor smooth, nor square, nor round, etc., is perfectly incomprehensible. This I am sure of as to my self: how far the faculties of other men may reach they best can tell” (NTV 123:220). This can be explained by the fact that imagination can produce only ideas it copies from ideas imprinted on the senses.

General Ideas

According to Berkeley, a thing’s being general consists not in its being a universal nature or conception, but in its having a relation to many things it signifies or represents (PHKI 14:33). As he sees it, an idea which is particular in its nature is “rendered universal” by being used to represent all things in the same kind. For instance, to demonstrate a general theorem, a geometer draws a particular triangle, isosceles as it may be, and uses it to stand for all triangles. It is not a matter of abstracting and excluding: “But only that the particular triangle I consider, whether of this or that sort it matters not, doth equally stand for and represent all rectilinear triangles whatsoever, and is in that sense universal ” (PHKI 15:34). It seems the mathematician chooses what it is about the particular that is to represent all others by selecting it for attention; it might be triangles, closed figures, red objects, groups that number three, and so on.

Further, Berkeley claims that the general idea of a triangle established in this way supports knowledge of general truths in cases where none of the idea’s specific features is “concerned in the proof,” or “mentioned.” The second edition adds: “And here it must be acknowledged that a man may consider a figure merely as triangular, without attending to the particular qualities of the angles, or relations of the sides. So far he may abstract: but this will never prove, that he can frame an abstract general inconsistent idea of a triangle” (PHKI 16:35). Against this, it is objected that to say someone can conceive a particular triangle as a triangle assumes what Berkeley purports to explain, what it is that collects and unifies things in a kind. 23 A second, perhaps more obvious, objection is that unless a person has the ability to recognize a particular as a member of the kind triangle, she cannot conceive it as such or intend it to stand for all triangles. This ability is assumed, but not explained, in the passage.

The force of these objections depends on exactly what the doctrine of general ideas is intended to explain in the context of the Introduction. Its main aim is to undercut general ideas that are abstract in order to prepare the way for idealism. Still, Berkeley owes an account of how particular things are made to serve as universals, a debt unpaid in the Introduction.

The resources needed to discharge it are developed in the earlier Essay Towards a New Theory of Vision , where it is in the service of the doctrine that vision is a language used by the author of nature (NTV 147:231). For present purposes, the application of the theory to vision need only be mentioned in passing. 24 Berkeley’s theory of linguistic signification consists of two main parts. One is the doctrine of immediate and mediate perception which is underwritten by what we might see as a precursor of the theory of associative learning. The second is his account of the basis of signification, specifically in a language.

Berkeley maintains that a person’s knowledge of what a sign stands for consists largely of a functional relation between cognition of (tokens of) the sign and thoughts of things it signifies. This is the pattern of Berkeley’s concept of immediate and mediate perception: immediate auditory perception of, say, a howl is apt to cause the perceiver to form an idea of an animal in distress. Causal dispositions that fit this model arise from habits picked up in experience, according to Berkeley. In particular, someone who regularly perceives two sorts of things together—or, not to beg the question, mutually similar things together with different mutually similar things—may form a habit by which perceiving one tends to cause her imagination to form an idea of the other. In Berkeley’s terminology, the one “suggests” the other.

According to this theory, having causal dispositions that follow this pattern enables one to comprehend what is signified by expressions in a language. It would not suffice for understanding signs of all sorts. Berkeley maintains that the relation between a sign and what it stands for can be based on any one of four relations: resemblance, geometrical (logical) inference, a strictly exceptionless law of nature, or arbitrary institution (TVV 14:257; also Alc 4,12:157–158). But, he argues, signs in a language are not based on the first three, but established by the fourth. “Arbitrary” is best understood not as having no reason, but as not being based on natures of things. Natural languages used by human beings are sustained by conventions established by those who use them and most often picked up by interacting with those who speak them. In the Principles , the requisite habits are reduced to patterns of ideas that tend to recur in a mind.

Returning to Introduction 15–16, the claim that we can consider a triangle as such confronts the objection that this assumes what it purports to explain. Although the text seems to make a start on this by stating that a particular triangle can be set up to signify all other particulars in the same kind, this is exposed to the obvious objection that doing this presupposes, but does not explain, the ability to recognize a particular as triangular. Berkeley could respond to the second objection that the person in question understands a language in which “triangle” is a name.

Berkeley’s account of signification in a language also responds to the primary objection that he fails to explain what collects and unifies instances of the same kind. What collects things in the same kind on Berkeley’s theory is given by the following biconditional form: particular x and particular y belong to the same kind if and only if were a spirit to perceive x and things similar to it together with y and things similar to it, the spirit would be apt to form a habit manifested by the pattern of immediate and mediate perception that is the core of linguistic comprehension. To explain what unifies things that satisfy this form, we should look to the apparatus of mental passions and operations that issues in these recurrent patterns, but little is said about spirits and their affections in NTV and the Principles . That reductive account may not be a proper theory of universals, but it is offered as a functional equivalent.

Abstract Ideas and Idealism

Judging by the remark about anticipation of design, the attack on abstract ideas makes a start on proving the idealist theory. Some scholars doubt it has the relevance this suggests, but I belong to the group convinced that Berkeley’s assessment is right. 25 In what follows, I argue that the mind-dependence of sensible things is directly inferred from the claim that key abstract ideas are inconceivable in PHK 5, briefly in PHK 6 and 24, and the master argument in PHK 22–23 consists of a similar anti-abstractionist move. 26

Before going on, we should get clear about Berkeley’s notions of impossibility, inconceivability, and unintelligibility. According to him, something is impossible if its notion contains a contradiction or “repugnancy.” In the usage of his time, the word could mean a contradiction, inconsistency, or incompatibility (OED). Conceivability is a matter of a mind’s ability to form a certain conception or thought. Roughly put, something is conceivable just in case it is thinkable. Finally, unintelligibility is a property linguistic expressions have just in case they are taken to have signification but actually signify nothing.

Berkeley holds that if something is impossible, it is inconceivable (DHP 232). It would be open to him to use this as a premise to argue from the impossibility of abstractions to their inconceivability, but it would be circular to infer from this that what the pretended idea supposedly represents is impossible. Now some scholars maintain this is the only way Berkeley attacks abstract ideas. 27 If this were right, the threat of circularity would bar him from inferring the idealist theory from the inconceivability of an abstract idea. However, it is not right. In DHP, Hylas’s suggestion that absolute rather than sensible extension inheres in matter starts an argument. When Hylas allows he is unable to form the idea of an extension which has no secondary quality, Philonous notes that nothing with a definition that “implies a repugnance” can possibly exist, and continues: “Since therefore it is impossible even for the mind to disunite the [idea] of extension … from all other sensible qualities, doth it not follow that where the one exist, there also necessarily the other exist likewise?” (DHP 194). Philonous has said nothing that exposes a logical repugnancy in the general idea. If anything shows it is repugnant, it is Hylas’s professed inability to form the idea and the claim that the mind is unable to disunite the idea of an extension from all other sensible qualities. Turning to singling abstractions, the text makes passing reference to a common view that such things are impossible. But it is unclear how one might prove that its logical inconsistent; to my knowledge, no argument to this effect against abstraction of qualities can be found in the philosophical writings of Berkeley. 28 This clarifies two points. The repugnancy of both types of abstract ideas of qualities consists in nothing but their being inconceivable, or unthinkable; no logical inconsistency is ever revealed. And the necessity of an extension’s existing together with a secondary quality is known by attending to the futility of the mind’s effort to pry the ideas apart.

I want to suggest there is a reason for Berkeley to use this mode of proof to attack abstract ideas: the impossibility of illegitimate abstractions is inherent in the necessities and possibilities specific to the constitution of sense experience. For Berkeley, knowledge of things we perceive is a sort of acquaintance which is prior to propositional means of expressing it; sentences in a language can only describe, or report, it more or less well. 29 A verbal argument that two elements of an experience cannot exist apart stands to be validated, or not, by reacquaintance with experience. In other words, the relations among ideas that give experience its distinctive morphology are best and first known by analyzing experience itself, noting which elements found together can, and cannot, be decomposed by imagination.

In an article that argues against the relevance of the attack on abstract ideas to Berkeley’s argument for idealism, Samuel Rickless focuses on PHK 4 and 5. 30 Although this is part of a more extensive argument, Rickless claims that on the best interpretation, they prove his point. The fourth section records the prevailing opinion that books and mountains exist in nature without a mind and produces the following argument to prove it cannot possibly be true: mountains are things we perceive by sense; the things we perceive by sense are ideas; it is not possible that ideas exist unperceived; so it is not possible that mountains exist without a mind (PHK 4:42). Section 5 opens with the following two sentences: “If we thoroughly examine this tenet, it will, perhaps, be found at bottom to depend on the doctrine of abstract ideas. For can there be a nicer strain of abstraction than to distinguish the existence of sensible objects from their being perceived so as to conceive them existing unperceived?” (PHK 5:42).

What does this remark about the doctrine of abstract ideas mean? If it means that the doctrine is a necessary condition of the truth of the prevailing, so that if the doctrine were true, the opinion would be true, then we might expect Berkeley to argue from the impossibility of an abstract idea to the mind-dependence of sensible objects. On the other hand, if the remark means the doctrine is sufficient to explain the prevalence of the opinion, Berkeley might well invoke the fallacious doctrine for this purpose. He might say that because the mistaken theories have long been taught by the learned, many people are inured to speaking without thinking of what they might mean. They utter a sentence such as “Mountains exist unperceived” without realizing it signifies nothing. Indeed, Berkeley needs to explain how they think they believe something they cannot think of. Rickless observes that both interpretations are semantically permissible, but argues that the second is clearly the better. He contends that because Berkeley has already argued that it is impossible that mountains exist unperceived, he has no need for a second argument in section 5. That would be repetitious and pointless. The second interpretation gives the argument in PHK 5 a clear purpose and place in Berkeley’s defense of the idealist theory. Overall, Rickless concludes, this is the sole purpose of PHK 5. Nothing else is said to depend on that doctrine. Rickless is right that section 5 offers this defense, but it also contains an argument against the conceivability of mind-independent sensible objects which needs to be taken into account.

There are three main problems with Rickless’s interpretation. First, if we can take Berkeley at his word, he is “content to rest the whole” on one argument which consists of a particular thought experiment (PHK 22: 50). I do not take his word lightly. Second, Berkeley plainly has a motive for following PHK 4 with a second proof of the idealist doctrine: the preceding argument is unlikely to carry the conviction of many readers. After all, it takes as a premise that sensible objects are nothing but ideas which, as Locke put it, cease to exist when they cease to be perceived. This is the sticking point of the idealist claim. Readers are no more likely to credit the premise than the conclusion. The second proof makes up for this by showing why in general something that is immediately perceived by and present to a mind, as ideas are, cannot be separated from cognition of it. Third, as I understand the argument in PHK 5, it is not an inference from an epistemically prior argument, but a self-contained proof.

The relevant part of the text poses a question: is it possible to separate, even in thought, things we see and feel from perception? A two-stage reason for the negative answer follows. In the first stage, Berkeley relates his own conceptual abilities. He says he “might as well divide a thing from itself” and continues with a reprise of the report of his thought experiments in Introduction 10. To repeat, the summary claim that what is impossible exceeds his power of conception is an inductive inference from a small number of trials. These reports provide a sort of foundation for the second stage: “Hence as it is impossible for me to see or feel anything without an actual sensation of that thing, so is it impossible for me to conceive in my thoughts any sensible thing or object distinct from the sensation or perception of it” (PHK 5:43). If the first premise were the tautology that it is impossible that something is seen or felt and not seen or felt, the argument would be invalid. On a more nuanced reading it expresses the thought that a person cannot see or feel an object without a sensation of it, a sensation that represents it as something that is sensed. As I understand it, the principle of the inference is that we must conceive sensible objects to be as we perceive or sense them to be. 31 The conclusion follows accordingly: it is impossible to conceive a sensible thing apart from a sensation or perception which represents it as something perceived. The main difference between this and Philonous’s proof of the inconceivability of the general abstract idea of an extension is that in the present case, Berkeley needs to bring out what the elements that constitute sense perception of an object are.

The following section, PHK 6, makes explicit appeal to conceptual feats readers may find they are unable to perform. It expands the reasoning in the previous section in two ways. One is by explicitly including in its scope all bodies comprised by the universe. To suppose that any part of the “choir of heaven and the furniture of the earth” exists otherwise than in a mind involves “all the absurdity of abstraction” (PHK 6:43). Another is by including both finite spirits and an eternal spirit in the scope of spirits that perceive sensible things. For proof of these weighty contentions, Berkeley appeals to a thought experiment once again: “To be convinced of which, the reader need only reflect and try to separate in his own thoughts the being of a sensible thing from its being perceived” (PHK 6:43).

What has come to be known as the master argument (PHK 22–23) challenges readers to “conceive it possible for a sound, or figure, or motion, or colour, to exist without the mind” (PHK 22:50) and promises to concede the possibility if they meet the challenge. It cannot be met, Berkeley argues, because in order to conceive it possible that books exist without a mind, a person must “conceive them existing unconceived or unthought of” (PHK 23:50). This is said not to be possible because if she conceives books, they do after all exist in her mind. Several things seem to be amiss here. But I will just briefly explain how I understand two key notions and construct an interpretation in terms of them.

What is it to exist in a mind? All qualities and sensible objects exist in a mind, and they do so if and only if they are known or perceived by a mind (PHK 2–3:41–42). Ideas imprinted by the senses exist in a mind, and they exist in a mind if and only if the mind perceives them (PHK 6:43).

The second key issue is how the verb “to conceive” is used in this passage. The verb takes a grammatical object which can signify either a thing to which the conception is directed or something internal to—specified by—the conception. If used in the first extensional way, it is impossible that a thing be conceived and not exist in nature. If used in the second intensional sense, the thing conceived need not exist outside of the conception. As we will see, the intensional reading is preferable.

Berkeley argues that conceiving books in a closet where no one is in a position to perceive them does not meet the challenge because when we concentrate on conceiving books without a mind, we take no notice of ourselves even as we think of them: “the mind is … deluded to think it can and doth conceive bodies existing unthought of or without the mind” (PHK 23:50). On the most common reading, this is to say that if actual books are conceived they are thereby thought of. It invites the response “So what?” If I happen to conceive my copy of Locke’s Essay , it is true that the volume is thought of but it need not have been. By contrast, Berkeley is engaged to show that its being perceived is necessary for its existence. This is reason to prefer the intensional interpretation of “conceives.”

As I understand it, the challenge is to conceive what realists suppose they are able to do, that is, to form a positive conception of books existing not in a mind. The rub is that if someone deliberately forms a conception of books existing not in a mind, she cannot avoid knowing the conceived books are thought of. There are, then, two things she conceives: books not existing in a mind and their being thought of. The second element represents books which are internal to the conception as books that are thought of. So although readers might conceive the former and omit their being thought of, as Berkeley says, selective attention takes nothing away from what she conceives. In effect, readers are tasked with conceiving books without knowing it. The inability to execute the task is intended to show that things of the sort that sometimes exist in minds essentially do so. Their mode of being is mind-dependent. It is very important that the master argument does not apply to spirits, which cannot exist in a mind to begin with. More would have to be said to develop this sketch into a full-blown interpretation. Whether that would advance the case for idealism is yet another question. But I hope what I have said is plausible enough to contribute to the textual evidence that the impossibility of abstractions is used for the purpose of proving idealism in the Principles.

The following section, PHK 24, broaches the question of what it might be for sensible things to exist otherwise than in a mind, were it possible. Readers are asked whether or not the phrase “ absolute existence of sensible objects in themselves, or without the mind ” can be understood (PHK 24:51). They are expected to find that it expresses either a contradiction—inert sensible objects exist in the manner of active spirits—or nothing at all. To conceive an existence which is neither that of something that is perceived nor that of something that perceives, that is, of nothing we know, is the most general, abstract, unintelligible idea of all (PHK 17:48; PHK 81:75–76; see especially PHK 89:79).

In conclusion, the attack on abstract ideas of qualities is a model of argumentation which is the pattern for arguments against certain abstract metaphysical ideas. Although some scholars hold that anti-abstractionist reasoning presupposes independent proof of the impossibility of the supposed separation, there is reason to think the former is epistemically basic in the Principles . Discursive arguments that sensible things cannot exist unless perceived need to be validated by direct examination of the structure of experience.

Another error is the assumption that the only use of words is to convey the thoughts of the person who utters them (PHKI 19–20:36–38 ) .

This is discussed at length by Kenneth Pearce (2017 , 8–30).

  Kenneth Winkler (1989 , 30–31) and Howard Robinson (2003 , 696 ff.) give particular importance to the problem of universals in discussing the doctrine of abstract ideas.

E 2.11.9; 3.3.11.

As several scholars have noted, Berkeley holds that passions and operations of mind, being its affections, are not ideas and there can be no ideas-of them (PHK 27:52–53; PHK 142:106; TD 196).

According to Berkeley, ideas of the first sort can be made to signify other ideas in nature and by human convention, but this requires that they be put to a certain use by some mind.

In Bolton (1987) , I say that ideas are objects, that this explains why abstract ideas are impossible, and that the theory of idea-objects is an axiom of Berkeley’s philosophy. These are views I still hold, although several scholars have made objections to them; for references, see Rickless (2012) . I concur with the defense against these objections in Pearce (2017 , 19–21). As I see it, the main difference in the present account lies in my coming to understand that Berkeley has a reason to ascribe considerable probative value to attending to one’s own ideas.

MI. This has often been assumed to be an early draft of the Introduction to PHK, but as Belfrage shows, there is no clear evidence for this. Belfrage argues that it is likely to have been written in 1708, two years before publication of PHK, but no exact date has been established so far.

“[Ideas] are not thought to Represent [things] any otherwise than as they resemble them” (MI 20:83). The fact that this thesis does not appear in PHK is noted and its possible causes are discussed in MI.

Cf. E 2.2.1.

Compare Locke’s view that “consciousness … is inseparable from thinking…. It being impossible for anyone to perceive, without perceiving that he does perceive” (E 2.27.9).

  George Pappas (2000 , 44) aptly calls this type of abstract ideas “category-transcendent.”

Berkeley endorses the principle that we have an idea (conceive) of a thing only if it is possible (DHP 232).

  Winkler (1989 , 33); Rickless (2012) holds this to be true of singling, but not generalizing, abstract ideas; also Rickless (2013 , 106–108).

Also Alc 2,6:333–334.

Locke’s notion of ideas is somewhat different from Berkeley’s; if the latter cannot accommodate abstract ideas, it remains to be seen whether the former can. There is, however, no reason to think Locke is committed to the view that the abstract idea of a triangle is a single idea; on the contrary, it is a complex simple mode. Michael Ayers (1991 , vol. 1, 248–252) maintains that Locke’s view is not different from Berkeley’s; Jonathan Walmsley (2014 , 29–58) makes a critical assessment of this.

  Winkler (1989 , 49–52) seems to have been first to bring this out.

This is urged in Atherton (1987 , 49–53); Pearce (2017) ; and Rickless (2012) takes this to hold only for generalizing abstract ideas.

E 4.2.1 is a good example.

But Michael Jacovides (2009) argues that Berkeley’s performance is due to the fact that conception is a “suggestible faculty” which can be influenced by what a person believes on the basis of an argument he produced.

See also Pearce (2017) .

See Rickless (2012 , 734n10).

  Robinson (2003 , 698).

  Bolton (2015) .

Others include Atherton (1987) and Pappas (2000) .

In addition, claims to the effect that certain abstract ideas are inconceivable or unintelligible serve as a premise in two arguments against the intelligibility of an abstractionist definition of material substance (PHK 17:47–48 and PHK 81:75–76) and as a premise in an argument against the materialist theory that primary qualities exist in material substance whereas secondary qualities exist only in the mind; primary qualities cannot be conceived apart from secondary ones (PHK 10:45).

  Winkler (1989 , 28–33); Rickless (2012) takes this to be true of singling abstract ideas only.

See also Ott (2004) . PHK 4:42 contains a discursive argument to show that it is impossible that sensible things exist unperceived. Its place in Berkeley’s overall strategy of arguing for idealism is discussed below.

  Bolton (2011) .

  Rickless (2012) ; Rickless (2013) makes a similar argument with some additions.

Michael Ayers (in Berkeley 1993 , xxiii) suggests that the constraint in view is: we have to think of a sensible thing as it would be if we were perceiving it. This As I see it, this is close to what Berkeley wants to hold but it does not identify the crucial feature a thing would have if we were perceiving it.

Atherton, Margaret.   1987 . “Berkeley’s Anti-Abstractionism.” In Essays on the Philosophy of George Berkeley , edited by Ernest Sosa , 45–60. Dordrecht: Reidel.

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Bolton, Martha Brandt.   2011 . “Belief and Its Objects in Berkeley’s System.” In Berkeley’s Lasting Legacy: 300 Years Later , edited by T. Airaksinen and B. Belfrage , 251–272. New Castle upon Tyne: Cambridge Scholars.

Bolton, Martha Brandt.   2015 . “Berkeley on the Language of Vision and the Rules of Visual Signification.” In The Battle of the Gods and Giants Redux , edited by Patricia Easton and Kurt Smith , 277–299. Leiden: Brill.

Jacovides, Michael.   2009 . “ How Berkeley Corrupted His Capacity to Conceive. ” Philosophia 37: 461–496.

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How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

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Nasa next-generation solar sail boom technology ready for launch.

Tara Friesen

Nasa’s new lightweight sailor , enabling future solar sails.

Sailing through space might sound like something out of science fiction, but the concept is no longer limited to books or the big screen. In April, a next-generation solar sail technology – known as the Advanced Composite Solar Sail System – will launch aboard Rocket Lab’s Electron rocket from the company’s Launch Complex 1 in Māhia, New Zealand. The technology could advance future space travel and expand our understanding of our Sun and solar system.  

Solar sails use the pressure of sunlight for propulsion, angling toward or away from the Sun so that photons bounce off the reflective sail to push a spacecraft. This eliminates heavy propulsion systems and could enable longer duration and lower-cost missions. Although mass is reduced, solar sails have been limited by the material and structure of the booms, which act much like a sailboat’s mast. But NASA is about to change the sailing game for the future.  

The Advanced Composite Solar Sail System demonstration uses a twelve-unit (12U) CubeSat built by NanoAvionics to test a new composite boom made from flexible polymer and carbon fiber materials that are stiffer and lighter than previous boom designs. The mission’s primary objective is to successfully demonstrate new boom deployment, but once deployed, the team also hopes to prove the sail’s performance.  

Like a sailboat turning to capture the wind, the solar sail can adjust its orbit by angling its sail. After evaluating the boom deployment, the mission will test a series of maneuvers to change the spacecraft’s orbit and gather data for potential future missions with even larger sails.

“Booms have tended to be either heavy and metallic or made of lightweight composite with a bulky design – neither of which work well for today’s small spacecraft. Solar sails need very large, stable, and lightweight booms that can fold down compactly,” said Keats Wilkie, the mission’s principal investigator at NASA’s Langley Research Center in Hampton, Virginia. “This sail’s booms are tube-shaped and can be squashed flat and rolled like a tape measure into a small package while offering all the advantages of composite materials, like less bending and flexing during temperature changes.”

After reaching its Sun-synchronous orbit, about 600 miles (1,000 kilometers) above Earth, the spacecraft will begin unrolling its composite booms, which span the diagonals of the polymer sail. After approximately 25 minutes the solar sail will fully deploy, measuring about 860 square feet (80 square meters) – about the size of six parking spots. Spacecraft-mounted cameras will capture the sail’s big moment, monitoring its shape and symmetry during deployment.

With its large sail, the spacecraft may be visible from Earth if the lighting conditions are just right. Once fully expanded and at the proper orientation, the sail’s reflective material will be as bright as Sirius, the brightest star in the night sky.

“Seven meters of the deployable booms can roll up into a shape that fits in your hand,” said Alan Rhodes, the mission’s lead systems engineer at NASA’s Ames Research Center in California’s Silicon Valley. “The hope is that the new technologies verified on this spacecraft will inspire others to use them in ways we haven’t even considered.”

Through NASA’s Small Spacecraft Technology program , successful deployment and operation of the solar sail’s lightweight composite booms will prove the capability and open the door to larger scale missions to the Moon, Mars, and beyond. 

This boom design could potentially support future solar sails as large as 5,400 square feet (500 square meters), about the size of a basketball court, and technology resulting from the mission’s success could support sails of up to 21,500 square feet (2,000 square meters) – about half a soccer field. 

“The Sun will continue burning for billions of years, so we have a limitless source of propulsion. Instead of launching massive fuel tanks for future missions, we can launch larger sails that use “fuel” already available,” said Rhodes. “We will demonstrate a system that uses this abundant resource to take those next giant steps in exploration and science.”  

Because the sails use the power of the Sun, they can provide constant thrust to support missions that require unique vantage points, such as those that seek to understand our Sun and its impact on Earth. Solar sails have long been a desired capability for missions that could carry early warning systems for monitoring solar weather. Solar storms and coronal mass ejections can cause considerable damage on Earth, overloading power grids, disrupting radio communications, and affecting aircraft and spacecraft. 

Composite booms might also have a future beyond solar sailing: the lightweight design and compact packing system could make them the perfect material for constructing habitats on the Moon and Mars, acting as framing structures for buildings or compact antenna poles to create a communications relay for astronauts exploring the lunar surface. 

“This technology sparks the imagination, reimagining the whole idea of sailing and applying it to space travel,” said Rudy Aquilina, project manager of the solar sail mission at NASA Ames. “Demonstrating the abilities of solar sails and lightweight, composite booms is the next step in using this technology to inspire future missions.” 

NASA Ames manages the Advanced Composite Solar Sail System project and designed and built the onboard camera diagnostic system. NASA Langley designed and built the deployable composite booms and solar sail system. NASA’s Small Spacecraft Technology (SST) program office based at NASA Ames and led by the agency’s Space Technology Mission Directorate (STMD), funds and manages the mission. NASA STMD’s Game Changing Development program developed the deployable composite boom technology. Rocket Lab USA, Inc of Long Beach, California is providing launch services. NanoAvionics is providing the spacecraft bus.   

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