human behavior research paper

The future of human behaviour research

Affiliations.

• 1 Department of Political Science, Ohio State University, Columbus, OH, USA. [email protected].
• 2 School of Communication and Digital Media Research Centre (DMRC), Queensland University of Technology, Brisbane, Queensland, Australia. [email protected].
• 3 Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADM+S), Melbourne, Victoria, Australia. [email protected].
• 4 Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy. [email protected].
• 5 Venetian Institute of Molecular Medicine (VIMM), Padova, Italy. [email protected].
• 6 Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, USA. [email protected].
• 7 Microsoft Research New York, New York, NY, USA. [email protected].
• 8 École Normale Supérieure, Paris, France. [email protected].
• 9 Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected].
• 10 Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany. [email protected].
• 11 Department of Psychology, University of California at Berkeley, Berkeley, CA, USA. [email protected].
• 12 American University of Beirut, Beirut, Lebanon. [email protected].
• 13 Department of Global Development, College of Agriculture and Life Sciences and Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, USA. [email protected].
• 14 Department of Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 15 Center for Social and Environmental Systems Research, Social Systems Division, National Institute for Environmental Studies, Tsukuba, Japan. [email protected].
• 16 State Key Laboratory of Brain and Cognitive Sciences and Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 17 WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 18 Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 19 Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA. [email protected].
• 20 Department of Experimental Psychology, University of Oxford, Oxford, UK. [email protected].
• 21 Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK. [email protected].
• 22 CORE - Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark. [email protected].
• 23 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. [email protected].
• 24 Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. [email protected].
• 25 Complex Human Data Hub, University of Melbourne, Melbourne, Victoria, Australia. [email protected].
• 26 ODID and SAME, University of Oxford, Oxford, UK. [email protected].
• 27 School of Public Policy, Georgia Institute of Technology, Atlanta, GA, USA. [email protected].
• 28 Centre of Excellence FAIR, NHH Norwegian School of Economics, Bergen, Norway. [email protected].
• 29 GESIS - Leibniz Institute for the Social Sciences, Köln, Germany. [email protected].
• 30 RWTH Aachen University, Aachen, Germany. [email protected].
• 31 Complexity Science Hub Vienna, Vienna, Austria. [email protected].
• PMID: 35087189
• DOI: 10.1038/s41562-021-01275-6
• Anthropology / trends
• Artificial Intelligence / trends
• Behavioral Research / trends*
• Social Sciences / trends*

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Systematic review article, designing immersive virtual environments for human behavior research.

• 1 Department of Design and Environmental Analysis, Cornell University, Ithaca, NY, United States
• 2 Department of Communication, Cornell University, Ithaca, NY, United States

What are strategies for the design of immersive virtual environments (IVEs) to understand environments’ influence on behaviors? To answer this question, we conducted a systematic review to assess peer-reviewed publications and conference proceedings on experimental and proof-of-concept studies that described the design, manipulation, and setup of the IVEs to examine behaviors influenced by the environment. Eighteen articles met the inclusion criteria. Our review identified key categories and proposed strategies in the following areas for consideration when deciding on the level of detail that should be included when prototyping IVEs for human behavior research: 1) the appropriate level of detail (primarily visual) in the environment: important commonly found environmental accessories, realistic textures, computational costs associated with increased details, and minimizing unnecessary details, 2) context: contextual element, cues, and animation social interactions, 3) social cues: including computer-controlled agent-avatars when necessary and animating social interactions, 4) self-avatars, navigation concerns, and changes in participants’ head directions, and 5) nonvisual sensory information: haptic feedback, audio, and olfactory cues.

Introduction

The influence of surrounding environments on behavior: research limitations.

Our surrounding physical environment can influence behavior ( Waterlander et al., 2015 ) as it “affords” (per Gibson, 1979 ) the activities of the broader social, political, and cultural world. By understanding how our surrounding environment affects occupants, researchers can identify evidence-based design approaches such as developing standardized evaluation toolkits ( Joseph et al., 2014 ; Rollings and Wells, 2018 ), identifying design moderators ( Rollings and Evans, 2019 ), and ultimately informing policy, including guidelines governing how facilities are built, renovated, and maintained ( Sachs, 2018 ). By understanding how environments affect behaviors on a microbehavioral (i.e., unconscious) level, researchers can identify appropriate interventions (e.g., providing more sidewalks to encourage physical activity) and thereby inform the development of more effective informational and environmental interventions to improve desirable behavior ( Marcum et al., 2018 ).

However, experimentally examining the influence of our surrounding environment on behavior is challenging. Real-life environmental manipulations may be costly and even politically challenging to implement ( Schwebel et al., 2008 ). On the other hand, behaviors induced in conventional lab-based environments may not be generalizable to real-life environments ( Ledoux et al., 2013 ). The influence of the surrounding environment on behaviors might be better understood ( Ledoux et al., 2013 ) if researchers could immerse participants in complex physical and social environments that are ecologically valid while being highly controlled ( Veling et al., 2016 ). Because of this, simulations are sometimes used to explore the relationship between environment and behavior ( Marans and Stokols, 2013 ). Potential simulations can include mockups, sketches, photographs, models, and immersive virtual environments (IVEs). While CAVE automatic virtual environments (CAVEs, Cruz-Neira et al., 1993 ) and head-mounted displays (HMDs) have both been used to simulate such environments, the recent increase in the availability of consumer HMDs means that many more researchers can now use IVEs to answer questions about the effects of surrounding environments on behaviors. In this review, we reviewed and synthesized peer-reviewed research that used IVEs presented in HMDs for research on behavior influenced by our surrounding environment, with the aim of showcasing the solutions found by previous researchers. As virtual reality (VR) and IVEs will be frequently mentioned in this review, it is important to distinguish “VR” as the technology used to create “IVEs.”

Immersive Virtual Environment Tools for Human Behavior Research: Making the Case

Past research suggests that VR is a useful research tool to simulate real-life environmental features, as it allows researchers to immerse participants in hypothetical contexts and study their responses to controlled environmental manipulations otherwise difficult to examine in real-life environments ( Parsons et al., 2007 ; Schwebel et al., 2008 ; Poelman et al., 2017 ; Ahn, 2018 ). Considerable work has demonstrated VR’s ability to elicit behavioral responses to virtual environments, even when the participant is well aware that the environment is not “real” as in demonstrations of the classic “pit demo” ( Meehan et al., 2003 ).

In 2002, Blascovich and colleagues foresaw the advantages of VR as a tool for research in the social sciences. Although Blascovich’s original article discussed the use of VR as a tool for social psychology specifically, the advantages he describes for balancing experimental control and mundane realism and improving replicability and representative sampling have made it a tool of interest for researchers in several social science fields. VR has a high degree of realism: users tend to react to scenarios as if they were occurring in the real world. VR allows for a high degree of experimental control. Environments, events, and even virtual people can be programmed to appear to every user in the same way. Thus, VR has already been used extensively for diagnosis ( Parsons et al., 2007 ), clinical education ( Lok et al., 2006 ; Atesok et al., 2016 ), and clinical and experimental interventions ( Difede and Hoffman, 2002 ; Wiederhold and Wiederhold, 2010 ; Wiederhold, 2017 ).

VR provides critical benefits over other methods available for behavior research ( Schwebel et al., 2008 ). These advantages are particularly applicable when considering the influence of environments on behavior. VR has the potential to examine how people behave in real-life situations, without exposing participants to the risk and inconsistency of real-world environments ( Blascovich et al., 2002 ). Participants can safely experience immersion in the virtual environment when the real environment is hazardous ( Viswanathan and Choudhury, 2011 ), permitting researchers to ethically examine potentially dangerous behaviors ( Schwebel et al., 2012 ). Additionally, it is relatively easy to manipulate environmental factors such as noise and crowding in virtual environments ( Neo et al., 2019 ).

The Design of VR Environments for Behavior Studies: Research Gap

A prototype “is an artifact that approximates a feature (or multiple features) of a product, service, or system” ( Otto and Wood, 2001 ; Camburn et al., 2017 , p. 1) and “a virtual prototype is one which is developed (and tested) on a computational platform” ( Camburn et al., 2017 , p. 17). VR, especially its prototyping functions (i.e., the test-refinement-completion of designs using digital mockups, Ulrich and Eppinger, 2012 ), has been increasingly applied to behavior research. In this review, we examine VR’s potential to address environmental effects on behavior. In these cases, IVEs should be designed such that interactions between the individual and the virtual environment are as analogous as possible to interactions that would take place if the individual were in the actual environment, with the ultimate goal of developing a more robust way of examining the impact of the surrounding environment on behavior.

VR is generally considered to be a high-presence medium. Presence refers to the sense of “being there” in the VR environment ( Heeter, 1992 ; Slater et al., 2009 ). While presence and immersion are terms sometimes used interchangeably, researchers have distinguished between the subjective psychological sense of presence and immersion, which can be considered a quality of the technology ( Slater, 2018 ). A virtual reality setup that provides highly detailed visual content, spatialized sound, and haptic feedback (e.g., through vibrating controllers) would be considered more immersive than a scene rendered on a desktop monitor. Greater immersion is generally considered to increase presence ( Cummings and Bailenson, 2016 ). Because consumer HMDs have reduced cost and expense while retaining a high sense of presence; it is plausible for many more researchers to use VR for prototyping applications; thus, we focus our recommendations on this larger pool of potential researchers.

While other considerable valuable works have used CAVE or desktop-based virtual environments to examine behavior, we have limited our analysis in this review to studies that use HMDs, to study behavior as it relates to the environment. The relatively lower cost and portability of new consumer HMDs mean that researchers who have not previously engaged with virtual reality now have the opportunity to use these systems for their research. This review aims to provide a summary of design considerations pulled from existing research in virtual reality that might prove useful to potential researchers who are not experienced in this area.

The qualities of HMDs provide special opportunities and constraints. HMDs combine portability with the ability to block out the surrounding environment, making them good for “in-the-wild” studies ( Oh et al., 2016 ). The greater presence HMDs can provide is particularly important to these behavioral studies but comes with tradeoffs. Users do not see their real bodies, so researchers must decide whether or not to include avatars. HMDs allow users to experience spaces that may be larger than the physical space that they are actually in, meaning that users’ abilities to navigate must be programmed and controlled. Such environments allow for the ready tracking of behavioral data ( Yaremych and Persky, 2019 ) and interaction with objects, but all of these interactions must be designed. In this review, we highlight the solutions and tradeoffs that previous researchers have made in this context.

Best Practices for Successful IVE-Based Experimental Studies

Heydarian and Becerik-Gerber (2017) describe “four phases of IVE-based experimental studies” and discuss best practices for consideration in different phases of experimental studies ( Figure 1 , see Heydarian and Becerik-Gerber, 2017 for in-depth discussion).

FIGURE 1 . Four phases of IVE-based experimental studies.

In this review, we focus on the “development of experimental procedure” phase, described by Heydarian and Becerik-Gerber as Phase 2. This includes the design and setup of the IVEs, especially considerations involving the level of detail required (i.e., factor(s) recognizable by participants; Heydarian and Becerik-Gerber, 2017 ). This may differ between studies and can include visual appearance, behavioral realism, and virtual human behavior. To meet the study objectives, a sense of presence is key, allowing study participants to feel “there” and thus behave as if they were in the actual environment.

However, information on the design process in Phase II can be hard to find. Researchers typically describe the “final environment” they have designed in publications, but justifications for the many design decisions they have made in the development of the virtual environment are less common, probably at least in part due to publication length limitations. However, this information is extremely valuable. The following review expands on the work by Heydarian and Becerik-Gerber (2017) by reviewing and synthesizing strategies from 18 studies using IVEs for behavior research. In addition, we have created a wiki ( https://osf.io/gyadu/ ) to collect citations for other papers that use IVEs for this purpose so that this database can be updated. We hope this synthesis and this wiki will be an additional resource for researchers new to this space to build on the knowledge of previous researchers to make informed choices when they are designing such IVEs.

Inclusion Criteria

Inclusion criteria were as follows: (a) the study examined nonclinical populations, (b) the study used IVEs presented via HMDs to examine behaviors influenced by the environment, (c) users experienced a virtual environment that plausibly represented an actual environment, and (d) the study provided sufficient details on how it designed and set up the IVE.

Review Procedure and Data Extraction

After consultation with a research librarian, we applied the following keywords ( Table 1 ) and MeSH search terms: virtual reality, behavior, prototype, and design ( Table 2 ). Terms were combined with the Boolean operators “and” and “or” to identify relevant studies. We did not conduct searches separately by specific behaviors, e.g., “grocery shopping” or environments, e.g., “grocery store,” but narrowed down the results from an initial search focusing on VR. Using PubMed, Web of Science, Scopus, and Google Scholar databases, we conducted a systematic search to identify English-language, peer-reviewed publications, and conference proceedings on experimental and proof-of-concept studies that described the design, manipulation, and setup of the IVEs to examine behaviors influenced by the environment. The search targeted articles were published before May 15, 2020 (i.e., no lower bound cutoff date). Reviewer 1 assessed retrieved texts to determine if they met the inclusion criteria. If a study was deemed potentially eligible, the full article was retrieved for further assessment and inclusion. A second reviewer screened all included articles. The selection was finalized after discussion and consensus between reviewers. We identified additional studies by searches of the references provided in the included publications ( Greenhalgh and Peacock, 2005 ). Once the finalized list of papers was determined, these data were extracted: first author, year, behavior, environment, and strategies in designing IVEs for behavior research.

TABLE 1 . List of keywords.

TABLE 2 . List of search terms.

Data Synthesis and Analysis

Due to the heterogeneity of research in the field, quantitative synthesis was not planned. To conduct a narrative synthesis, two reviewers independently categorized studies based on strategies in designing IVEs for behavior research. Based on past use of VR in behavior research, our research question was as follows: What are strategies that researchers identified as effective in designing IVEs for behavior research?

We screened 203 citations and reviewed 61 full texts, of which 18 met the inclusion criteria ( Figures 2 and 3 ). All the studies we found were in the range of 2015–2020. Exclusion criteria were as follows: (a) the study examined clinical populations (57 citations), (b) the study did not use IVE presented via HMDs to examine behaviors influenced by the environment (32), (c) the study did not use VR to create a virtual environment (28), and (d) the study did not provide sufficient details on how it designed and set up the IVE (50). Case studies that did not describe behavioral outcomes were thus also excluded ( Lovreglio et al., 2018 ). As some studies were rejected for more than one reason, the sum of studies is greater than 142. For brevity, the types of behaviors and environments are summarized in Table 3 .

FIGURE 2 . Flow diagram.

FIGURE 3 . A yes/no flowchart to decide if a study provided sufficient details on how it designed and set up the IVE.

TABLE 3 . Types of behavior and environment.

Building from recent research using IVEs as a prototyping tool to examine the relationship between the environment and human behaviors, this review provides strategies drawn from previous IVEs designed to understand environments’ influence on behaviors. This discussion is grounded in our analysis of what design researchers have reported as effective in previous experiments and on the outcomes in the reviewed studies. Five key categories emerged from this analysis: (1) the appropriate level of detail (primarily visual) in the environment, (2) context, (3) social cues, (4) participant tracking and rendering, and (5) nonvisual sensory information ( Figure 4 . A high-resolution version of this figure is available as Supplementary Figure S1 ).

FIGURE 4 . Flow diagrams summarizing the strategies for the design of IVEs to understand environments’ influence on behaviors.

In this review, many recommendations (e.g., including dynamic representation of the participant’s body) are dependent on the research goal and tradeoffs (e.g., additional cost, development time, and technical capabilities). Hence, before any meaningful discussion of what researchers have reported as effective in designing IVEs for environment-behavior research, all strategies and decisions should be evaluated in the context of two design principles: (1) the research goal and (2) potential tradeoffs to design choices, such as development cost and scalability. Additionally, the recommendations featured in this review were specific examples used by researchers in past studies. It is important to note that there are various ways to approach these considerations that may or may not have been discussed in past studies.

Level of Detail in the Environment

Important commonly found environmental accessories.

Researchers proposed that IVEs should incorporate typical elements (features such as furniture or features) of actual environments. In a study examining gambling behavior, Dickinson et al. (2020) included items such as paper slips, pens, and stools in the betting shop ( Figure 5 ).

FIGURE 5 . Dickinson et al. (2020) included items such as paper slips, pens, and stools in the betting shop.

Realistic Textures

Realistic textures can be created by taking photos of the actual product before attaching it to the virtual objects. This is particularly important if participants are expected to move around the environment and pick up objects to examine them ( Morrongiello et al., 2015 ; Lombart et al., 2019 ; Siegrist et al., 2019 ). For example, in a study examining purchase behavior, Lombart et al. (2019) used real product textures with high-resolution pictures from real products ( Figure 6 ).

FIGURE 6 . Lombart et al. (2019) used real product textures with high-resolution pictures from real products.

Computational Costs Associated With Increased Details

Realistic rendering can increase an individual’s sense of presence in the virtual environment. According to Slater et al. (2009) , visual realism has two components: geometric realism (i.e., virtual and real objects look alike) and illumination realism (i.e., the fidelity of the lighting model). However, building complex IVEs can be time-consuming, requires heavy computational algorithms, and may even decrease frame rate when participants view the IVE ( Sobhani et al., 2017 ; Lin et al., 2020 ).

Researchers must then decide which extraneous features in the surrounding environment are not key to the research question and can be removed while maintaining ecological validity. For example, in examining distracted pedestrian crossing behavior, Sobhani et al. (2017) excluded other pedestrians and cyclists, focusing on vehicles and the participant ( Figure 7 ). Before finalizing the research design and hypotheses, researchers should always consider the complexity of their desired IVE since the impact of a “suboptimal” IVE (e.g., an IVE that lacks critical details of the surrounding environment) could have significant effects on participants’ behaviors ( Lin et al., 2020 ).

FIGURE 7 . Sobhani et al. (2017) excluded other pedestrians and cyclists, focusing on vehicles and the participant.

Minimizing Unnecessary Detail

IVEs may be perceived as more immersive than lab conditions ( Dickinson et al., 2020 ). However, high levels of visual realism (i.e., consistency between one’s virtual vs. real-world experience Witmer and Singer, 1998 ) might increase expectations for other aspects (e.g., nonvisual and tactile) of the simulation to be equally realistic ( Dickinson et al., 2020 ). This raises a critical consideration in designing IVEs for research: while realistic IVEs generally increase the ecological validity of research, some kinds of realism (e.g., adding avatars) inherently increase the complexity and confounding variables associated with experimental environments. Here, we define a confounding variable as an “extraneous variable whose presence affects the variables being studied so results do not reflect the actual relationship between the variables under study” ( Pourhoseingholi et al., 2012 , p. 79).

Too much detail can reintroduce some challenges associated with research in actual environments ( Dickinson et al., 2020 ). Before designing an IVE, researchers can use input from past research, end-users, and subject experts to identify possible confounds and evaluate the risks and benefits of including a given feature into the IVE ( Persky et al., 2018 ). For example, in a study examining navigation behaviors in powered wheelchair driving simulators, Alshaer et al. (2017) did not tell participants about the passability of the doorframes or gaps to preserve realism and did not include furnishings or decorations to avoid distractions and to remove cues to size and distance provided by familiar objects ( Figure 8 ).

FIGURE 8 . Alshaer et al. (2017) did not tell participants about the passability of the doorframes or gaps to preserve realism. Furnishings or decorations were excluded to avoid distractions.

Before creating IVEs to examine behaviors, researchers need to identify what contextual cues are necessary and evaluate whether VR can incorporate all the required elements. Social elements involve perhaps the most complicated tradeoffs, discussed in the following section.

Contextual Elements

In IVEs, participants give special attention to objects most relevant to looking behaviors, such as windows ( Lee et al., 2019 ). Participants may also engage in more exploratory behaviors (e.g., looking intensely at objects irrelevant to the research questions), behaviors that may not be as salient in actual environments ( Lee et al., 2019 ). For example, in Lee et al. (2019) , participants spent more time looking at “see-through” surfaces such as windows ( Figure 9 ). Perhaps, participants learned that available behaviors in IVEs were mostly “looking around” and focused their attention on windows, instead of objects with functionality (e.g., portability) in the real but not virtual world ( Lee et al., 2019 ). Participants’ attention to display surfaces or windows highlights the notion of multiple embeddedness during interactions in virtual environments ( Lee et al., 2019 ). A person’s experience in an IVE is still embedded in the surrounding environment ( Lee et al., 2019 ). For example, when participants allocate visual attention to window surfaces displaying extra information regarding the exterior, they may be creating a mental model of the IVE’s location and themselves in the virtual space ( Lee et al., 2019 ). Researchers should consider whether such features (e.g., windows) should be included or excluded as participants may devote unnecessary attention to “checking them out.” For example, including a “skybox” and trees as a surrounding exterior makes users think that they are inside a building with windows (i.e., virtual realism) ( Morrongiello et al., 2015 ; Nordbo et al., 2015 ).

FIGURE 9 . In Lee et al. (2019) , participants spent more time looking at “see-through” surfaces such as windows.

Stimulatory and instructional cues can provide relevant information and navigation within IVEs. In a study examining energy consumption behavior, Saeidi et al. (2019) used stimulatory cues related to the spatial and temporal configuration of the IVE to simulate relevant information about the IVE, such as the sense of time, weather, and crowding. Specifically, Saeidi et al. (2019) used lighting and moving traffic to evoke a sense of time. However, researchers should consider using such cues in moderation. For example, in a study examining evacuation behavior by Tucker et al. (2018) , smoke was incorporated only to enhance participants’ anxiety and perceived hazard and not to the extent where the simulating impacts of smoke hinder visibility ( Tucker et al., 2018 ) ( Figure 10 ).

FIGURE 10 . Tucker et al. (2018) incorporated smoke only to enhance participants’ anxiety and perceived hazard and not to the extent where the simulating impacts of smoke hinder visibility.

Saeidi et al. (2019) also used instructional cues to help participants navigate within the IVE and distinguish and interact with operable virtual objects. For example, as participants hover the controller over operable objects, they would start blinking, signaling activation ( Saeidi et al., 2019 ) ( Figure 11 ).

FIGURE 11 . Saeidi et al. (2019) used instructional cues to help participants navigate within the IVE and distinguish and interact with operable virtual objects.

Animating Interactions

In an IVE, a feature (e.g., traffic and avatar) may be moving at a simulated speed. Identifying and creating the appropriate speed may be important for research examining behaviors such as road crossing and emergency evacuation. To reduce potential confounders (i.e., walking speed), in this example, the researchers kept the demonstration avatar’s walking speed constant based on the staff’s walking speed ( Shi et al., 2019 ). If possible, researchers should use pretests to ensure that calibration accuracy between participants’ actual movements and virtual animations is acceptable to participants ( Shi et al., 2019 ). Specifically, there should be minimal movement mismatches in the virtual environment when participants rotate their bodies as these mismatches can affect their feelings of presence ( Shi et al., 2019 ). In a study by Lin et al. (2020) examining evacuation behaviors, the range of avatar speeds depended on their age and gender.

Social Cues

If social aspects are important, researchers need to find ways to incorporate associated cues or agents into the IVE ( Neo et al., 2019 ).

Include Computer-Controlled Agent-Avatars When Necessary

Computer-controlled agent-avatars may enhance the realism of IVEs and facilitate the examination of behaviors such as distractions and emergency evacuation. However, avatars may also unduly distract participants from engaging in the behavior of interest. For example, in Dickinson et al. (2020) , no agent-avatars were placed in the IVE, so as not to distract participants playing the electronic gaming machine.

Animating Social Interactions

In general, introducing avatars of the self or of others requires a number of decisions. If agent-avatars are introduced, they must be animated appropriately. A number of animations are available for free or for purchase (e.g., Mixamo.com ) can be designed using modeling programs or can be generated from human motion ( Gonzalez-Franco et al., 2020 ). These animations can be automatically triggered by the actions of the participant, but sometimes a “wizard-of-Oz” scenario, in which a key digital element is actually controlled by a human user (for example, see Lucas et al., 2019 ) may be more useful. For example, in examining fall risk, instead of relative to playing a prerecorded animation, Shi et al. (2019) used a lab staff member to control another avatar in real time as the human-controlled avatar seemed more “natural and acceptable” to the participants.

Participant Tracking and Rendering

Self-avatars.

Self-avatars can enhance a user’s sense of presence ( Fox and Bailenson, 2010 ). People tend to experience an elevated sense of presence when there is a virtual representation of oneself in the VR environment and when other users (avatars) recognize them ( Nash et al., 2000 ; Fox and Bailenson, 2010 ).

When investigating certain behaviors, such as pedestrian road crossing, it may be helpful for a participant to see a dynamic representation of their body to increase their sense of presence ( Kooijman et al., 2019 ). Kooijman et al. (2019) propose that the representation needs to be realistic (i.e., not robot-looking) and gender-specific and provides synchronous tactile-visual feedback to evoke a full-body illusion ( Kooijman et al., 2019 ). However, as other researchers have found, a first-person perspective alone can aid in creating a useful body-ownership illusion ( Slater et al., 2010 ).

As suggested by Kooijman et al. (2019) , motion suits are increasingly used in road safety research and may become more common for other types of VR-based research. Different headsets may have different capabilities to track and represent user behavior. Some newer HMDs, for example, the Oculus Quest, now offer the ability to track and render users’ hands without requiring them to hold hand controllers.

Navigation Concerns

Navigation via real walking can enhance one’s sense of presence in IVEs ( Shi et al., 2019 ); however, this option is constrained by the physical space available ( Iryo-Asano et al., 2018 ; Kooijman et al., 2019 ). For example, in Kooijman et al. (2019) , which examined pedestrian crossing behaviors, participants were asked not to walk beyond the third “zebra stripe” due to the space constraints of the laboratory. Given this conflict, allowing participants to control their walking direction and speed can help them feel as if they are walking in VR environments ( Morrongiello et al., 2015 ; Marcum et al., 2018 ).

Changes in Participants’ Head Directions

Mizuchi and Inamura (2018) evaluated human behavior difference with a restricted field of view in real and IVEs and found that large changes in the head direction and some head-mounted display properties affect spatial perception about recognition speed and manipulation skill in IVEs. A scenario in which participants must frequently and dramatically change head direction may be unfavorable for observing behaviors in IVEs. In a study by Shi et al. (2019) examining fall risk behaviors, slight movement mismatches in the IVE when participants rotate their heads can affect their sense of presence (i.e., being there in the simulated world) ( Figure 12 ). IVEs and scenarios should be designed to avoid large changes in participants’ head direction, for example, putting participants in a narrow space ( Mizuchi and Inamura, 2018 ).

FIGURE 12 . Shi et al. (2019) highlighted that slight movement mismatches in the IVE when participants rotate their heads can affect their sense of presence. Shi et al. (2019) used real planks to provide haptic feedback while participants walked on “virtual planks.”

Nonvisual Sensory Information

The realism of virtual simulations was highly rated in various studies; however, most simulations lack some characteristics of the real environment (e.g., haptic feedback, sounds, smell, and other sensory contents) ( Ledoux et al., 2013 ). As with other elements that might increase immersion but create technical difficulties, adding additional sensory modalities requires the careful consideration of tradeoffs.

Haptic Feedback

Some VR-based studies integrate real elements besides the VR displays, for example, to provide passive haptic feedback. For example, in a study examining fall risk behaviors, Shi et al. (2019) used real planks to provide haptic feedback while participants walked on “virtual planks.” With such integration, the real and virtual element (e.g., a plank) should be carefully calibrated to enhance realism, and yet address safety issues (e.g., a participant falling off a plank) ( Shi et al., 2019 ). To enhance setups with real and virtual elements, avatars may be included to reflect any interactions observed with the real elements, such as falling off a real plank ( Shi et al., 2019 ).

IVEs may include relevant (i.e., typical of the actual environment) audio to isolate viewers from the real world ( Dickinson et al., 2020 ). Researchers should first determine if the sounds can enhance the IVE’s realism or if they might confound the study ( Morrongiello et al., 2015 ). In a study examining evacuation behaviors, Markwart et al. (2019) included sounds of a storm (i.e., rain; thunder), wind, cars driving by, splatter sounds from the water fountain, and footsteps. To make virtual experiences more realistic, certain sounds, such as avatars' voices, should be gender-specific ( Markwart et al., 2019 ).

Olfactory Cues

None of the studies included olfactory cues in the IVE. However, some studies cited the lack of characteristics of a real food choice environment (e.g., food smell) as a key limitation ( Ledoux et al., 2013 ; Marcum et al., 2018 ). For example, the lack of olfactory feedback might become a factor if participants were asked to pick up the virtual food ( Ledoux et al., 2013 ; Marcum et al., 2018 ). Olfactory cues have been used in studies examining virtual food; for example, Li and Bailenson (2018) explored the role of olfactory cues of a virtual donut on satiation and eating behavior and Stelick et al. (2018) developed a proof-of-concept study to determine if the pungent flavor notes of blue cheese may be enhanced by showing participants a virtual dairy barn. In Li and Bailenson (2018) , the authors attached a scented cotton bud to the front of the HMD with Velcro strips at the exact same time the participants saw and smelled a virtual donut.

Overall, more targeted haptic, auditory, and olfactory feedback will likely become possible with the rapid growth of VR technology. Nonvisual sensory information may be more important if participants engage in, for example, food selection behavior, where aesthetic and reward-oriented features may be more important ( Marcum et al., 2018 ). Adding nonvisual sensory information will also help researchers who need to capture the potential responses of a more diverse participant pool, for example, participants with low vision ( Zhao et al., 2019 ).

Limitations of This Review

This review summarizes published information about researchers’ design decisions when creating IVEs to test the effect of environments on behavior. However, we recognize that much valuable information remains unpublished due to page limits and other constraints of academic publishing. While we hope this paper can be useful, especially for researchers less familiar with this area, providing experienced researchers with resources to share their design experiences will greatly aid the research community. This would allow for the inclusion of projects that did not include behavioral data at the time of publication of this paper but that are likely to contain valuable information, for example, ( Lovreglio et al., 2018 ).

Due to the heterogeneity of human behaviors, we could not design a search strategy for each behavior (e.g., human behavior in a supermarket; human behavior in pedestrian crossing). Our search returned limited results due to our strict inclusion criteria (i.e., a study must use HMDs) and the resulting small number of studies. Our decision to limit this review to studies using HMDs may have reduced the breadth and depth of our analysis, as well as the variety of environments and behaviors. Variations in research questions and perspectives also limited our results, despite our best efforts to systematically identify and categorically include relevant studies. Some studies met the inclusion criteria but provided no discussion on how to design IVE to examine behaviors. Due to a reduced pool of studies from which to draw conclusions, some environmental considerations highlighted in this review (e.g., haptic feedback; olfactory cues) depended on the intended application and purpose. By using the term “human behavior” relatively broadly as the starting point, recommendations can be generalized to a larger and more diverse audience including researchers, designers, and practitioners.

Our review analyzed the use of IVEs in behavioral science research and provided design considerations when prototyping IVEs for research on the interaction between the environment and human behavior. We found that rather than trying to replicate every aspect of the physical environment, researchers carefully considered the level of detail needed for each element. We also found that interdisciplinary collaboration is required to conceptualize, plan, design, and execute a VR study to examine behaviors ( Metsis et al., 2019 ; Neo et al., 2019 ). We provided some sample workflows illustrated with examples from published work.

With these design considerations gleaned from experienced VR researchers in mind, other behavioral scientists may be able to better use VR to develop designs in order to examine behaviors. Ultimately, by enhancing our knowledge of the design and use of VR, researchers can better understand environmental factors that influence behavior and how to effectively alter environmental and/or policy-based interventions. However, the valuable information about the design decisions researchers makes in creating these virtual environments can be hard to find. Studies currently available only as pilots or case studies (e.g., Lovreglio et al., 2018 ), if used in the future to examine and capture behavioral data, provide promising opportunities to help researchers better understand the environments’ influence on behaviors.

While this review aims to provide a summary of relevant research up to the present, finding ways for more researchers to be able to easily share their hard-won knowledge is an important need for the community of current and interested researchers.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material ; further inquiries can be directed to the corresponding author.

Author Contributions

JN was involved in conceptualization, data curation, formal analysis, methodology, project administration, writing-original draft preparation, review, and editing. AW and MS were involved in formal analysis, methodology, writing-original draft preparation, review, and editing.

Conflict of Interest

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 would like to acknowledge the help of Ms. Amelia Kallaher (Applied Social Science Librarian, Cornell University) for developing the search strategy.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frvir.2021.603750/full#supplementary-material

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Keywords: immersive virtual environment, human behavior, design, prototype development, environmental psychology, virtual reality

Citation: Neo JRJ, Won AS and Shepley MM (2021) Designing Immersive Virtual Environments for Human Behavior Research. Front. Virtual Real. 2:603750. doi: 10.3389/frvir.2021.603750

Received: 07 September 2020; Accepted: 13 January 2021; Published: 04 March 2021.

Reviewed by:

Copyright © 2021 Neo, Won and Shepley. 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) and the copyright owner(s) 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: Jun Rong Jeffrey Neo, [email protected]

On the Dynamics of Human Behavior: The Past, Present, and Future of Culture, Conflict, and Cooperation

I provide a theoretically-guided discussion of the dynamics of human behavior, focusing on the importance of culture (socially-learned information) and tradition (transmission of culture across generations). Decision-making that relies on tradition can be an effective strategy and arises in equilibrium. While dynamically optimal, it generates static `mismatch.' When the world changes, since traits evolve slowly, they may not be beneficial in their new environment. I discuss how mismatch helps explain the world around us, presents special challenges and opportunities for policy, and provides important lessons for our future as a human species.

The article, which was delivered during the 2022 AEA Distinguished Lecture, was prepared for the American Economic Review Papers and Proceedings. I am grateful to Joseph Henrich and the other participants of `culture club' for numerous insightful discussions. I also thank the many individuals who provided valuable comments on the content of the article: Siwan Anderson, Robert Boyd, Felipe Valencia Caicedo, Ellora Derenoncourt, Leanna Eaton, Ben Enke, Grace Finley, Martin Fiszbein, Paola Giuliano, Leander Heldring, Nippe Lagerlof, Sara Lowes, Eoin McGuirk, Eduardo Montero, Jacob Moscona, Michael Muthukrishna, Jesse Perla, Gautam Rao, Christina Romer, David H. Romer, Ambra Seck, Clara Sievert, for helpful comments and suggestions. I also thank Aditi Chitkara, Grace Finley, Vibhu Pratyush, Leo Saenger, Fernando Secco, and German Vega Acuna for excellent RA work. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

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Article Contents

Introduction, what is hbe, a systematic overview of current research, hbe: strengths, weaknesses, opportunities, and open questions, supplementary material, human behavioral ecology: current research and future prospects.

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Daniel Nettle, Mhairi A. Gibson, David W. Lawson, Rebecca Sear, Human behavioral ecology: current research and future prospects, Behavioral Ecology , Volume 24, Issue 5, September-October 2013, Pages 1031–1040, https://doi.org/10.1093/beheco/ars222

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Human behavioral ecology (HBE) is the study of human behavior from an adaptive perspective. It focuses in particular on how human behavior varies with ecological context. Although HBE is a thriving research area, there has not been a major review published in a journal for over a decade, and much has changed in that time. Here, we describe the main features of HBE as a paradigm and review HBE research published since the millennium. We find that the volume of HBE research is growing rapidly, and its composition is changing in terms of topics, study populations, methodology, and disciplinary affiliations of authors. We identify the major strengths of HBE research as its vitality, clear predictions, empirical fruitfulness, broad scope, conceptual coherence, ecological validity, increasing methodological rigor, and topical innovation. Its weaknesses include a relative isolation from the rest of behavioral ecology and evolutionary biology and a somewhat limited current topic base. As HBE continues to grow, there is a major opportunity for it to serve as a bridge between the natural and social sciences and help unify disparate disciplinary approaches to human behavior. HBE also faces a number of open questions, such as how understanding of proximate mechanisms is to be integrated with behavioral ecology’s traditional focus on optimal behavioral strategies, and the causes and extent of maladaptive behavior in humans.

Very soon after behavioral ecology (henceforth BE) emerged as a paradigm in the late 1960s and early 1970s, a tradition of applying behavioral ecological models to human behavior developed. This tradition, henceforth human behavioral ecology (HBE), quickly became an important voice in the human-related sciences, just as BE itself was becoming an established and recognized approach in biology more generally. HBE continues to be an active and innovative area of research. However, it tends not to receive the attention it might, perhaps in part because its adherents are dispersed across a number of different academic disciplines, spanning the life and social sciences. Although there were a number of influential earlier reviews, particularly by Cronk (1991) and Winterhalder and Smith (2000) , there has not been a major review of the HBE literature published in a journal for more than a decade. In this paper, we undertake such a review, with the aim of briefly but systematically characterizing current research activity in HBE, and drawing attention to prospects and issues for the future. The structure of our paper is as follows. In the section “What is HBE?”, we provide a brief overview of the HBE approach to human behavior. The section “A systematic overview of current research” presents our review methodology and briefly describes what we found. We argue that the HBE research published in the period since 2000 represents a distinct phase in the paradigm’s development, with a number of novel trends that require comment. Finally, the section “HBE: strengths, weaknesses, opportunities, and open questions” presents our reflections on the current state and future prospects of HBE, which we structure in terms of strengths, weaknesses, opportunities, and open questions.

BE is the investigation of how behavior evolves in relation to ecological conditions ( Davies et al. 2012 ). Empirically, there are 2 arms to this endeavor. One arm is the study of how measurable variation in ecological conditions predicts variation in the behavioral strategies that individuals display, be it at the between-species, between-population, between-individual, or even within-individual level. (Throughout this paper, “ecological conditions” is to be interpreted in its broadest sense, to include the physical and social aspects of the environment, as well as the state of the individual within that environment.). The other arm concerns the fitness consequences of the behavioral strategies that individuals adopt. Because fitness—the number of descendants left by individuals following a strategy at a point many generations in the future—cannot usually be measured within a study, this generally means measuring the consequences of behavioral strategies in some more immediate proxy currency related to fitness, such as survival, mating success, or energetic return. The 2 arms of BE are tightly linked to one another; the fitness consequences of some behavioral strategy will differ according to the prevailing ecological conditions. Moreover, central to BE is the adaptationist stance. That is, we expect to see, in the natural world, organisms whose behavior is close to optimal in terms of maximizing their fitness given the ecological conditions that they face. This expectation is used as a hypothesis-generating engine about which behaviors we will see under which ecological conditions. The justification for the adaptationist stance is the power of natural selection. Selection, other things being equal, favors genes that contribute to the development of individuals who are prone to behaving optimally across the kinds of environments in which they have to live ( Grafen 2006 ). Note that this does not imply that behavioral strategies are under direct genetic control. On the contrary, selection favors various mechanisms for plasticity, such as individual and social learning, exactly because they allow individuals to acquire locally adaptive behavioral strategies over a range of environments ( Scheiner 1993 ; Pigliucci 2005 ), and it is these plastic mechanisms that are often in immediate control of behavioral decisions. However, the capacity for plasticity is ultimately dependent on genotype, and plasticity is deployed in the service of genetic fitness maximization.

BE is also characterized by a typical approach, to which actual exemplars of research projects conform to varying degrees. This approach is to formulate simple a priori models of what the individual would gain, in fitness terms, by doing A rather than B, and using these models to make predictions either about how variation in ecological conditions will affect the prevalence of behaviors A and B, or about what the payoffs to individuals doing A and B will be, in some currency related to fitness. These models are usually characterized by the assumption that there are no important phylogenetic or developmental constraints on the range of strategies that individuals are able to adopt and also by a relative agnosticism about exactly how individuals arrive at particular behavioral strategies (i.e., about questions of proximate mechanism as opposed to ultimate function; Mayr 1961 ; Tinbergen 1963 ). The assumptions of no mechanistic constraints coming from the genetic architecture or the neural mechanisms are known, respectively, as the phenotypic gambit ( Grafen 1984 ) and the behavioral gambit ( Fawcett et al. 2012 ). To paraphrase Krebs and Davies (1981 ), “think of the strategies and let the mechanisms look after themselves.” We return to the issue of the validity of the behavioral gambit in particular in section “Open questions.” However, one of the remarkable features of early research in BE (what Owens 2006 calls “the romantic period of BE”) was just how well the observed behavior of animals of many different species was explained by very simple optimality models based on the gambits.

HBE is the study of human behavior from an adaptive perspective. Humans are remarkable for their ability to adapt to new niches much faster than the time required for genetic change ( Laland and Brown 2006 ; Wells and Stock 2007 ; Nettle 2009b ). HBE has been particularly concerned with explaining this rapid adaptation and diversity, and thus, the concept of adaptive phenotypic plasticity has been even more central to HBE than it is to BE in general. HBE represents a rejection of the notion that fundamentally different explanatory approaches are necessary for the study of human behavior as opposed to that of any other animal. Note that this does not imply that humans have no unique cognitive and behavioral mechanisms. On the contrary, they clearly do. Rather, it implies that the general scientific strategy for explaining behavior instantiated in BE remains similar for the human case: understand the fitness costs and benefits given the ecological context, make predictions based on the hypothesis of fitness maximization, and test them. There is a pleasing cyclicity to the development of HBE. BE showed that microeconomic models based on maximization, which had come from the human discipline of economics, could be used at least as a first approximation to predict the behavior of nonhuman animals. HBE imported these principles, enriched from their sojourn in biology by a focus on fitness as the relevant currency, back to humans again.

The first recognizably HBE papers appeared in the 1970s (e.g., Wilmsen 1973 ; Dyson-Hudson and Smith 1978 ). The pioneers were anthropologists, and to a lesser extent archaeologists. A major focus was on explaining foraging patterns in hunting and gathering populations ( Smith 1983 ), though other topics were also represented from the outset ( Cronk 1991 ). The focus on foragers was due to the evolutionary antiquity of this mode of subsistence, as well as these being the populations in which optimal foraging theory was most straightforwardly applicable. However, there is no reason in principle for HBE research to be restricted to such populations. The emphasis in HBE is on human adaptability; humans have mechanisms of adaptive learning and plasticity by virtue of which they can rapidly find adaptive solutions to living in many kinds of environments. Thus, we might expect their behavior to be adaptively patterned in societies of all kinds, not just the types of human society, which have existed for many millennia.

The first phase of HBE lasted through the 1980s ( Borgerhoff Mulder 1988 ). In the second phase, the 1990s, HBE grew rapidly, with Winterhalder and Smith (2000) estimating that there were nearly 300 studies published during the decade. Its focus broadened to encompass more studies from nonforaging subsistence populations, such as horticulturalists and pastoralists (e.g., Borgerhoff Mulder 1990 ), and the use of historical demographic data (e.g., Voland 2000 ; Clarke and Low 2001 ). There were also some pioneering forays into the BE of industrialized populations ( Kaplan 1996 ; Wilson and Daly 1997 ). The 1990s were characterized by an increasing emphasis on topics which fall under the general headings of distribution (cooperation and social structure) and particularly reproduction (mate choice, mating systems, reproductive decisions, parental investment), rather than production (foraging). Anthropologists continued to dominate HBE, and the methodologies of the studies reflect this: many of the studies represented the field observations of a single field researcher from a single population, usually a single site. Having briefly outlined what HBE is and where it came from, we now turn to reviewing the HBE research that has appeared in the years since the publication of Winterhalder and Smith (2000) .

Our objective was to ascertain what empirical research has been done within the HBE paradigm since 2000, and characterize its key features, quantitatively where possible. We thus conducted a systematic search of 17 key journals for papers published between the beginning of 2000 and late 2011, which clearly belong in the HBE tradition (see Supplementary material for full methodology). This involved some contentious decisions about how to draw the boundaries of HBE and in the end, we drew it narrowly, including only papers containing quantitative data on naturally occurring behavior in human populations and employing a clearly adaptive perspective. This excludes a large number of studies that take an adaptive perspective but measure hypothetical preferences or decisions in experimental scenarios. It also excludes many studies that focus on nonbehavioral traits such as stature or physical maturation. The sample is not exhaustive even of our chosen subset of HBE, given that some HBE research is published in edited volumes, books, or journals other than those we searched. However, we feel that our strategy provides a good transect through current research, which is prototypically HBE, and the sampling method is at least repeatable and self-consistent over time.

We used the full text of the papers identified to code a number of key variables relevant to our review, including year of publication, journal, first author country of affiliation, and first author academic discipline. We also adopted Winterhalder and Smith’s (2000) ternary classification of topics into production (foraging and other productive activity), distribution (resource sharing, cooperation, social structure), and reproduction (mate choice decisions, sexual selection, life-history decisions, parental and alloparental investment). Finally, we coded the presence of some key features we wished to examine: the presence of any data from foraging populations, the presence of any data from industrialized populations, the use of secondary data, and the use of comparative data from more than one population.

The search resulted in a database of 369 papers (see Supplementary material for reference list and formal statistical analysis; an endnote library of the references of the papers in the database is also available from the corresponding author). The distribution of papers across journals is shown in Table 1 , which also shows the median year of publication of a paper in that journal. The overall median year of publication for the full sample was 2007; thus, the table can be used to identify those journals that carried HBE papers disproportionately earlier in the study interval (e.g., American Anthropologist , median 2004), and those which carried them disproportionately more recently (e.g., American Journal of Human Biology , median 2009). The total number of papers found per year increased significantly over the 12 years sampled, from around 20 at the beginning to nearly 50 in 2011 ( Figure 1a ; regression analysis suggests an average increase of 2.4 papers per year). In the Supplementary material , we show that HBE papers also increased as a proportion of all papers published in our target journals. First authors were affiliated with institutions in 28 different countries, with 57.5% based in the United States and 20.1% in the United Kingdom. In terms of discipline, anthropology (including archaeology) was strongly represented (49.9% of papers), followed by psychology (19.5%) and biology (12.7%). The remaining papers came from demography (3.3%), medicine and public health (3.0%), sociology and social policy (2.4%), economics and political science (2.2%), or were for various reasons unclassifiable (7.0%). However, the growth in number of papers over time was due to increasing HBE activity outside anthropology ( Figure 1a ). In 2000–2003, 64.0% of papers were from anthropology departments, whereas by 2009–2011, this figure was 47.4%. Our search strategy may, if anything, have underestimated the growth in HBE research from outside anthropology, because our search strategy was based on the journals that had carried important BE or HBE research prior to 2000 and did not include any specialist journals from disciplines such as demography or public health.

Numbers and percentages of papers in the database by journal. Also shown is the median year of publication of an HBE paper in the sample in that journal

a Formerly Journal of Cultural and Evolutionary Psychology .

b Targeted search only; for all other journals, all abstracts read.

Number of published papers identified by year over the study period (a) by disciplinary affiliation of first author; (b) by type of study population (other = agriculturalist, pastoralist, horticulturalist, or multiple types); (c) by tripartite classification of topic.

In terms of type of population studied, 80 papers (21.7%) contained some data from foragers, broadly defined to include any subsistence population for whom foraging forms a substantial part of the diet. One hundred and forty-five papers (39.3%) contained data from industrialized populations. The remainder of papers studied either contemporary or historical agricultural, horticultural, and pastoral populations. As Figure 1b shows, the amount of work on industrialized populations has tended to increase over time, with 22 such papers in 2000–2002 (29.3% of total) and 58 in 2009–2011 (43.0%). By contrast, the amount of work on forager populations is much more stable (20 papers [26.7%] in 2000–2002, 27 papers [20.0%] in 2009–2011). As for topic, we classified 64.8% of our papers as concerning reproduction, with 9.5% concerning production and 13.3% distribution. The remaining 12.5% either spanned several topics or fit none of the 3 categories. Table 2 gives some examples of popular research questions addressed in each of the 3 topic areas. The preponderance of reproduction has increased over time ( Figure 1c ); in 2000–2002, 53.3% of the papers fell into this category, whereas by 2009–2011, it was 68.9%. In fact, the growth of HBE papers during the study period has been completely driven by an increase in papers on reproductive topics (see Supplementary material ). We classified papers according to whether they involved analysis of secondary data sets gathered for other purposes. The number of papers involving such secondary analysis increased sharply through the study period, whereas those involving primary data did not (see Supplementary material ). Comparative analyses also increased significantly over time, but not faster than the overall growth in paper numbers.

Some examples of popular research questions in our database of recent HBE papers

To summarize, the data suggest that HBE has changed measurably in the period since 2000. Some of the changes in this period represent continuations of trends already incipient before, such as the expansion away from foraging and foragers toward reproduction and other types of population ( Winterhalder and Smith 2000 ). Our analysis suggests that it is primarily research into the BE of industrialized societies, which has expanded in the subsequent years, such that over 40% of HBE research published in the most recent 3-year period was conducted on such populations. More “traditional” HBE studies of foraging and small-scale food producing societies have continued, but only at a modestly increased rate compared with the 1990s. An unexpected feature of HBE post-2000 is the expansion of HBE in disciplines outside anthropology. Much of the growth has come from the adoption of HBE ideas by researchers based in departments of psychology, and, to a modest extent, other social sciences such as demography, public health, economics, and sociology. This is concomitant with the increasing focus on large-scale industrialized societies, as well as changes in methodology. Anthropologists often work alone or in small teams to gather special-purpose, opportunistic data sets from a particular field site, and many of the pioneering HBE studies were done in this way. In demography, public health, and sociology, by contrast, research tends to be based on very large, systematically collected, representative data sets, such as censuses, cohort, and panel studies, which are designed with multiple purposes in mind. Particular researchers can then interrogate them secondarily to address their particular questions. As HBE has welcomed more researchers from these other social sciences, it has also adopted these secondary methods more strongly (see section “Strengths” for further discussion). We also note the increase in the number of comparative studies. Comparative methods (albeit usually comparing related species rather than populations of the same species) have been a strong feature of BE since the outset (or before, Cullen 1957 ), and thus this is a natural development for HBE. HBE comparative studies use existing cross-cultural databases ( Quinlan 2007 ), integrate multiple ethnographic or historical sources ( Brown et al. 2009 ), or, increasingly, coordinate researchers to collect or derive standardized measures across multiple populations ( Walker et al. 2006 ; Borgerhoff Mulder et al. 2009 ). Comparative studies have become more powerful in their analytical strategies (see section “Strengths”).

The literature review in section “A systematic overview of current research” allowed us to characterize current HBE research and show some of the ways it has changed in the last decade. In this section, we discuss what we see as the strengths, weaknesses, opportunities, and open questions for HBE as a paradigm. This is inevitably more of a personal assessment than the preceding sections, and we appreciate that not everyone in the field will share our views.

The first obvious strength of HBE is vitality . As Darwinians, it comes naturally to us to assume that something that is increasing in frequency has some beneficial features. Thus, the fact that the number of recognizably HBE papers per year found by our search strategy has doubled in a decade, and that there are more and more adopters outside of anthropology, indicates that a range of people find an HBE approach useful. Where does this utility spring from? In part, it is that HBE models tend to make very clear, a priori predictions motivated by theory. The same cannot be said of all other approaches in the human sciences, and, arguably, the more we complicate behavioral ecological models by including details about how proximate mechanisms work, the more this clarity tends to disappear. We return in section “Open questions” to the issue of whether agnosticism about mechanism can be justified, but we note here that a great strength of (and defense for) simple HBE models is that they so often turn out to be empirically fruitful, despite their simplicity. Whether we are considering when to have a first baby ( Nettle 2011 ), what the effects of having an extra child will be in different ecologies ( Lawson and Mace 2011 ), whether to marry polygynously, polyandrously, or monogamously ( Fortunato and Archetti 2010 ; Starkweather and Hames 2012 ), or which relatives to invest time and resources in ( Fox et al. 2010 ), predictions using simple behavioral ecological principles turn out to be useful in making sense of empirically observed diversity in behavior. HBE has also demonstrated the generality of certain principles, such as the fact that male culturally defined social success is positively associated with reproductive success in many different types of society, albeit that the slope of the relationship differs according to features of the social system ( Irons 1979 ; Kaplan and Hill 1985 ; Borgerhoff Mulder 1987 ; Hopcroft 2006 ; Fieder and Huber 2007 ; Nettle and Pollet 2008 ).

A related strength of HBE is its broad scope . HBE models can apply to many kinds of behavioral decision (in principle, all kinds) and in all kinds of society. It is relatively rare in the human sciences for the same set of predictive principles to apply to variation both within and between societies and to societies ranging from small-scale subsistence populations to large-scale industrial states, but HBE thinking about, for example, reproductive decisions has exactly this scope ( Nettle 2011 ; Sear and Coall 2011 ). This would be a strength indeed, even without the crucial additional feature that the explanatory principles invoked are closely related to those that can be applied to species other than our own. Thus, HBE brings a relative conceptual coherence to the study of human behavior, a study that has traditionally been spread across a number of different disciplines each with different conceptual starting points.

Another strength of HBE as we have defined it here is its relatively high ecological validity . Much psychological research into human behavior relies on hypothetical self-reports and self-descriptions, or contrived experimental situations ( Baumeister et al. 2007 ), and much of behavioral economics consists of artificial games whose relevance to actual allocation decisions outwith the laboratory has been questioned ( Levitt and List 2007 ; Bardsley 2008 ; Gurven and Winking 2008 ). Although human behavioral ecologists use such techniques as their purposes require, at the heart of HBE is still a commitment to looking at what people really do, in the environments in which they really live, as a central component of the endeavor. Furthermore, HBE’s focus on behavioral diversity means that it has studied a much wider range of populations than other approaches in the human sciences (see Henrich et al. 2010 ), and this has led to a healthy skepticism of simple generalizations about human universal preferences or motivations ( Brown et al. 2009 ). Measuring relationships between behavior and fitness-relevant outcomes across a broad range of environments, HBE has now amassed considerable evidence in favor of its core assumptions that context matters when studying the adaptive consequences of human behavior and that behavioral diversity arises because the payoffs to alternative behavioral strategies are ecologically contingent.

HBE is also characterized by increasing methodological rigor. The early phases of HBE were defined by exciting theoretical developments, as evolutionary hypotheses for human behavioral variation were first formulated and presented in the literature. However, conducting empirical studies capable of rigorously testing hypotheses derived from HBE theory presents a number of methodological challenges, not least because the human species is relatively long lived and rarely amenable to experimental manipulation. These challenges are now being increasingly overcome, as HBE expands its tool kit to include new sources of data, statistical methods, and study designs. As noted in the section “A systematic overview of current research,” recent years have witnessed an increased use of secondary demographic and social survey data sets, which often provide larger, more representative samples and a broader range of variables than afforded by field research. Some sources of secondary data have also enabled lineages to be tracked beyond the life span of any individual researcher, providing valuable new data on the correlates of long-term fitness (e.g., Lahdenpera et al. 2004 ; Goodman and Koupil 2009 ).

Statistical methods have also become more advanced. Multilevel analyses are now routinely used in HBE research to deal with hierarchically structured data and accurately partition sources of behavioral variance at different levels (e.g., within and between villages; Lamba and Mace 2011 ). Phylogenetic comparative methods, which utilize information on historical relationships between populations, have become popular for testing coevolutionary hypotheses since they were first applied to human populations in the early 1990s ( Mace and Pagel 1994 ; Mace and Holden 2005 ), though debate remains about their suitability for modeling behavioral transmission in humans ( Borgerhoff Mulder et al. 2006 ). Issues of causal inference are also being addressed with more sophisticated analytical techniques. For example, structural equation modeling and longitudinal methods such as event history analysis have enabled researchers to achieve greater confidence when controlling for potential cofounding relationships (e.g., Sear et al. 2002 ; Lawson and Mace 2009 ; Nettle et al. 2011 ). HBE researchers are also following wider trends in the social and natural sciences by exploring alternatives to classic significance testing, such as information-theoretic and Bayesian approaches for considering competing hypotheses ( Towner and Luttbeg 2007 ). Some researchers have also been able to harness “natural experiments” in situations where comparable populations or individuals are selectively exposed to socioecological change. For example, Gibson and Gurmu (2011) examined the effect of changes in land tenure (from family inheritance to government redistribution) on a population in rural Ethiopia, demonstrating that competition between siblings for marital and reproductive success only occurs when land is inherited across generations. These advancements represent an exciting and necessary step forward, as empirical methods “catch up” with the powerful theoretical framework set out in the early days of HBE.

Finally, HBE has shown itself capable of topical innovation. A pertinent recent example is cooperative breeding (typically loosely defined in HBE as the system whereby women receive help from other individuals in raising their offspring). The idea that human females might breed cooperatively had been around for several decades ( Williams 1957 ), and began to be tested empirically in the late 1980s and 1990s (e.g., Hill and Hurtado 1991 ), but it was the 21st century that saw a real upsurge in interest in this topic, leading to a revitalization of the study of kinship in humans ( Shenk and Mattison 2011 ). HBE has now mined many of the rich demographic databases available for our species to test empirically the hypothesis that the presence of other kin members is associated with reproductive outcomes such as child survival rates and fertility rates. These analyses typically find support for the hypothesis that women adopt a flexible cooperative breeding strategy where they corral help variously from the fathers of their children, other men, and pre- and postreproductive women ( Hrdy 2009 ).

Though we see HBE as a strong paradigm, there are some important weaknesses of its current research to be noted. The first is HBE’s relative isolation from the rest of BE. The core journals of BE are Behavioral Ecology and Behavioral Ecology and Sociobiology . Our search revealed only 8 HBE papers in these journals (2.2% of the sample). The vast majority of papers in our sample appeared in journals which never carry studies of species other than humans, and we know of rather few human behavioral ecologists who also work on other systems. West et al. (2011) have recently argued that evolutionary concepts are widely misapplied (or outdated understandings are applied, a phenomenon colloquially dubbed “the disco problem”) in human research, due to insufficient active integration between HBE and the rest of evolutionary biology.

HBE is clearly not completely decoupled from the rest of BE (see Machery and Cohen 2012 for quantitative evidence on this point). For example, within BE, there has been a decline in interest in foraging theory and a rise in interest in sexual selection ( Owens 2006 ), which are mirrored in the changes in HBE described in section “A systematic overview of current research.” Behavioral ecologists have also become less concerned with simply showing that animals make adaptive decisions, and more concerned with the nature of the neurobiological and genetic mechanisms underlying this ( Owens 2006 ). Parallel developments have occurred in the human literature, with the rise of adaptive studies of psychological mechanisms (see e.g., Buss 1995 ). Our search strategy did not include these studies, because their methodologies are different from those of “classical” HBE, but there is no doubt that they have increased in number. Finally, we note that there has been a recent increase in interest in measuring natural selection directly in contemporary human populations ( Nettle and Pollet 2008 ; Byars et al. 2010 ; Stearns et al. 2010 ; Milot et al. 2011 ; Courtiol et al. 2012 ). This anchors HBE much more strongly to evolutionary biology in general. Despite these developments, we see the isolation of HBE from the rest of biology as a potential risk. We hope to see more behavioral ecologists start to work on humans, and more projects across taxonomic boundaries, in the future.

Finally, we note the rather restricted topic base. HBE has had a great deal to say recently about mating strategies, reproductive decisions, fertility, and reproductive success, but much less about diet, resource extraction, resource storage, navigation, spatial patterns of habitat use, hygiene, social coordination, or the many other elements involved in staying alive. In part, this is because, as HBE expands to focus more on large-scale populations, it discovers that there are already disciplines (economics, sociology, human geography, public health) that deal extensively with these topics. It is in the general area of reproduction that it is easiest to come up with predictions that are obviously Darwinian and differentiate HBE from existing social science approaches. Nonetheless, the explanatory strategy of HBE is of potential use for any topic where behavioral effort has to be allocated in one way rather than another, and thus we would hope to see a broadening of the range of questions addressed as HBE continues to grow.

Opportunities

As HBE continues to expand, we see a major opportunity for HBE to build bridges to the social sciences. At the moment, most HBE papers are published in journals that only carry papers that take an adaptive evolutionary perspective, not general social science journals. Thus, HBE is possibly as separated from other approaches to human behavior as it is from parallel approaches to the behavior of other species. This may be because early proponents of HBE saw it as radically different from existing social science approaches to the same problems, by virtue of its generalizing hypothetico-deductive framework and commitment to quantitative hypothesis testing ( Winterhalder and Smith 2000 ). However, the social science those authors came into closest contact with was sociocultural anthropology, which is perhaps not a very typical social science (see Irons 2000 for an account of the hostile reception of HBE within sociocultural anthropology). As HBE’s expansion brings it into closer proximity with disciplines like economics, sociology, demography, public health, development studies, and political science, there may be more common ground than was previously thought. Social scientists are united in the notion that human behavior is very variable and that context is extremely important in giving rise to this variation. These are commitments that HBE obviously shares. Indeed, although it is still common in the human sciences for authors to rhetorically oppose “evolutionary” to “nonevolutionary” (or “social” and “biological”) explanations of the same problem as if these were mutually exclusive endeavors ( Nettle 2009a ), HBE defies such dichotomies adeptly.

Much of social science is highly quantitative and, generally lacking the ability to perform true experiments, relies on multivariate statistical approaches applied to observational data sets to test between competing explanations for behavior patterns. HBE is just the same, and indeed, since the millennium, has become much more closely allied to other social sciences, adopting the large-scale data resources they provide, as well as methodological tools like multilevel modeling, which they have developed to deal with these. HBE employs a priori models based on the individual as maximizer, a position not shared explicitly by all social sciences. However, this approach is widespread in economics and political science. Indeed, it was economics that gave it to BE. The big difference between HBE and much of social science is the explicit invocation of inclusive fitness (or its proxies) as the end to which behavior is deployed. This does not necessarily make it a competing endeavor, especially because what is measured in HBE is not usually fitness itself, but more immediate proxies. Rather, HBE models can often be seen as adding an explicitly ultimate layer of explanation, giving rise to new predictions and unifying diverse empirical observations, without being incompatible with existing, more proximate theories.

Indeed, our perception is that a number of social science theories make assumptions about the ends of behavior, which are quite similar to those of HBE, just not explicitly expressed in Darwinian terms; basically, people’s sets of choices are constrained by the environment in which they have to live, and they make the best choices they can given these constraints, often with knock-on effects that behavioral ecologists would describe as trade-offs. Examples include the work of Geronimus on how African American women adjust their patterns of childbearing to the prevailing rates of mortality and morbidity in their neighborhoods ( Geronimus et al. 1999 ), the work of Drewnowski and colleagues on how people adjust the type of foodstuffs they consume to the budgets they have to spend ( Drewnowski and Specter 2004 ; Drewnowski et al. 2007 ), or Downey’s work on the effects of increasing family size on socioeconomic outcomes of the children ( Downey 2001 ). If the introductory sections of any of these papers were written from a more explicitly Darwinian perspective, they would look perfectly at home in a BE journal. The breaking down of the social science–natural science divide has long been held as desirable, but is not easy to achieve in practice. HBE’s boundary with the social sciences may be one frontier where some progress can occur. Social scientists have long lamented the fragmentation of their field into multiple disciplinary areas with little common ground (e.g., Davis 1994 ). Given HBE’s broad scope and general principles, it has the potential to serve as something of a lingua franca across social scientists working on different kinds of problems.

A related opportunity for HBE is the potential for applied impact . HBE models have the potential to provide new and practical insights into contemporary world issues, from natural resource management ( Tucker 2007 ) to the consequences of inequality within developed populations ( Nettle 2010 ). The causes and consequences of recent human behavioral and environmental changes (including urbanization, economic development, and population growth) are recurring themes in recent studies in HBE. The utility of an ecological approach is clearly demonstrated in studies exploring the effectiveness of public policies or intervention schemes seeking to change human behavior or environments. HBE models clarify that human behavior tends to be deployed in the service of reproductive success, not financial prudence, health, personal or societal wellbeing ( Hill 1993 ), an important insight that differs from some economic or psychological theories. By providing insights into ultimate motivations and proximate pathways to human behavioral change, HBE studies can sometimes offer direct recommendations for the design and implementation of future initiatives ( Gibson and Mace 2006 ; Shenk 2007 ; Gibson and Gurmu 2011 ). Addressing contemporary world issues does, however, present methodological and theoretical challenges for HBE, requiring more explicit consideration of how research insights may be translated into interventions and communicated to policymakers and users ( Tucker and Taylor 2007 ).

Open questions

An open question for HBE is how the study of mechanism can be integrated into functional enquiry. This is an issue for BE generally, not just the human case. As mentioned in the section “What is HBE?”, BE has tended to proceed by the behavioral gambit—the assumption that the nature of the proximate mechanisms underlying behavioral decisions is not important in theorizing about the functions of behavior. It is important to understand the status of the behavioral gambit because it has sometimes been unfairly criticized (see Parker and Maynard Smith 1990 ). In the natural world, individuals do not always behave optimally with respect to any particular decision because there are phylogenetic or mechanistic constraints on their ability to reach adaptive solutions. However, in general terms, the only way to discover the existence of such departures from optimality is to have a theoretical model that shows what the optimal behavior would be and to test empirically whether individual behavior shows the predicted pattern. Where it does not, this may point to unappreciated constraints or trade-offs and thus shed light on the biology of the organism under study. Thus, the use of the term gambit is entirely apt; the behavioral gambit is a way of opening the enquiry designed to gain some advantage in the quest to understand. It is not the end game.

Where there is no sizable departure from predicted optimality, the ultimate adaptive explanation does not depend critically on understanding the mechanisms. This does not mean the question of mechanism is unimportant, of course; mechanistic explanations must still be sought and integrated with functional ones. This is beginning to occur in some cases. In the field of human reproductive ecology, the physiological mechanisms involved in adaptive strategies are beginning to be understood ( Kuzawa et al. 2009 ; Flinn et al. 2011 ), and there is also increasing interchange between HBE researchers and experimentalists studying psychological mechanisms ( Sear et al. 2007 ), which is clearly a development to be welcomed.

Where there is a patterned departure from optimality, understanding the mechanism becomes more critical. Aspects of mechanism can then be modeled as additional constraints, which may explain the strategies individuals pursue. For example, Kacelnik and Bateson (1996) showed that the pattern of risk aversion for variability in food amount and risk proneness for variability in food delay is not predicted by optimal foraging theory, except when Weber’s law (the principle that perceptions of stimulus magnitude are logarithmically, not linearly, related to actual stimulus magnitude) is incorporated into models as a mechanistic constraint. At a deeper level, though, this just raises further questions. Why should Weber’s law have evolved, and once it has evolved, can selection relax it for any particular task? These are what McNamara and Houston call “evo-mecho” questions ( McNamara and Houston 2009 ). Departures from optimality in one particular context raise such questions pervasively. Issues such as the robustness, neural instantiability, efficiency, and developmental cost of different kinds of mechanisms become salient here, and many apparently irrational quirks of behavior become interpretable as side effects of evolved mechanisms whose overall benefits have exceeded their costs over evolutionary time ( Fawcett et al. 2012 ). However, we would still argue that the best first approximation in understanding a question is to employ the behavioral gambit to generate and test simple optimality predictions, even though an understanding of mechanism will be essential for explaining why these may fail.

Although the issue of how incorporation of mechanism changes the predictions of BE models is a general one, in the human case, it has been discussed in particular with reference to transmitted culture because this is a class of mechanism on which humans are reliant to a unique extent ( Richerson and Boyd 2005 ). Transmitted culture refers to the behavioral traditions that arise from repeated social learning. Social learning can be an evolutionarily adaptive strategy, and the equilibrium solutions reached by it will often be the fitness-maximizing ones under reasonable assumptions ( Henrich and McElreath 2003 ). After all, if reliance on culture on average led to maladaptive outcomes, there would be strong selection on humans to rely on it less. Indeed, there is evidence that humans tend to forage efficiently for socially acquired information, using it when it is adaptive to do so ( Morgan et al. 2012 ). Thus, we would argue that culture can be treated, to a first approximation, just like any other proximate mechanism: that is, it can be set aside in the initial formulation of functional explanations ( Scott-Phillips et al. 2011 , though see Laland et al. 2011 for a different view). As an example, we could take Henrich and Henrich’s (2010) data on food taboos for pregnant and lactating women in Fiji. These authors show that the taboos reduce women’s chances of fish poisoning by 30% during pregnancy and 60% during breastfeeding and thus are plausibly adaptive. The fact that in this case it is culture by which women acquire them, rather than genes or individual learning, does not affect this conclusion or the data needed to test it. However, the quirks of how human social learning works may well explain some nonadaptive taboos that are found alongside the adaptive ones, which are in effect carried along by the generally adaptive reliance on social learning. Thus, although the behavioral gambit can be used to explain the major adaptive features of these taboos, an understanding of the cultural mechanisms is required to explain the details of how the observed behavior departs in subtle ways from the optimal pattern. Culture may often lead to maladaptive side effects in this way ( Richerson and Boyd 2005 ). Although its general effect is to allow humans to rapidly reach adaptive equilibria, nonadaptive traits can be carried along by it, and, compared with other proximate mechanisms, it produces very different dynamics of adaptive change.

A final open question is the extent of human maladaptation. Humans have increased their absolute numbers by orders of magnitude and colonized all major habitats of the planet, so they are clearly adept at finding adaptive solutions to the problem of living. However, there are also some clear cases of quite systematic departures from adaptive behavior. Perhaps most pertinently, the low fertility rate typical of industrial populations still defies a convincing adaptive explanation, despite being a longstanding topic for HBE research (see Borgerhoff Mulder 1998 ; Kaplan et al. 2002 ; Shenk 2009 ). There are patterns in the fertility of modernizing populations, which can be readily understood from an HBE perspective: parents in industrialized populations who have large families suffer a cost to the quality of their offspring, particularly with regard to educational achievement and adult socioeconomic success, so there is a quality–quantity trade-off ( Lawson and Mace 2011 ). Moreover, the reduction in fertility rate is closely associated with improvement in the survival of offspring to breed themselves, so that, as the transition to small families proceeds, the probability of having at least one grandchild may remain roughly constant ( Liu and Lummaa 2011 ). However, despite all this, it remains the case that people in affluent societies could still have many more grandchildren and great-grandchildren by having more children, and yet they do not ( Goodman et al. 2012 ). Any explanation of the demographic transition must, therefore, invoke some kind of maladaptation or mismatch between the conditions under which decision-making mechanisms evolved and those under which they are now operating.

Our review has shown that HBE is a growing and rapidly developing research area. The weaknesses of HBE mostly amount to a need for more research activity, and the unresolved questions, though important, do not in our view undermine HBE’s core strengths of theoretical coherence and empirical utility. HBE is being applied to more questions in more human populations with better methods than ever before. Our hope is that HBE will inspire more behavioral biologists to work on humans, for whom a wealth of data is available, and more social scientists to adopt an adaptive, ecological perspective on their behavioral questions, thus adding a layer of deeper explanations, as well as generating new insights.

Supplementary material can be found at Supplementary Data

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Computer Science > Human-Computer Interaction

Title: generative agents: interactive simulacra of human behavior.

Abstract: Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.

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Article contents

Habit formation and behavior change.

• Benjamin Gardner Benjamin Gardner Department of Psychology, King's College London
•  and  Amanda L. Rebar Amanda L. Rebar Department of Human, Health, and Social Sciences, Central Queensland University
• https://doi.org/10.1093/acrefore/9780190236557.013.129
• Published online: 26 April 2019

Within psychology, the term habit refers to a process whereby contexts prompt action automatically, through activation of mental context–action associations learned through prior performances. Habitual behavior is regulated by an impulsive process, and so can be elicited with minimal cognitive effort, awareness, control, or intention. When an initially goal-directed behavior becomes habitual, action initiation transfers from conscious motivational processes to context-cued impulse-driven mechanisms. Regulation of action becomes detached from motivational or volitional control. Upon encountering the associated context, the urge to enact the habitual behavior is spontaneously triggered and alternative behavioral responses become less cognitively accessible.

By virtue of its cue-dependent automatic nature, theory proposes that habit strength will predict the likelihood of enactment of habitual behavior, and that strong habitual tendencies will tend to dominate over motivational tendencies. Support for these effects has been found for many health-related behaviors, such as healthy eating, physical activity, and medication adherence. This has stimulated interest in habit formation as a behavior change mechanism: It has been argued that adding habit formation components into behavior change interventions should shield new behaviors against motivational lapses, making them more sustainable in the long-term. Interventions based on the habit-formation model differ from non-habit-based interventions in that they include elements that promote reliable context-dependent repetition of the target behavior, with the aim of establishing learned context–action associations that manifest in automatically cued behavioral responses. Interventions may also seek to harness these processes to displace an existing “bad” habit with a “good” habit.

Research around the application of habit formation to health behavior change interventions is reviewed, drawn from two sources: extant theory and evidence regarding how habit forms, and previous interventions that have used habit formation principles and techniques to change behavior. Behavior change techniques that may facilitate movement through discrete phases in the habit formation trajectory are highlighted, and techniques that have been used in previous interventions are explored based on a habit formation framework. Although these interventions have mostly shown promising effects on behavior, the unique impact on behavior of habit-focused components and the longevity of such effects are not yet known. As an intervention strategy, habit formation has been shown to be acceptable to intervention recipients, who report that through repetition, behaviors gradually become routinized. Whether habit formation interventions truly offer a route to long-lasting behavior change, however, remains unclear.

• automaticity
• behavior change
• dual process

What Are Habits and Habitual Behaviors ?

Everyday behaviors shape human health. Many of the dominant causes of death, including heart disease, diabetes, cancer, chronic lower respiratory diseases, and stroke, are preventable (World Health Organization, 2017 ). Adopting health-promoting behaviors such as eating more healthily or increasing physical activity may improve quality of life, physical and mental health, and extend lives (Aune et al., 2017 ; Centers for Disease Control and Prevention, 2014 ; Rebar et al., 2015 ; World Health Organization, 2015 ). For some behaviors, one performance is sufficient to attain desired health outcomes; a single vaccination, for example, can yield immunity to disease (e.g., Harper et al., 2004 ). For many behaviors, however, achieving meaningful health outcomes depends on repeated performance: Going for a run once, for example, will not achieve the same health benefits as regular activity over a prolonged period (Erikssen et al., 1998 ). In such instances, behavior change must be viewed as a long-term process, which can be conceptually separated into stages of initiation and maintenance (Prochaska & DiClemente, 1986 ; Rothman, 2000 ). This distinction is important from a practical perspective because while people may possess the capability, opportunity, and motivation to initiate behavior change (Michie, van Stralen, & West, 2011 ), they often fail to maintain it over time, lapsing back into old patterns of behavior (Dombrowski, Knittle, Avenell, Araujo-Soares, & Sniehotta, 2014 ). Some have attributed this to changes in motivation after initial experiences of action (Armitage, 2005 ; Rothman, 2000 ). People may overestimate the likelihood of positive outcomes or the valence of such outcomes, or they may fail to anticipate negative outcomes (Rothman, 2000 ). Alternatively, a newly adopted behavior may lose value and so become deprioritized over time. Motivation losses threaten to derail initially successful behavior change attempts.

Habit formation has attracted special attention as a potential mechanism for behavior change maintenance (Rothman, Sheeran, & Wood, 2009 ; Verplanken & Wood, 2006 ) because habitual behaviors are thought to be protected against any dips in conscious motivation. Viewing habit as a means to maintenance may seem truistic; in everyday discourse, a habit is an action done repetitively and frequently, and so making action habitual will necessarily entail maintenance. Within psychology, however, the term habit denotes a process whereby exposure to a cue automatically triggers a non-conscious impulse to act due to the activation of a learned association between the cue and the action (Gardner, 2015 ). Habit is learned through “context-dependent repetition” (Lally, van Jaarsveld, Potts, & Wardle, 2010 ): Repeated performance following exposure to a reliably co-occurring cue reinforces mental cue-action associations. As these associations develop, the habitual response gradually becomes the default, with alternative actions becoming less cognitively accessible (Danner, Aarts, & de Vries, 2008 ). Habit is formed when exposure to the cue is sufficient to arouse the impulse to enact the associated behavior without conscious oversight (Gardner, 2015 ; Neal, Wood, Labrecque, & Lally, 2012 ; Wood, Labrecque, Lin, & Rünger, 2014 ). In the absence of stronger influences favoring alternative actions, the habit impulse will translate smoothly and non-consciously into action, and the actor will experience behavior as directly cued by the context (Wood & Neal, 2007 ).

Defining habit as a process that generates behavior breaks with earlier definitions, which depicted habit as a form of behavior (see Gardner, 2015 ). This definition of habit as a process resolves a logical inconsistency that arises from portraying habit as a determinant of behavior (e.g., Hall & Fong, 2007 ; Triandis, 1980 ); as Maddux ( 1997 , pp. 335–336) noted, “a habit cannot be both the behavior and the cause of the behavior.” It also allows for the habit process to manifest in multiple ways for any behavior. A distinction has been drawn between habitually instigated and habitually executed behavior (Gardner, Phillips, & Judah, 2016 ; Phillips & Gardner, 2016 ). Habitual instigation refers to habitual triggering of the selection of an action and a non-conscious commitment to performing it upon encountering a cue that has consistently been paired with the action in the past. Habitual execution refers to habit facilitating completion of the sub-actions that comprise any given action such that the cessation of one action in a sequence automatically triggers the next. Take, for example, “eating a bag of chips.” While people typically mentally represent this activity as a single unit of action (Wegner, Connally, Shearer, & Vallacher, 1983 , cited in Vallacher & Wegner, 1987 ), it can be deconstructed into a series of discrete sub-actions (e.g., “opening bag,” “putting hand in bag,” “putting food in mouth,” “chewing,” “swallowing”; Cooper & Shallice, 2000 ). “Eating a bag of chips” is habitually instigated to the extent that the actor is automatically cued to select “eating chips” from available behavioral options. This may also activate the first sub-action in the sequence (“opening bag”). “Eating a bag of chips” is habitually executed to the extent that the cessation of, for example, “putting my hand in the bag” habitually cues “putting food in mouth,” the cessation of which habitually cues “chewing,” and so on, until the perceptually unitary action (“eating a bag of chips”) is complete. 1 The term habitual behavior describes any action that is either instigated or executed habitually. This includes actions that are habitually instigated but non-habitually executed (e.g., habitually triggered to begin eating a bag of chips, but deliberates about how many chips to put in mouth), non-habitually instigated but habitually executed (e.g., consciously decides to eat a bag of chips, but habitually puts the chips in mouth, chews, and swallows), or both habitually instigated and habitually executed (e.g., habitually starts eating chips, and habitually puts them in mouth, chews, and swallows; Gardner, 2015 ). This description allows for a behavior to be habitual, yet not fully automated (see Aarts, Paulussen, & Schaalma, 1997 ; Marien, Custers, & Aarts, 2019 ) and better resonates with everyday experiences of complex health behaviors such as physical activity, which may be partly habit-driven, yet also require conscious oversight to be successfully completed (Rhodes & Rebar, 2019 ).

Habit has been implicated in behaviors across a range of domains, including media consumption (LaRose, 2010 ), purchasing patterns (Ji & Wood, 2007 ), environmentally relevant actions (Kurz, Gardner, Verplanken, & Abraham, 2014 ), and health behaviors. Studies have pointed to a multitude of health-related actions that may potentially be performed habitually, including dietary consumption (Adriaanse, Kroese, Gillebaart, & De Ridder, 2014 ), physical activity (Rebar, Elavsky, Maher, Doerksen, & Conroy, 2014 ), medication adherence (Hoo, Boote, Wildman, Campbell, & Gardner, 2017 ), handwashing (Aunger et al., 2010 ), and dental hygiene (Wind, Kremers, Thijs, & Brug, 2005 ). Habit strength is consistently found to correlate positively with behavioral frequency (Gardner, de Bruijn, & Lally, 2011 ; Rebar et al., 2016 ) and may bridge the “gap” between intention and behavior, though there are varying accounts regarding interplay between habits and intentions in regulating behavior. Some have argued that people are more likely to act on intentions when they have habits for doing so (Rhodes & de Bruijn, 2013 ). When motivation is momentarily low upon encountering associated contexts, habit may translate into performance despite motivational lapses. In this way, habit has been proposed to represent a form of self-control, protecting regularly performed behaviors that are desired in the longer-term against shorter-term motivation losses (Galla & Duckworth, 2015 ). Other studies have suggested that habit can direct action despite intentions not to act (Neal, Wood, Wu, & Kurlander, 2011 ; Orbell & Verplanken, 2010 ; but see Rebar et al., 2014 ). For example, one study showed that United Kingdom smokers with habits for smoking while drinking alcohol reported “action slips” after the introduction of a smoking ban in public houses; despite intending to adhere to the ban, several reporting “finding themselves” beginning to light up cigarettes while consuming alcohol (Orbell & Verplanken, 2010 ). These two perspectives concur in highlighting the potential for habit to override conscious motivational tendencies. Such effects may be attributable to habitual instigation rather than execution (Gardner et al., 2016 ); someone who is habitually prompted to act is more likely to frequently perform those actions and to do so without relying on intention.

The effects of habit—or more specifically, instigation habit (Gardner et al., 2016 )—have important implications for behavior maintenance. By virtue of their cue-dependent, automatic nature (Orbell & Verplanken, 2010 ), habitually instigated behaviors should, in theory, persist even when they no longer serve the goal that initially motivated performance, or where motivation has eroded (Wood & Neal, 2007 ). For example, a person starting a new job out of town may consistently decide to commute by bicycle, which will likely create a habit for bicycle commuting whereby the workday morning context automatically prompts bicycle use without any deliberation over available alternatives (Verplanken, Aarts, Knippenberg, & Moonen, 1998 ). This may, however, lead to instances whereby the commuter “accidentally” uses the bicycle out of habit, despite, for example, knowing of road closures that will slow the journey and which would render alternative transport modes preferable (see Verplanken, Aarts, & Van Knippenberg, 1997 ). This example demonstrates several key features of habitual responses: learning via consistent pairing of cues (e.g., 8 a.m. on a workday) and action (selecting the bicycle); cue-dependent automaticity (using the bicycle at 8 a.m. on a workday without deliberation); and goal-independence, persisting even where an actor no longer has the motivation to act or is motivated to act in another way (e.g., when roads are closed). It also demonstrates how habit formation can maintain behavior by “locking in” new behaviors, protecting them against losses in conscious motivation. Habit development may also play a useful role in cessation of unwanted behaviors. Many ingrained behaviors—for example, eating high-calorie snacks—persist because they have become habitual and so are difficult to change. The lack of reliance on conscious intentions that is characteristic of habitual behavior, and which is thought to protect new behaviors against motivation losses, makes it difficult to break unwanted habits despite strong intentions to do so (Webb & Sheeran, 2006 ). While habit formation per se is not a sufficient strategy for “giving up” an unwanted behavior, behavior change can be made easier by seeking to form a new (“good”) habit in place of the old (“bad”) habit, rather than attempting only to inhibit the unwanted action (Adriaanse, van Oosten, de Ridder, de Wit, & Evers, 2011 ). Indeed, in the real world, habit development often involves displacing existing actions with more desirable alternatives such as eating healthy snacks in place of higher-calorie foods (Lally, Wardle, & Gardner, 2011 ; McGowan et al., 2013 ). Such “habit substitution” can take one of two basic forms, involving either avoidance of cues to the unwanted action or the development of new responses that compete with the unwanted habitual response. The “habit discontinuity hypothesis” speaks to the former of these, arguing that naturally occurring disruption of contexts—such as a residential relocation, for example—discontinues exposure to old habit cues (Walker, Thomas, & Verplanken, 2015 ). This represents an opportunity for people to act on their conscious motivation in response to newly encountered cues, and so to develop new, potentially more desirable habitual responses such as using active travel modes in place of more sedentary travel options like driving (Verplanken & Roy, 2016 ). Bad habits offer established cue-response structures that can hasten learning of new, good habits. Thus, where discontinued cue exposure is not feasible, people may seek to develop new cue-behavior associations to compete with and ultimately override old associations (Bouton, 2000 ; Walker et al., 2015 ). For example, people wishing to reduce habitual unhealthy snacking may form plans that dictate that when they are watching television and wish to snack (cue), they will eat fruit (new, desired behavior) instead of high-calorie foods (undesired, habitual behavior; e.g., Adriaanse, Gollwitzer, De Ridder, De Wit, & Kroese, 2011 ). In both instances of discontinued cue exposure and the adoption of competing responses to existing cues, the development of new habit associations and the decaying (or deprioritizing) of old habit associations are thought to occur concurrently (Adriaanse et al., 2011 ; Walker et al., 2015 ; Wood & Neal, 2007 ).

How Does Habit Form?

There have been calls for habit formation, whether focused solely on establishing new actions or displacing unwanted actions, to be adopted as an explicit goal for behavior change interventions (Rothman et al., 2009 ; Verplanken & Wood, 2006 ). Developing effective habit formation interventions requires an understanding of how habit forms.

The concept of behavior as an automatic response to covarying contextual cues, directed by learned cue-action associations, is rooted in behaviorist principles and studies of animal learning (e.g., Hull, 1943 ; Skinner, 1938 ; Thorndike, 1911 ). For example, in his maze-learning studies, Tolman ( 1932 ) noted that his rats, having repeatedly run down the route at the end of which was a food reward, continued to pursue that route even when the reward was removed. Adams ( 1982 ) trained rats to press a lever in a cage so as to receive intermittently delivered sucrose pellets. After receiving a lithium chloride injection that caused ingestion of the sucrose to induce nausea, those rats that were more highly trained (i.e., had pressed and received the sucrose reward a greater number of times in the training phase) were likely to persist longer in pressing the lever. Of course, unlike rats, humans possess the cognitive capacity to anticipate and reflect on their actions, and health-related behaviors among humans are inherently more complex than selecting maze routes or pressing levers. Yet, homologous neural processes are implicated in the acquisition and practice of habitual responses in rats and humans (Balleine & O’Doherty, 2010 ), and, like rats, people can acquire habitual behavioral responses despite a lack of insight into those behaviors or the associations that govern their performance (Bayley, Frascino, & Squire, 2005 ).

The route to human habit formation is conceptually simple: A behavior must be repeatedly performed in the presence of a cue or set of cues (i.e., context) so that cue-behavior associations may develop. For behaviors that are initially purposeful and goal-directed, the habit-formation process represents a period of transition whereby behavioral regulation transfers from a reflective and deliberative processing system to an impulsive system, which generates action rapidly and automatically based solely on activation of associative stores of knowledge (Strack & Deutsch, 2004 ). While there has been much lab-based research into the learning of relatively simple habitual responses in humans (e.g., button pressing; Webb, Sheeran, & Luszczynska, 2009 ), only relatively recently have studies focused on formation of real-world health-related habits (Fournier et al., 2017 ; Judah, Gardner, & Aunger, 2013 ; Lally et al., 2010 ). This work has largely been facilitated by the development of the Self-Report Habit Index (SRHI; Verplanken & Orbell, 2003 ), which affords reflections on the “symptoms” of habit, such as repetitive performance, mental efficiency, and lack of awareness.

Lally et al.’s ( 2010 ) seminal habit formation study used an SRHI sub-scale to assess the trajectory of the relationship between repetition and habit development among 96 participants for a 12-week period. They were instructed to perform a self-chosen physical activity or diet-related behavior (e.g., “going for a walk”) in response to a naturally occurring once-daily cue (e.g., “after breakfast”). Each day, they reported whether they had performed the action on the previous day, and if so, rated the experienced automaticity of its performance. Habit development within individuals was found to be most accurately depicted by an asymptotic curve, with early repetitions achieving sharpest habit gains, which later slowed to a plateau. The level at which habit peaked differed across participants, with some reportedly attaining scores at the high end of the automaticity index and others peaking below the scale mean. This plateau was reached at a median of 66 days post-baseline, though there was considerable between-person variation in the time taken to reach the plateau (18–254 days, the latter a statistical forecast assuming continued performance beyond the study period). These findings were echoed in a study of adoption of a novel stretching behavior (Fournier et al., 2017 ). Once-daily performance was found to yield asymptotic increases in self-reported habit strength. Habit plateaued at a median of 106 days for a group that performed the stretch every morning upon waking, and 154 days for those who stretched in the evening before bed, which the authors interpreted as evidence of the role of cortisol (which naturally peaks in the morning) in habit learning.

These studies reveal that habit development is not linear; if this were so, the fourth repetition of a behavior would have the same reinforcing impact on habit as would, say, the 444th. Rather, the asymptotic growth curve demonstrates that initial repetitions have the greatest impact on habit development. This in turn demands that the habit formation process be broken down into discrete phases and that the early phase, characterized by the sharpest gains in automaticity, may be a critical period during which people require most support to sustain motivation before the action becomes automatic (Gardner, Lally, & Wardle, 2012 ). Lally and Gardner ( 2013 ) have proposed a framework that organizes habit formation (and substitution) into four interlinked phases (see also Gardner & Lally, 2019 ). It argues that, for new behaviors initially driven by conscious motivation, habit forms when a person (1) makes a decision to act and (2) acts on his or her decision (3) repeatedly, (4) in a manner conducive to the development of cue-behavior associations. Phases 1 and 2 may be taken together to represent pre-initiation, occurring before the first enactment of the new behavior, whereas phases 3 and 4 are post-initiation phases, addressing the motivational and volitional elements needed to sustain behavior after initial performance (phase 3) and the effect of repetition on habit associations (phase 4) (see also Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ; Rothman, 2000 ). Phase 3 captures the critical period after initiation but before habit strength has peaked (Fournier et al., 2017 ; Lally et al., 2010 ).

The framework is not intended as a theory or model of the habit formation process, but rather as a means to conceptually organize the processes and mechanisms that underpin habit development. According to the framework, any variable can promote habit formation in one or more of four ways: It may enhance motivation (phase 1) or action control (i.e., the enactment of intentions into behavior; Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ) (phase 2) so as to initiate the behavior; it may modify motivation and other action control processes to continue to perform the behavior (phase 3); or it may strengthen cue-behavior associations (phase 4). One variable may operate through multiple processes: For example, anticipating pleasure from action can motivate people to perform it for the first time (phase 1) and to continue to perform it (phase 3) (Radel, Pelletier, Pjevac, & Cheval, 2017 ; Rothman et al., 2009 ). The experience of pleasure can also quicken learning of cue-behavior associations (phase 4) (de Wit & Dickinson, 2009 ). By extension, Lally and Gardner’s ( 2013 ) framework categorizes techniques that promote habit formation according to their likely mechanism (or mechanisms) of action; techniques may enhance motivation (phase 1) or action control (phase 2) to initiate change, sustain motivation and action control over time (phase 3), or reinforce cue-behavior associations (phase 4).

Which Behavior Change Techniques Should Be Used to Form Habit?

The most comprehensive taxonomy of behavior change techniques currently available defines habit formation as a discrete technique, which it defines as any effort to “prompt rehearsal and repetition of the behavior in the same context repeatedly so that the context elicits the behaviour” (Michie et al., 2013 , Suppl. Table 3 , p. 10). Yet, this definition incorporates only context-dependent repetition and not any other technique that may promote habit by increasing the likelihood of context-dependent repetition (i.e., promoting motivation or action control; phases 1–3 of Lally and Gardner’s framework) or enhancing the contribution of each repetition to the learning of habit associations (phase 4). Although context-dependent repetition is necessary for habit to form, it realistically requires supplementation with techniques targeting pre- and post-initiation phases en route to habit formation (Gardner Lally, & Wardle, 2012 ). While Michie et al. ( 2013 ) treat habit formation as a unitary technique, habit formation may perhaps be more realistically seen as an intervention approach that comprises a broader suite of techniques, which marry context-dependent repetition with strategies that: reinforce motivation; boost action control capacity, opportunity, or skills; facilitate post-initiation repetition; or quicken the learning of associations arising from repetition.

Theory points to techniques that may facilitate progression through these phases. Intention formation (phase 1 of Lally & Gardner’s [ 2013 ] framework) is likely when people anticipate that the action or its likely consequences will be positive and believe that they have a realistic opportunity and capability to perform the behavior (Ajzen, 1991 ; Bandura, 2001 ; Michie et al., 2011 ; Rogers, 1983 ; Schwarzer, Lippke, & Luszczynska, 2011 ). Providing information on the likely positive consequences of action, or choosing to pursue actions that are already most highly valued, may therefore aid habit development by enhancing motivation. Action control skills are required to initiate intention enactment (phase 2) and to maintain the behavior by consistently prioritizing the intention over competing alternatives (phase 3). This will likely be facilitated by self-regulatory techniques such as planning, setting reminders, self-monitoring, and reviewing goals to ensure they remain realistic and attractive, and receiving (intrinsic) rewards contingent on successful performance (Gardner et al., 2012 ; Lally & Gardner, 2013 ). People are most likely to engage in context-dependent repetition in response to highly salient cues (e.g., event- rather than time-based cues, which likely require conscious monitoring; McDaniel & Einstein, 1993 ). Pairing the action with more frequently and consistently encountered cues may quicken habit learning at phase 4 (Gardner & Lally, 2019 ). Highly specific action plans detailing exactly what will be done and in exactly which situation (i.e., implementation intentions; Gollwitzer, 1999 ) should therefore be conducive to the acquisition of associations (but see Webb et al., 2009 ). Implementation intentions can also facilitate habit substitution: By consistently enacting new, pre-specified cue responses that directly compete with existing habitual responses, such as feeding children water instead of sugary drinks (McGowan et al., 2013 ), new responses may acquire the potential to override and erode old habitual responses (Adriaanse et al., 2011 ). The reinforcing value of repetition may also be strengthened where intrinsic reward is delivered or attention is drawn to an undervalued intrinsic reward arising from action (Radel et al., 2017 ).

Which Behavior Change Techniques Have Been Used to Form Habit, and with What Effect?

While theory can recommend techniques that should be used to promote habit formation, evaluations of habit-based interventions are needed to show which techniques have been used, and with what effect, in real-world behavior change contexts. To this end, a systematic literature search was run to identify habit-based health-promotion interventions and to document the behavior change methods used.

Four psychology and health databases (Embase, Medline, PsycInfo, Web of Science) were searched in March 2018 to identify sources that had cited one of nine key papers about habit and health. These sources were selected to capture topics of habit measurement (Gardner, Abraham, Lally, & de Bruijn, 2012 ; Ouellette & Wood, 1998 ; Verplanken & Orbell, 2003 ), principles and processes of habit formation (Gardner, Lally, & Wardle, 2012 ; Lally & Gardner, 2013 ; Lally et al., 2010 ; Lally et al., 2011 ), and conceptual commentaries (Gardner, 2015 ; Wood & Rünger, 2016 ). Papers were eligible for review if they (a) were published in English, (b) were peer-reviewed, (c) reported primary quantitative or qualitative data, (d) had tested efficacy or effectiveness for changing behavior or habit, (e) used interventions designed to promote habit formation for health behaviors, (f) targeted context-dependent repetition, and (g) were informed by theory or evidence around habit, operationalized as a learned automatic response to contextual cues or a process that generates such responses. Interventions adopted primarily to elucidate the habit formation process (rather than to develop or assess intervention effectiveness; e.g., Judah et al., 2013 ; Lally et al., 2010 ) and any that focused exclusively on breaking existing habits (e.g., Armitage, 2016 ) were excluded. For each eligible intervention, all available material was coded, including linked publications (e.g., protocols), to identify component techniques using the Behavior Change Technique Taxonomy v1 (Michie et al, 2013 ).

Twenty papers, reporting evaluations of 19 interventions, were identified. Four of the 19 interventions represented variants of interventions used elsewhere in the 20 papers. For example, one trial evaluated the same habit-based intervention component in two conditions, which varied only in the frequency of supplementary motivational interviews and booster phone calls (Simpson et al., 2015 ). Thus, the 19 could be reduced to 15 unique habit-based interventions, of which four focused on both dietary and physical activity habits, six on physical activity (or sedentary behavior) only, two on dietary consumption only, two on dental hygiene, and one on food safety. In all of the studies, habit measures were self-reported.

Diet and Physical Activity Interventions

One randomized controlled trial (RCT) compared, in overweight and obese adults, an intervention that included advice on forming and substituting healthy for unhealthy habits, with a non-habit-based intervention that emphasized relationships with food, body image, and weight biases (Carels et al., 2014 ; see also Carels et al., 2011 ). Those in the habit-based intervention received training on changing old routines and developing new ones, including advice on using cues and forming implementation intentions. Both intervention groups received weekly weight assessments and monitored their physical activity, calorie intake, and output. At a 6-month follow-up, both the habit-based ( n = 30) and non-habit intervention groups ( n = 29) were eating a healthier diet, exercising more regularly, and had lost weight. Physical activity habit strengthened and sitting habit weakened in both groups, though no between-group differences were found in weight loss or habit strength.

Lally et al.’s ( 2008 ) “Ten Top Tips” weight loss intervention centered on a leaflet outlining recommendations for forming healthy eating and physical activity habits, as supplemented by a daily adherence monitoring diary. The leaflet included advice on routinization, identifying effective cues, and habit substitution. A small non-randomized trial compared the intervention, augmented with monthly ( n = 35) or weekly weighing ( n = 34), against a no-treatment control. The intervention group lost more weight than the control group at 8 weeks and maintained weight loss at 32 weeks. Scores at 32 weeks suggested the tips had become habitual, and habit change correlated positively with weight loss (Lally et al., 2008 ; see also Lally et al., 2011 ). In a subsequent RCT (Beeken et al., 2012 , 2017 ), intervention recipients ( n = 267) lost more weight at 3 months than did a usual-care group ( n = 270). At 24 months, the intervention group had maintained weight loss, though the usual care group had lost a similar amount of weight. Habit strength, measured only at baseline and 3 months, increased more in the intervention than in the control group (Beeken et al., 2017 ). Weight loss at 3 months was attributable to gains in both habit and self-regulatory skill (Kliemann et al., 2017 ).

Simpson et al.’s ( 2015 ) weight-loss intervention provided participants with motivational advice designed to prompt intention formation, with information about how to form dietary and activity habits, and social support. Two intervention variants, differing according to the frequency of sessions, were evaluated against a minimal-treatment control, which did not feature habit-based advice, in a feasibility RCT among obese patients. Recipients of the more intensive intervention variant ( n = 55) showed greater BMI reduction at a 12-month follow-up than did the less intensive intervention ( n = 55) or control groups ( n = 60). There were no between-group differences at 12 months in physical activity or overall healthy eating, nor were there differences in activity or diet habit scores.

One RCT compared an 8-week computer-tailored intervention designed to reduce cardiovascular risk against a no-treatment control among cardiac and diabetes rehabilitation patients who already intended to increase their activity and fruit and vegetable consumption (Storm et al., 2016 ). The intervention provided information about health risks of inactivity and unhealthy diet and enhancing self-regulatory skills. Immediately following intervention cessation, fruit and vegetable consumption and physical activity habit and behavior scores were greater among the intervention ( n = 403) than control group ( n = 387), but no differences were observed 3 months post-baseline.

Physical Activity and Sedentary Behavior Interventions

An intervention for new gym members promoted habits for both physical activity and preparatory actions for gym attendance (e.g., packing a gym bag; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). Members received advice on how to form habits, including selecting time cues, setting action plans, and using accessories to increase enjoyment and so support cue-consistent performance and foster intrinsic motivation, which theory suggests can strengthen the impact of repetition on habit development (Lally & Gardner, 2013 ). Moderate-to-vigorous physical activity gains, objectively observed at an 8-week follow-up, were greater among intervention recipients ( n = 47) than the no-treatment control group ( n = 47). Habit strength was not assessed.

All 49 participants in Fournier et al.’s ( 2017 ) RCT were given access to twice-weekly, 1-hour tailored physical activity sessions for 28 weeks, with one group ( n = 23) also sent SMS reminders targeting intrinsic motivation and consistent performance to the intervention group to foster habitual attendance. Although physical activity habit strength (assessed using a subscale of the SRHI) increased for both groups immediately post-intervention, the SMS group experienced quicker habit gains. Marginally greater activity was observed in the SMS group at 12 months.

One 4-month intervention for middle- to older-aged adults comprised seven 2-hour group sessions and sought to create new balance and strength exercise habits by recommending small modifications to everyday routines (e.g., placing frequently used items on high shelves to promote stretching to reach them) (Fleig et al., 2016 ; see also Clemson et al., 2012 ). An uncontrolled trial among 13 participants showed that, while there were no apparent changes in objectively measured physical performance, there were considerable habit strength gains for the recommended actions over 6 months. Notably, participants reported in interviews that the exercises had become automatically triggered, yet they performed them consciously, suggesting that the intervention promoted habitual instigation rather than execution.

Another intervention promoting small activity changes in older adulthood was evaluated in two papers (Matei et al., 2015 ; White et al., 2017 ). Drawing on Lally et al.’s ( 2008 ) “Ten Top Tips,” it comprised a leaflet offering recommendations for integrating and substituting light-intensity physical activities into everyday routines, with supplementary self-monitoring record sheets (Gardner, Thune-Boyle, et al., 2014 ). An 8-week uncontrolled trial was undertaken among two discrete samples (Matei et al., 2015 ). No changes were found in sitting time, physical activity, or sitting or physical activity habit among one sample ( n = 16), but a second sample ( n = 27) reported decreased sitting time and increased walking. Qualitative data suggested both groups experienced automaticity gains and some health benefits. A subsequent pilot RCT showed that intervention recipients ( n = 45) experienced no greater change than did a control group ( n = 46) who received a pre-existing fact sheet promoting activity and reducing sitting, but with no habit-based advice (White et al., 2017 ). Both groups reduced sitting time and sitting habit and increased activity and activity habit.

Using an experience sampling design, Luo et al. ( 2018 ) tracked change in standing or moving breaks from sedentary behavior in office workers given 3 weeks of access to automated computer-based reminders to break up sitting, timed to occur based on daily self-selected work and break durations. Although sitting behavior was not monitored, habit strength and self-regulation for taking “moving breaks” during work hours both increased significantly across the study.

Similarly, Pedersen et al. ( 2014 ) evaluated a software package that automatically deactivated desk-based employees’ computer screens every 45 minutes to substitute new physical activity habits for existing prolonged sitting habits. Although all participants received information on the detrimental health impact of sitting and benefits of activity, self-report activity data suggested that those who used the software for 13 weeks ( n = 17) expended greater energy per day than did those not given the software ( n = 17).

Dietary Interventions

One intervention promoted habitual healthy child-feeding practices among parents of children aged 2–6 years (McGowan et al., 2013 ). On each of four occasions over 8 weeks, parents chose to pursue one of four families of habit formation targets (increased feeding of fruit, vegetables, water, and healthy snacks). They received advice on the importance of child dietary consumption and on self-regulatory strategies, including action planning, goal setting, and context-dependent repetition. An RCT showed that intervention parents ( n = 58) reported greater child intake of vegetables, water, and healthy snacks but a waiting-list control group ( n = 68) did not. Habit strength increased for all three behaviors, and a habit score averaged across behaviors correlated with behavior change (McGowan et al., 2013 ; see also Gardner, Sheals, Wardle, & McGowan, 2014 ).

In one RCT, fruit and vegetable consumption changes were compared between participants who received habit-based messages, and those receiving general, non-habit-based tips for increasing consumption or messages about healthy eating more broadly (Rompotis et al., 2014 ). Notably, habit-based messages focused on anticipating stimulus control and environmental modification and on eating the same fruits and vegetables at the same time each day, so targeting both habitual instigation and execution (see Phillips & Gardner, 2016 ). The intervention was delivered via SMS in one set of conditions and email in the other. At 8-weeks post-intervention, both intervention groups (SMS n = 26, email n = 30) had increased fruit consumption and fruit habit strength, but those in all other conditions had not (SMS fruit and vegetable tips, n = 24, SMS healthy eating tips, n = 23; email fruit and vegetable tips, n = 29, email healthy eating n = 29). No effects were found on vegetable consumption or habit.

Oral Hygiene

Two school-based interventions aimed to increase tooth brushing in primary school children. One involved weekly dental hygiene lessons and daily tooth brushing practice time (Gaeta, Cavazos, Cabrera, & Rosário, 2018 ). School visits were also made by health promoters, and a seminar was held for teachers. One control group ( n = 52) received the visits and seminar only, and a second control group ( n = 52) received the seminar only. A quasi-experiment showed that children in the habit-based intervention ( n = 106) and visits-and-seminar control group had less dental plaque, and a stronger tooth brushing habit at 12-week follow-up than did the seminar-only control group. The habit-based intervention group had the lowest plaque.

Wind et al.’s ( 2005 ) intervention also involved allocation of a designated tooth brushing time during the school day and encouragement from teachers. Tooth brushing rates increased in the intervention group ( n = 141) during treatment but not in the control group (the nature of which could not be identified from the published report; n = 155). There were no differences in behavior at 12-months post-intervention nor in habit at any follow-up.

Food Safety

An intervention promoted the microwaving of dishcloths or sponges, for hygiene reasons (Mullan, Allom, Fayn, & Johnston, 2014 ). Recipients received emails and a poster providing instructions on how and why to microwave the dishcloths and sponges, designed to be placed in kitchens to act as a cue to the action. In an RCT, one intervention group was instructed to self-monitor their action, for intervention purposes, every 3 days ( n = 15) and another every 5 days ( n = 17). Relative to those who received an unrelated control treatment ( n = 13), frequency and habit strength increased in the two intervention groups at 3 weeks and was sustained to the final 6-week follow-up.

Behavior Change Techniques Used in Previous Interventions

A total of 32 discrete behavior change techniques were each identified in at least one of the 15 interventions (see Table 1 and Table 2 ). Aside from context-dependent repetition itself—which, as an inclusion criterion, was necessarily present in all interventions—the most commonly used were “use prompts and cues” (present in 11 interventions; 73%), “action planning” (8 interventions; 53%), “provide instruction on how to perform the behavior” (8 interventions; 53%), “set behavioral goals” (8 interventions; 53%), and “self-monitor behavior” (7 interventions; 47%). Also common were “behavioral practice or rehearsal” (6 interventions; 40%), “provide information on health consequences” (6 interventions; 40%), and “problem solving” (5 interventions; 33%). “Behavioral substitution” and habit substitution (labeled “habit reversal” in the taxonomy) were each used in 4 interventions (27%).

Table 1. Behavior Change Techniques Identified in 15 Habit Formation Interventions

Note . With the exception of “context-dependent repetition,” all technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

* This technique is labeled “habit formation” in the BCT Taxonomy v1 (Michie et al., 2013 ). Rephrasing this as “context-dependent repetition” more clearly delineates the underlying technique (i.e., to consistently repeat behavior in an unvarying context) from the outcome that it is designed to serve (i.e., to form habit). It also better acknowledges the possibility that such repetition may not lead to the formation of habit. For example, Lally et al. ( 2010 ) observed some participants who failed to attain peak habit strength in an 84-day study period, and some who experienced gains that peaked at low levels, suggesting that while repetition had rendered the behavior more habitual, the action remained predominantly regulated by conscious motivation rather than habit.

Table 2. Behavior Change Techniques Documented in 15 Habit Formation Interventions

Note . All technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

While all 15 interventions were based on the principle of habit formation, none used context-dependent repetition as a standalone technique. 2 The use of techniques additional to repetition echoes the view that in the real world, habit is best promoted by embedding context-dependent repetition into a broader package of techniques that also target motivation and action control, which are prerequisites for repetition (Lally & Gardner, 2013 ). Techniques most commonly adopted in past interventions have focused predominantly on action control (e.g., planning, goal-setting, identifying cues, rehearsing action, problem solving). The relative paucity of techniques targeting motivation may reflect an assumption that, for most of the behaviors targeted, intervention recipients generally recognize the value of behavior change, but lack the volitional skills, opportunities, or resources to change. Whether motivation should be targeted as part of a habit-formation intervention will depend on whether target populations understand the need for change and prioritize the target behavior above alternatives.

Fewer than half of the 15 interventions appear to have addressed factors that may moderate the relationship between repetition and habit development. Theory and evidence suggest that the mental associations that underlie habit will develop most strongly or quickly where actions are more simple or intrinsically rewarding and in response to cues that are salient and consistently encountered (Lally & Gardner, 2013 ; McDaniel & Einstein, 1993 ; Radel et al., 2017 ). Several of the reviewed interventions purposively promoted habit formation for simple behaviors (Beeken et al., 2017 ; Fleig et al., 2016 ; Lally et al., 2010 , 2011 ; Matei et al., 2015 ; Mullan et al., 2014 ; White et al., 2017 ). Kaushal et al. ( 2017 ) emphasized the importance of intrinsic reward in their physical activity promotion intervention, and Fournier et al. ( 2017 ) targeted intrinsic motivation. These studies highlight how interventions may move beyond simply promoting repetition toward targeting factors that may reduce the number of repetitions required for a target behavior to become habitual.

How Should Habit-Based Interventions Be Evaluated?

Previous interventions attest to the potential for habit-based approaches to change behavior. Although many intervention studies were not designed to test effectiveness, 13 of the 15 interventions were associated with positive change on at least one index of behavior or behavior-contingent outcomes (e.g., weight loss) at one or more follow-ups. Process evaluations pointed to the strengthening of habit as a key mechanism underpinning behavioral change based on increases in self-reported automaticity scores or qualitative reflections on the subjective experience of automaticity (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Kliemann et al., 2017 ; Lally et al., 2011 ; Matei et al., 2015 ). Additionally, acceptability studies have suggested that recipients find the concept of context-dependent repetition—which distinguishes habit-based and non-habit-based interventions—easy to understand and follow (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Lally et al., 2011 ; Matei et al., 2015 ).

Limitations of evaluation methods preclude understanding of how best to support habit formation. It is not yet clear whether promotion of context-dependent repetition is necessary for habit to develop or, indeed, whether it represents the most “active” ingredient of a habit formation intervention. One study found that a control group that did not receive habit-based advice reported similar physical activity habit gains to those among a group that received habit guidance (White et al., 2017 ). Conversely, another study showed that intervention recipients deviated from habit-based advice (e.g., by setting goals that were not specific, measurable, or achievable), yet habit strengthened (Gardner, Sheals, et al., 2014 ). Habit formation may therefore arise as a byproduct of interventions that do not explicitly target habit development. The unique contribution of context-dependent repetition to behavior change remains unclear because none of the reviewed studies compared a habit-based intervention with an otherwise identical non-habit-based equivalent. Indeed, most studies have evaluated habit formation interventions against minimal-treatment control groups or used uncontrolled designs. Future research should seek to compare matched habit- and non-habit-based interventions or otherwise use factorial designs, which allow testing for isolated effects within a multicomponent intervention, or mediation analyses, which can assess whether habit change underpins intervention effects.

Intervention evaluations have also been limited by short follow-up periods, which is ironic given that the key purported benefit of incorporating habit formation into interventions is the potential to increase longevity of behavior change. Few studies evaluated outcomes over 12 months or longer, with the longest observed follow-up being 24 months (Beeken et al., 2017 ). Beeken et al.’s ( 2017 ) “Ten Top Tips” intervention showed greater impact than did a non-habit-based usual-care treatment on dietary and physical activity habits, and weight loss, at the 3-month follow-up, which the authors found to be due in part to habit development (Kliemann et al., 2017 ). Yet, while weight loss was maintained at 24 months, the advantage conferred by the habit-based intervention over usual care was lost, suggesting that any habit gains may have dissipated, or alternatively, that for those who were successful in maintaining the behaviors over the 2-year period, habit formation had occurred regardless of condition. These possibilities cannot be investigated because habit strength was not evaluated at 24 months. Elsewhere, however, a small exploratory (non-intervention) study suggested that habit gains may erode over time: Among a group of participants forming dental flossing habits over 8 weeks, habit strength had considerably eroded in the subgroup of participants who provided data at a 6-month follow-up (Judah et al., 2013 ). Until more is done to assess the longevity of habit-based intervention effects, the hypothesis that habit persists over time, and so supports behavior maintenance, remains insufficiently tested.

Theory proposes that, through consistent performance, behaviors become habitual such that they are initiated automatically upon encountering cues via the activation of learned context-behavior associations. Habitual behaviors are thought to be self-sustaining, and so forming a habit has been proposed as a means to promote long-term maintenance of behavior. Interventions that seek to promote habit formation should include not only advice on context-dependent repetition, but also techniques that support the motivation and action control needed to repeat the action and that may enhance the reinforcing value of repetition on habit development. Fifteen interventions were found to have used habit formation principles to encourage engagement in health-promoting behaviors, and these have tended to supplement advice on repetition with action control techniques. Previous research suggests a habit-based approach has much to offer to behavior change initiatives; habit formation offers an acceptable, easily understood intervention strategy, with the potential to change behavior and yield favorable health outcomes. Yet, the unique effects of habit-specific techniques, and the longevity of effects, have not been adequately explored. The central assumption of the habit-based approach—that habit gains translate into long-term behavior maintenance—remains largely untested.

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• Wood, W. , & Neal, D. T. (2007). A new look at habits and the habit-goal interface . Psychological Review , 114 (4), 843–863.
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1. Rhodes and colleagues have extended this line of thinking by incorporating preparatory actions into the process, showing that habitual preparation for an activity (e.g., packing a gym bag) can influence frequency of engagement in the focal behavior (in this case, exercise; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). However, this differs from the instigation–execution distinction in that it focuses on the role of habit in different behaviors (preparatory actions vs. focal actions) rather than different roles of habit in the same behavior.

2. This is perhaps inevitable given the present review criteria, which excluded studies that used context-dependent repetition to study the habit formation process itself. However, real-world studies of the formation of health habits have not been based on context-dependent repetition alone; both Lally et al. ( 2010 ) and Fournier et al. ( 2017 ) instructed participants to use prompts and cues and set action plans or implementation intentions (see also Judah et al., 2013 ).

Related Articles

• Methods of Health Behavior Change
• Theoretical Approaches to Physical Activity Promotion
• Use of Wearable Activity Trackers for Physical Activity Promotion

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Collection  29 March 2022

2021 Top 25 Social Sciences and Human Behaviour Articles

We are pleased to share with you the 25 most downloaded  Nature Communications  articles* in social sciences and human behaviour published in 2021. Featuring authors from around the world, these papers highlight valuable research from an international community.

Browse all Top 25 subject area collections  here .

*Data obtained from SN Insights (based on Digital Science's Dimensions) and normalised to account for articles published later in the year.

Research highlights

Psychological characteristics associated with COVID-19 vaccine hesitancy and resistance in Ireland and the United Kingdom

Hesitancy and resistance towards vaccination is a challenge for public health. Here the authors determine psychological characteristics associated with COVID-19 vaccine hesitancy or resistance attitudes in the UK and Ireland.

• Jamie Murphy
• Frédérique Vallières
• Philip Hyland

Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China

The growing energy consumption and carbon emissions of Bitcoin mining could potentially undermine global sustainability efforts. Here, the authors show the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emissions.

• Shangrong Jiang
• Shouyang Wang

Potentially long-lasting effects of the pandemic on scientists

The pandemic has caused disruption to many aspects of scientific research. In this Comment the authors describe the findings from surveys of scientists between April 2020 and January 2021, which suggests there was a decline in new projects started in that time.

• Dashun Wang

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons

Little is known about the brain’s computations that enable the recognition of faces. Here, the authors use unsupervised deep learning to show that the brain disentangles faces into semantically meaningful factors, like age or the presence of a smile, at the single neuron level.

• Irina Higgins
• Matthew Botvinick

An actionable anti-racism plan for geoscience organizations

Racism thrives in geoscience. We present an antiracism plan to support the recruitment, retention and success of Black, Indigenous, and other people of color in geoscience. Our action plan can be adapted by any organization to remove barriers to participation for all marginalized geoscientists.

• Hendratta N. Ali
• Sarah L. Sheffield
• Blair Schneider

Neutral bots probe political bias on social media

Social media platforms moderating misinformation have been accused of political bias. Here, the authors use neutral social bots to show that, while there is no strong evidence for such a bias, the content to which Twitter users are exposed depends strongly on the political leaning of early Twitter connections.

• Diogo Pacheco
• Filippo Menczer

Individual differences in information-seeking

Information-seeking is important for learning, social behaviour and decision making. Here the authors investigate factors that associate with individual differences in information-seeking behaviour.

• Christopher. A. Kelly
• Tali Sharot

Lack of consideration of sex and gender in COVID-19 clinical studies

Sex and gender have been associated with differences in SARS-CoV-2 incidence and clinical outcomes and therefore warrant consideration in study designs. Here, the authors assess registered and published clinical COVID-19 studies and find that sex-disaggregated analyses are infrequently presented or planned.

• Mathias Wullum Nielsen
• Sabine Oertelt-Prigione

Optimal COVID-19 quarantine and testing strategies

Safely reducing the necessary duration of quarantine for COVID-19 could lessen the economic impacts of the pandemic. Here, the authors demonstrate that testing on exit from quarantine is more effective than testing on entry, and can enable quarantine to be reduced from fourteen to seven days.

• Chad R. Wells
• Jeffrey P. Townsend
• Alison P. Galvani

Brain network coupling associated with cognitive performance varies as a function of a child’s environment in the ABCD study

Previous research suggests that, for children and adults, there is an association between better performance on cognitive tests and less functional connectivity between two brain networks. Here, the authors find that this association does not hold in a sample of children from households in poverty, highlighting the need for more diverse samples to incorporate a range of childhood environments in developmental cognitive neuroscience.

• Monica E. Ellwood-Lowe
• Susan Whitfield-Gabrieli
• Silvia A. Bunge

mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity

Autism spectrum disorder (ASD) is characterised by synaptic surplus and atypical functional connectivity. Here, the authors show that synaptic pathology in Tsc2 haploinsufficient mice is associated with autism-like behavior and cortico-striatal hyperconnectivity, and that analogous functional hyperconnectivity signatures can be linked to mTOR-pathway dysfunction in subgroups of children with idiopathic ASD.

• Marco Pagani
• Noemi Barsotti
• Alessandro Gozzi

Cognitive functions and underlying parameters of human brain physiology are associated with chronotype

How being a “morning person” or “evening person” affects human cognition and brain physiology is not well understood. Here the authors show evidence of an association of chronotype with cognitive functions and related physiological parameters.

• Mohammad Ali Salehinejad
• Miles Wischnewski
• Michael A. Nitsche

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

The neural and computational mechanisms underpinning pitch perception remain unclear. Here, the authors trained deep neural networks to estimate the fundamental frequency of sounds and found that human pitch perception depends on precise spike timing in the auditory nerve, but is also adapted to the statistical tendencies of natural sounds.

• Mark R. Saddler
• Ray Gonzalez
• Josh H. McDermott

Sources of confidence in value-based choice

The authors show that metacognitive awareness of choice certainty is closely linked to endogenous attentional states that guide decision behaviour.

• Jeroen Brus
• Helena Aebersold
• Rafael Polania

CDH2 mutation affecting N-cadherin function causes attention-deficit hyperactivity disorder in humans and mice

Molecular mechanisms of attention-deficit hyperactivity disorder (ADHD) are not fully understood. Here the authors demonstrate a mutation in CDH2, encoding N-cadherin, that is associated with ADHD, and in a mouse model, delineate molecular electrophysiological characteristics associated with this mutation.

• D. Halperin

The pupil responds spontaneously to perceived numerosity

Rapid and spontaneous estimation of number is observed in many animals. Here the authors show that perceived number of items modulates the pupillary light response in humans, confirming its spontaneous nature, and introducing pupillometry as a tool to study numerical cognition.

• Elisa Castaldi
• Antonella Pomè
• Paola Binda

Infant gut microbiome composition is associated with non-social fear behavior in a pilot study

Experimental manipulation of the gut microbiome in animal models impacts fear behaviours. Here, the authors show in a pilot study that features of the human infant gut microbiome are associated with non-social fear behaviours during a laboratory based assessment.

• Alexander L. Carlson
• Rebecca C. Knickmeyer

Linear reinforcement learning in planning, grid fields, and cognitive control

Models of decision making have so far been unable to account for how humans’ choices can be flexible yet efficient. Here the authors present a linear reinforcement learning model which explains both flexibility, and rare limitations such as habits, as arising from efficient approximate computation

• Payam Piray
• Nathaniel D. Daw

Predicting lapses of attention with sleep-like slow waves

Attentional lapses occur in many forms such as mind-wandering or mindblanking. Here the authors show different types of attentional lapse are accompanied by slow waves, neural activity that is characteristic of transitions into sleep.

• Thomas Andrillon
• Angus Burns
• Naotsugu Tsuchiya

Shifting parental beliefs about child development to foster parental investments and improve school readiness outcomes

Parents’ investments in their children are a critical input in the production of early skills, yet those investments differ across socioeconomic backgrounds. Here the authors show that variations in parental beliefs about the impact of such investments can be one of the sources of investment disparities, and report interventions that can potentially shift those beliefs.

• John A. List
• Julie Pernaudet
• Dana L. Suskind

Partially overlapping spatial environments trigger reinstatement in hippocampus and schema representations in prefrontal cortex

The authors examine how we differentiate highly similar places from each other. They provide evidence for complementary neural mechanisms in the human hippocampus and prefrontal cortex involved in processing interfering and common elements important to remembering places that we have visited.

• Arne D. Ekstrom

Neural and computational mechanisms of momentary fatigue and persistence in effort-based choice

The willingness to exert effort into demanding tasks often declines over time through fatigue. Here the authors provide a computational account of the moment-to-moment dynamics of fatigue and its impact on effort-based choices, and reveal the neural mechanisms that underlie such computations.

• Tanja Müller
• Miriam C. Klein-Flügge
• Matthew A. J. Apps

Inequality is rising where social network segregation interacts with urban topology

Not much is known about the joint relationships between social network structure, urban geography, and inequality. Here, the authors analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads.

• Johannes Wachs
• Balázs Lengyel

Finding positive meaning in memories of negative events adaptively updates memory

Finding positive meaning in past negative events is associated with enhanced mental health. Here the authors show this adaptively updates memory, leading to enhanced positive emotion and content at future retrieval, which remains two months later.

• Megan E. Speer
• Sandra Ibrahim
• Mauricio R. Delgado

How social relationships shape moral wrongness judgments

Moral judgments depend on relational context, with different normative cooperative expectations – relational norms – embedded in different social relationships, such as parent-child, romantic partners, siblings, or acquaintances. Here, the authors show how relational norms for care, hierarchy, reciprocity, and mating are embedded in a set of everyday social relationships in the United States, and use this information to predict out-of-sample moral judgments in relational context.

• Brian D. Earp
• Killian L. McLoughlin
• Molly J. Crockett

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Human Behavior Research: The Complete Guide

Bryn Farnsworth

Introduction to Human Behavior Research and Studies

Academic and commercial researchers alike are aiming toward a deeper understanding of how humans act, make decisions, plan, and feel. Advances in wearable sensor technology along with procedures for multi-modal data acquisition and analysis have lately been enabling researchers all across the globe to tap into previously unknown secrets of the human brain and mind.

Still, as emphasized by Makeig and colleagues (2009), the most pivotal challenge lies in the systematic observation and interpretation of how distributed brain processes support our natural, active, and flexibly changing behavior and cognition.

We all are active agents, continuously engaged in attempting to fulfill bodily needs and mental desires within complex and ever-changing surroundings while interacting with our environment. Brain structures have evolved that support cognitive processes targeted towards the optimization of outcomes for any of our body-based behaviors.

In this complete guide to understanding human behavior research, you’ll get a full run-down of how to get started with analyzing the systems, emotions and cognition that make humans tick, using scientifically credible methods such as biosensor research.

N.B. this post is an excerpt from our Human Behavior Guide. You can download your free copy below and get even more insights into human behavior.

Free 52-page Human Behavior Guide

For Beginners and Intermediates

• Get accessible and comprehensive walkthrough
• Valuable human behavior research insight
• Learn how to take your research to the next level

Table of Contents

So what exactly is behavior.

In scientific research, human behavior is a complex interplay of three components: actions, cognition, and emotions.

Sounds complicated? Let’s address them one by one.

Actions are Behavior

An action denotes everything that can be observed, either with bare eyes or measured by physiological sensors. Think of an action as an initiation or transition from one state to another – at a movie set, the director shouts “action” for the next scene to be filmed.

Behavioral actions can take place on various time scales, ranging from muscular activation to sweat gland activity, food consumption, or sleep.

Cognitions are Behavior

Cognitions describe thoughts and mental images you carry with you, and they can be both verbal and nonverbal. “I have to remember to buy groceries,” or “I’d be curious to know what she thinks of me,” can be considered verbal cognitions. In contrast, imagining how your house will look like after remodeling could be considered a nonverbal cognition.

Cognitions comprise skills and knowledge – knowing how to use tools in a meaningful manner (without hurting yourself), sing karaoke songs or being able to memorize the color of Marty McFly’s jacket in “Back to the Future” (it’s red).

Emotions are Behavior

Commonly, an emotion is any relatively brief conscious experience characterized by intense mental activity, and a feeling that is not characterized as resulting from either reasoning or knowledge. This usually exists on a scale, from positive (pleasurable) to negative (unpleasant).

Other aspects of physiology that are indicative of emotional processing – such as increased heart rate or respiration rate caused by increased arousal – are usually hidden to the eye. Similar to cognitions, emotions cannot be observed directly. They can only be inferred indirectly by tracking facial electromyographic activity (fEMG),  analyzing facial expressions , monitoring arousal using ECG, galvanic skin response (GSR) , respiration sensors, or self-reported measures, for example.

What is the Study of Human Behavior?

The study of human behavior is a fascinating and complex field that delves into the myriad ways individuals think, act, and interact. This multidisciplinary approach draws from psychology, sociology, anthropology, and even biology to provide comprehensive insights into human actions and societal dynamics.

Exploring Human Behavior Studies

Human behavior studies strive to understand the ‘why’ behind actions and reactions. This exploration involves examining both innate and learned behaviors, how environmental factors shape actions, and the impact of mental processes on decision-making. Researchers employ various methods, from controlled laboratory experiments to field observations, ensuring a holistic understanding of human behavior in diverse contexts.

Significance of Studying Humans

Understanding human behavior is crucial for numerous reasons. It aids in predicting responses in different situations, which is invaluable in areas like marketing, policy-making, and therapy. Additionally, studying behavior helps address social issues, improve educational approaches, and enhance interpersonal relationships. By understanding the underlying motivations and factors influencing actions, we can foster more empathetic and effective interactions within society.

Applying Insights from Human Behavior Studies

The practical applications of human behavior studies are vast and varied. In healthcare, these insights assist in developing better patient care strategies and public health initiatives. In business, understanding consumer behavior drives marketing and product development. In education, insights into learning patterns lead to more effective teaching methods. Furthermore, in the realm of public policy, this knowledge informs laws and regulations that consider the behavioral tendencies of the populace.

The study of human behavior is not just an academic pursuit but a tool that, when wielded wisely, can significantly enhance the quality of life and societal progress. This field continues to evolve, promising even greater insights and applications in the future.

Everything is Connected

Actions, cognitions and emotions do not run independently of each other – their proper interaction enables you to perceive the world around you, listen to your inner wishes and respond appropriately to people in your surroundings. However, it is hard to tell what exactly is cause and effect – turning your head (action) and seeing a familiar face might cause a sudden burst of joy (emotion) accompanied by an internal realization (cognition):

Action = emotion (joy) + cognition (“hey, there‘s Peter!”)

In other cases, the sequence of cause and effect might be reversed: Because you’re sad (emotion) and ruminating on relationship issues (cognition), you decide to go for a walk to clear your head (action).

Emotion (sadness) + cognition (“I should go for a walk“) = action

Takeaways: What You Should Know…

Humans are active consumers of sensory impressions.

You actively move your body to achieve cognitive goals and desires, or to get into positive (or out of negative) emotional states. In other words: While cognition and emotion cannot be observed directly, they certainly drive the execution of observable action. For example, through moving your body to achieve cognitive goals and desires, or to get into positive (or out of negative) emotional states.

Cognitions are specific to time and situations

The former is important as you have to couple responses dynamically to stimuli, dependent on intentions and instructions. This allows you to respond to one and the same stimulus in near-unlimited ways. Stability, by contrast, is crucial for maintaining lasting stimulus-response relationships, allowing you to respond consistently to similar stimuli.

Imagination and abstract cognition are body-based

Even abstract cognitions (devoid of direct physical interaction with the environment) are body-based. Imagining limb movements triggers the same brain areas involved when actually executing the movements. When you rehearse material in working memory, the same brain structures used for speech perception and production are activated (Wilson, 2001).

We’d love to learn more about you! Talk to a specialist about your research and business needs and get a live demo of the capabilities of the iMotions Research Platform.

Learning and Behavior

When we talk about behavior, we need to consider how it is acquired. Learning denotes any acquisition process of new skills and knowledge, preferences, attitudes and evaluations, social rules and normative considerations.

You surely have heard of the “nature – nurture” debate – in the past, there has been quite some fighting about whether behavior was solely driven by genetic predispositions (nature) or environmental factors (nurture).

Today, it’s no longer a question of either/or. There simply is too much evidence for the impact of nature and nurture alike – behavior is considered to be established by the interplay of both factors.

Current theoretical frameworks also emphasize the active role of of the agent in acquiring new skills and knowledge. You are able to develop and change yourself through ongoing skill acquisition throughout life, which can have an impact on a neurological level. Think of it as assigning neuroscientific processes to the phrase “practice makes perfect”.

Classical Conditioning

Classical conditioning refers to a learning procedure in which stimulus-response pairings are learned – seeing tasty food typically triggers salivation (yummy!), for example. While food serves as unconditioned stimulus, salivation is the unconditioned response.

Unconditioned stimulus -> unconditioned response

Seeing food -> salivation

If encountering food is consistently accompanied by a (previously) neutral stimulus such as ringing a bell, a new stimulus-response pairing is learned.

unconditioned stimulus + conditioned stimulus -> unconditioned response

seeing food + hearing bell -> salivation

The bell becomes a conditioned stimulus and is potent enough to trigger salivation even in absence of the actual food.

conditioned stimulus – > response

hearing bell -> salivation

Described as generalization, this learning process was first studied by Ivan Pavlov and team (1927) through experiments with dogs, which is why classical conditioning is also referred to as Pavlovian conditioning.

Today, classical conditioning is one of the most widely understood basic learning processes.

Operant Conditioning

Operant conditioning, also called instrumental conditioning, denotes a type of learning in which the strength of a behavior is modified by the consequences (reward or punishment), signaled via the preceding stimuli.

In both operant and classical conditioning behavior is controlled by environmental stimuli – however, they differ in nature. In operant conditioning, behavior is controlled by stimuli which are present when a behavior is rewarded or punished.

Operant conditioning was coined by B.F. Skinner. As a behaviorist, Skinner believed that it was not really necessary to look at internal thoughts and motivations in order to explain behavior. Instead, he suggested to only take external, observable causes of human behavior into consideration.

According to Skinner, actions that are followed by desirable outcomes are more likely to be repeated while those followed by undesirable outcomes are less likely to be repeated. In this regard, operant conditioning relies on a fairly simple premise: Behavior that is followed by reinforcement will be strengthened and is more likely to occur again in the future.

The key concepts of operant conditioning are:

• Positive reinforcement (otherwise known just as reinforcement) occurs when a behavior is rewarding, increasing the frequency of that behavior.
• Negative reinforcement (escape) occurs when a behavior is followed by the removal of an aversive stimulus, increasing the frequency of the behavior.
• Punishment occurs when a behavior is followed by an aversive stimulus, causing a decrease in that behavior.
• Penalty occurs when a behavior is followed by the removal of a rewarding stimulus
• Extinction occurs when a behavior that had previously been reinforced is no longer effective.

These learning theories give guidance for knowing how we gather information about the world. The way in which we learn is both emotionally and physiologically appraised. This will have consequences for how we act, and carry out behaviors in the future – what we attend to, and how it makes us feel.

Decisions and Behavior

While behavior is acquired through learning, whether the acting individual decides to execute an action or withhold a certain behavior is dependent on the associated incentives, benefits and risks (“if Peter was penalized for doing this, I certainly won‘t do it!”).

But which are the factors driving our decisions? Theories such as social learning theory provide a base set of features, but one of the most influential psychological theories about decision-making actually has its origins in an economics journal.

In 1979, Daniel Kahneman & Amos Tversky published a paper proposing a theoretical framework called the Prospect Theory. This laid the foundations for Kahneman’s later thoughts and studies on human behavior, that was summarized in his bestselling book “Thinking, Fast and Slow”.

System 1 and System 2

Kahneman‘s theories were also concerned with how people process information. He proposed that there are two systems that determine how we make decisions: System 1 – which is fast but relatively inaccurate, and system 2 – which is slow but more accurate.

The theory suggests that our everyday decisions are carried out in one of these two ways, from buying our morning coffee, to making career choices. We will use different approaches depending on the circumstances.

Decision-making and Emotions

Human behavior and decision-making are heavily affected by emotions – even in subtle ways that we may not always recognize. After making an emotionally-fueled decision, we tend to continue to use the imperfect reasoning behind it, and “a mild incidental emotion in decision-making can live longer than the emotional experience itself” as pointed out by Andrade & Ariely (2009).

An example of mood manipulation affecting decision making was completed by researchers who wanted to know how a willingness to help could be affected by positive feelings.

To study their question, they placed a Quarter (25ct) clearly visible in a phone booth (yes, these things actually existed!) and waited for passers-by to find the coin. An actor working on behalf of the psychologist stepped in, asking to take an urgent phone call. Study participants who actually found the coin were significantly happier, allowing the confederate to take the call, while those who didn’t find the coin were unaffected, and more likely to say no (Isen & Levin, 1972).

Getting Started with Human Behavior Research

Research on human behavior addresses how and why people behave the way they do. However, as you have seen in the previous sections, human behavior is quite complex as it is influenced, modulated and shaped by multiple factors which are often unrecognized by the individual: Overt or covert, logical or illogical, voluntary or involuntary.

Conscious vs. unconscious behavior

Consciousness is a state of awareness for internal thoughts and feelings as well for proper perception for and uptake of information from your surroundings.

A huge amount of our behaviors are guided by unconscious processes. Just like an iceberg, there is a great amount of hidden information, and only some of it is visible with the naked eye.

Overt vs. covert behavior

Overt behavior describes any aspects of behavior that can be observed, for example body movements or (inter-)actions. Also, physiological processes such as blushing, facial expressions or pupil dilation might be subtle, but can still be obeserved. Covert processes are thoughts (cognition), feelings (emotion) or responses which are not easily seen. Subtle changes in bodily processes, for instance, are hidden to the observer‘s eye.

In this case, bio- or physiological sensors are used to aid the observation with quantitative measures as they uncover processes that are covert in the first place. Along this definition, EEG , MEG, fMRI and fNIRS all monitor physiological processes reflecting covert behavior.

Rational vs. irrational behavior

Rational behavior might be considered any action, emotion or cognition which is pertaining to, influenced or guided by reason. In contrast, irrational behavior describes actions that are not objectively logical.

Patients suffering from phobias often report an awareness for their thoughts and fears being irrational (“I know that the spider can‘t harm me”) – albeit they still cannot resist the urge to behave in a certain way.

Voluntary vs. involuntary behavior

Voluntary actions are self-determined and driven by your desires and decisions. By contrast, involuntary actions describe any action made without intent or carried out despite an attempt to prevent it. In cognitive-behavioral psychotherapy, for example, patients are exposed to problematic scenarios, also referred to as flooding, such as spiders, social exhibition or a transatlantic plane ride.

Many of our behaviors appear to be voluntary, rational, overt, and conscious – yet they only represent the tip of the iceberg for normal human behavior. The majority of our actions are involuntary, potentially irrational, and are guided by our subconscious. The way to access this other side of behavior is to examine the covert behaviors that occur as a result.

Back to top 

Measuring Human Behavior

In order to describe and interpret human behavior, academic and commercial researchers have developed intricate techniques allowing for the collection of data indicative of personality traits, cognitive-affective states and problem solving strategies.

In experimental setups , specific hypotheses about stimulus-response relationships can be clarified. Generally, research techniques employed by scientists can be classified into qualitative and quantitative procedures.

Qualitative studies  gather non-numerical insights, for example by analyzing diary entries, using open questionnaires, unstructured interviews or observations. Qualitative field / usability studies, for example, aim towards understanding how respondents see the world and why they react in a specific way rather than counting responses and analyzing the data statistically.

Quantitative studies characterize statistical, mathematical or computational techniques using numbers to describe and classify human behavior. Examples for quantitative techniques include structured surveys, tests as well as observations with dedicated coding schemes. Also, physiological measurements from EEG , EMG , ECG , GS R, and other sensors produce quantitative output, allowing researchers to translate behavioral observations into discrete numbers and statistical outputs.

Behavioral Observation

Behavioral observation is one of the oldest tools for psychological research on human behavior. Researchers either visit people in their natural surroundings (field study) or invite individuals or groups to the laboratory.

Observations in the field have several benefits. Participants are typically more relaxed and less self-conscious when observed at home, at school or at the workplace. Everything is familiar to them, permitting relatively unfiltered observation of behavior which is embedded into the natural surroundings of the individual or group of interest.

However, there’s always the risk of distraction – shouting neighbors or phones ringing. Field observations are an ideal starting point of any behavioral research study. Just sitting and watching people offers tremendous amounts of insights if you’re able to focus on a specific question or aspect of behavior.

Observation in the laboratory , by contrast, allows much more experimental control. You can exclude any unwanted aspects and completely ban smart phones, control the room layout and make sure to have everything prepared for optimal recording conditions (correct lighting conditions, ensuring a quiet environment, and so on).

You can create near-realistic laboratory environments – building a typical family living room, office space or creative zone, for example, to make respondents feel at ease and facilitating more natural behavior.

Surveys and Questionnaires

Surveys and questionnaires are an excellent tool to capture self-reported behaviors and skills, mental or emotional states or personality profiles of your respondents. However, questionnaires are always just momentary snapshots and capture only certain aspects of a person’s behavior, thoughts and emotions.

Surveys and questionnaires typically measure what Kahneman would describe as system 2 processes – thoughts that are carried out slowly and deliberately. System 1 processes – thoughts that are fast and automatic – can be measured by other methods that detect quick physiological changes.

Focus groups

In market research, focus groups typically consist of a small number of respondents (about 4–15) brought together with a moderator to focus on beliefs and attitudes towards a product, service, concept, advertisement, idea or packaging. Focus groups are qualitative tools as their goal is to discuss in the group instead of coming to individual conclusions.

What are the benefits of a product, what are the drawbacks, where could it be optimized, who are ideal target populations? All of these questions can be addressed in a focus group.

Beyond Surveys and Focus Groups

While surveys and focus groups can be instrumental in understanding our conscious thoughts and emotions, there is more to human behavior than meets the eye. The subconscious mind determines how our behavior is ultimately carried out, and only a small fraction of that is accessible from traditional methodologies – using surveys and focus groups.

As some researchers have claimed, up to 90% of our actions are guided by the subconscious. While the other 10% is important, it is clear that there is much to gain by probing further than what is tested by traditional methods.

Modern approaches aim to explore the hidden and uncharted territory of the subconscious, by measuring reliable outputs that provide deeper information about what someone is really thinking.

Biosensors for Learning About Human Behaviour

In addition to observing overt behavior, you can use biosensors and measurement devices in order to understand how mind, brain and body interact.

Biosensors give access to otherwise hidden processes. These usually hidden processes (at least to an observer) can give indications about the thought processes that Daniel Kahneman would describe as belonging to System 1 – fast and largely emotionally driven reactions. These reactions are quick processes that underlie a large portion of our decision-making and our resulting behavior.

Eye tracking

offers incredible insights into visual attention above and beyond any other experimental method. While eye tracking is commonly used to monitor where we direct our eye movements at a certain point in time, it also tracks the dilation of the pupil.

Electroencephalography (EEG)

is a neuroimaging technique measuring electrical activity generated by the brain from the scalp surface using sensors (electrodes) and amplifier systems. It is ideal for assessing brain activity associated with perception, cognition, and emotional processes.

Among all biosensors, EEG has the highest time resolution , thereby revealing substantial insights into sub-second brain dynamics of engagement, motivation, frustration, cognitive workload, and further metrics associated with stimulus processing, action preparation, and execution.

functional Near-Infrared Spectroscopy (fNIRS)

fNIRS records the diffusion of near-infrared light by human skull, scalp and brain tissue, allowing researchers to monitor cerebral blood flow in specified brain regions. While fNIRS is a relatively new technology, it has already proven to be a very promising tool in human behavior research.

Magnetic Resonance Imaging (MRI)

Whenever you would like to accomplish brain imaging with excellent spatial resolution, Magnetic Resonance Imaging (MRI) is the method of choice. MRI can be used to generate structural scans of high spatial precision, representing an accurate and highly precise 3D rendering of the respondent’s brain.

For examining dynamic changes in the brain, functional MRI (fMRI) can be used. The scanner uses magnetic fields and radio frequencies to measure changes in oxygenated and de-oxygenated blood flow in specific regions of the brain, that can then be related to cognitive processes.

Electrodermal activity (EDA)

EDA also referred to as galvanic skin response (GSR) , reflects the amount of sweat secretion from sweat glands in our skin. Increased sweating results in higher skin conductivity. When exposed to emotional stimulation, we “sweat emotionally” – particularly on our forehead, hands and feet. Just as pupil dilation, skin conductance is controlled subconsciously, therefore offering tremendous insights into the unfiltered, unbiased emotional arousal of a person.

Facial Expressions

As facial expressions are tied to our inner emotions, and our emotions rule so much of our behavior, studying facial expressions gives an insight into the reasons for our actions .

Facial expression analysis is a non-intrusive method that assesses head position and orientation, micro-expressions (such as lifting of the eyebrows or opening of the mouth) and global facial expressions of basic emotions (joy, anger, surprise, etc.) using a webcam placed in front of the respondent. Facial data is extremely helpful to validate metrics of engagement, workload or drowsiness.

Electromyographic (EMG

Electromyographic (EMG) sensors monitor the electric energy generated by bodily movements of the face, hands or fingers, etc. You can use EMG to monitor muscular responses to any type of stimulus material to extract even subtle activation patterns associated with consciously controlled hand/finger movements (startle reflex). Also, facial EMG can be used to track smiles and frowns in order to infer one’s emotional valence.

Electorcardiography (EEG)

Track heart rate, or pulse, from electrocardiography (ECG) electrodes or optical sensors (Photoplethysmogram; PPG) to get insights into respondents’ physical state, anxiety and stress levels (arousal), and how changes in physiological state relate to their actions and decisions.

How to Put it Together for Human Behavior Psychology

While biosensor and imaging methods present unparalleled access into an individual‘s thoughts, feelings, and emotions, the best way to understand someone in entirety is to complement the measurements with more traditional methods, such as with surveys and focus groups.

By combining the measures, we‘re able to interpret both parts of what Kahneman described as System 1 and System 2 – both fast, emotionally driven decisions, as well as slow and deliberate decisions. Utilizing the insights offered by both routes of investigation gives a whole view of the thoughts and behaviors that an individual possesses.

The grid below summarizes the two methods in an overview, and shows how using both can answer a wide array of questions.

Human Behavior Metrics

Metrics are derived from observation or sensor data and reflect cognitive-affective processes underlying overt and covert actions. Typically, they are extracted using computer-based signal pre-processing techniques and statistics. In the following, we will describe the most important metrics in human behavior research.

Emotional valence

One of the most indicative aspects of emotional processing is your face. Facial expressions can be monitored either using facial electromyography (fEMG) sensors placed on certain facial muscles, or video-based facial expression analysis procedures. A very fine-tuned manual observation technique is the Facial Action Coding System (FACS) primarily designed by Paul Ekman. Trained coders, and sophisticated software, can evaluate the amount of activation of modular Action Units (AU), which represent very brief and subtle facial expressions lasting up to half a second.

Based on the sub-millisecond changes in muscular activation patterns or changes in global facial features (lifting an eyebrow, frowning, lifting up the corners of the mouth), behavioral researchers infer universal emotional states such as joy, anger, surprise, fear, contempt, disgust, sadness or confusion.

Emotional arousal

While facial expressions can provide insights into the general direction of an emotional response (positive – negative), they cannot tell the intensity of the felt emotion as described by means of arousal. Arousal refers to the physiological and psychological state of being responsive to stimuli and is relevant for any kind of regulation of consciousness, attention and information processing.

The human arousal system is considered to comprise several different but heavily interconnected neural systems in the brainstem and cortex.

Physiological arousal and emotional valence can be thought of as taking place on a scale, in which both interact with each other. The intensity of arousal therefore influences the intensity of emotion. Capturing data about both of these processes can provide more information about an individual and their behavior.

Although all of these processes are taking place on the microscopic level and cannot be observed with the eye, arousal can be measured by using several psychphysiological methods such as eye tracking, EEG, GSR, ECG, respiration, and more.

Workload and cognitive load Decisions are often made under several constraints (with respect to time, space and resources), and there is obviously a threshold in how much information you can take into consideration. Working memory represents the cognitive system responsible for transient holding and processing of information, and human cognitive-behavioral research has a particular interest in this aspect due to its crucial role in the decision-making process.

The total amount of mental effort being used in working memory is typically referred to as cognitive load.

Perception and attention Do stimuli “pop out” and elicit our interest? Do we watch a movie clip or an advertisement because it is visually captivating? For cognitive-behavioral scientists it is highly relevant to determine the level of saliency of stimuli , and whether or not it captures our attention. Saliency detection is considered to be a key attentional mechanism that facilitates learning and survival. It enables us to focus our limited perceptual and cognitive resources on the most pertinent subset of the available sensory data.

Motivation and engagement Another metric relevant for cognitive-behavioral scientists is motivation, sometimes referred to as action motivation. It describes the drive for approaching/avoiding actions, objects and stimuli.

Shopping behavior is primarily driven by engagement and the underlying motivation to buy a product, therefore it would be beneficial to infer one’s motivation already during the initial exposure with an item. EEG experiments have provided rich evidence for certain brain activation patterns reflecting increased or decreased motivational states.

Besides EEG, one’s level of attention can be determined based on eye tracking , both in lab settings as well as in real-world environments. Remote eye trackers are mounted in front of a computer or TV screen and record the respondents’ gaze position on screen.

Eye tracking glasses are the optimal choice for monitoring attentional changes in freely moving subjects, allowing you to extract measures of attention in real-world environments such as in-store shopping or package testing scenarios.

Application Fields in Study of Human Behavior Psychology

Consumer neuroscience and neuromarketing.

There is no doubt about it: Evaluating consumer preferences and delivering persuasive communication are critical elements in marketing. While self-reports and questionnaires might be ideal tools to get insights into respondents’ attitudes and awareness, they might be limited in capturing emotional responses unbiased by self-awareness and social desirability.

As only so much of our overt, conscious behavior is captured by traditional methods such as surveys and focus groups, biosensors offer a way to fill that gap.

Psychological research

Psychologists analyze how we respond emotionally towards external and internal stimuli, how we think about ourselves and others, and how we behave. In systematic studies, researchers can measure and vary stimulus properties (color, shape, duration of presentation) and social expectations in order to evaluate how personality characteristics and individual learning histories impact emotional, cognitive and perceptual processing.

Media testing and advertising

In media research, individual respondents or focus groups can be exposed to TV advertisements, trailers and full-length pilots while monitoring their behavioral responses, for example, using facial expression analysis . Identifying scenes where emotional responses were expected but the audience just didn’t “get it” is crucial to refining the appeal of the TV-program. Facial expression analysis can also be used to find the key frames that result in the most extreme facial expressions – showing when the program really landed on target.

Software UI and website design

Ideally, handling software and navigating websites should be a pleasant experience – frustration and confusion levels should certainly be kept as low as possible. Monitoring user behavior, for example based on scrolling or click-ratio as well as facial expressions, while testers browse websites or software dialogs can provide insights into the emotional satisfaction of the desired target group.

Eye tracking is a particularly useful technology, as it helps pinpoint exactly what the person is looking at during their experience with the website. When combined with other measures, it gives an insight into what exactly gave them a positive or negative feeling during the interaction.

Human behavior is a multi-faceted and dynamic field of study, requiring many points of interrogation to yield insights. Learning processes lay the foundation for determining many of our behaviors, although we are constantly changing in response to our environment. Understanding our behaviors is a tricky task, but one that we are getting ever closer to accomplishing. Traditional methods of study have taught us many things, and now biosensors can lead the way.

I hope you’ve enjoyed reading this snippet from our Complete Pocket Guide to Human Behavior – if you’d like to learn even more and become a true expert in human behavior, then download our free guide below!

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Delving into Human Behavior: the Art of Naturalistic Observation

This essay about the method of naturalistic observation in psychology, highlighting its unique ability to capture authentic human behavior in real-life settings. It discusses the importance of observing behavior in natural environments, where individuals interact spontaneously, offering insights into social dynamics and generating new research avenues. Despite challenges like observer bias and resource constraints, naturalistic observation remains a valuable tool for understanding the intricacies of human behavior and social interaction.

How it works

In the vast landscape of psychological research, one methodology stands out for its ability to capture the essence of human behavior in its most authentic form: naturalistic observation. Far from the sterile confines of a laboratory, naturalistic observation ventures into the heart of everyday life, unveiling the intricacies of human interaction and behavior within their natural habitat. It is a journey into the realm of genuine experience, where the complexities of social dynamics and individual quirks are laid bare for scrutiny and understanding.

At its core, naturalistic observation offers a unique perspective on human behavior by immersing researchers in the environments where it naturally unfolds. Whether it’s a bustling city street, a tranquil park, or a lively classroom, these natural settings serve as the stage for the drama of everyday life. Here, researchers become silent observers, blending into the background as they witness the ebb and flow of human interaction with an unobtrusive gaze. It is through this lens that the true essence of behavior is revealed, unencumbered by the constraints of artificial experimental setups.

One of the most compelling aspects of naturalistic observation is its ability to capture the nuances of social interaction in real-time. In these natural settings, individuals behave in ways that are spontaneous and unscripted, offering researchers a glimpse into the intricacies of human relationships and social dynamics. Whether it’s the subtle cues of nonverbal communication or the complex interplay of group dynamics, naturalistic observation allows researchers to peel back the layers of social behavior and uncover its underlying mechanisms.

Moreover, naturalistic observation holds immense potential for uncovering unexpected insights and generating new avenues of research. As researchers immerse themselves in the rich tapestry of everyday life, they may stumble upon intriguing patterns or phenomena that spark their curiosity. Perhaps it’s the way pedestrians navigate a crowded street or the dynamics of conversation in a bustling café. These seemingly mundane observations can serve as the seeds for further exploration, leading researchers down unexpected paths of inquiry and discovery.

However, naturalistic observation is not without its challenges and limitations. One of the most significant hurdles is the potential for observer bias, wherein the presence of the researcher may subtly influence the behavior of those being observed. To mitigate this risk, researchers employ a variety of strategies, such as blending into the environment or employing covert observation techniques. Additionally, naturalistic observation can be resource-intensive, requiring researchers to invest significant time and effort in data collection and analysis.

Despite these challenges, the benefits of naturalistic observation are undeniable. By providing a window into the complexities of human behavior in its natural habitat, this approach offers unparalleled insights into the intricacies of social interaction and individual behavior. It is a journey into the heart of what it means to be human, where the mundane becomes extraordinary and the ordinary becomes extraordinary. In the hands of skilled researchers, naturalistic observation is not just a tool for understanding behavior; it is a gateway to a deeper understanding of the human experience itself.

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AI Systems Displaying Deceptive Behavior Raises Concerns, Research Shows

A recent study emphasizes the increasing problems with artificial intelligence (AI) systems’ deceptive responses. This is according to a review paper published in the journal Patterns about current AI systems, which were created to be honest but have acquired the unpleasant power of deception, from fooling human players in online games of world dominance to employing people to solve “prove-you’re-not-a-robot” tests.

The study, led by Peter Park, a postdoctoral fellow at Massachusetts Institute of Technology who specializes in  AI existential safety, highlighted that while such examples may seem minor, the problems they uncover could soon become very real.

Park stated that, as opposed to traditional software, deep-learning AI systems are not “written” but “grown” through some form of selective breeding. Therefore, AI behavior that seems predictable and manageable in training will become unpredictable as soon as it is out there.

Examples of Deception

The study explored the different situations in which AI systems showed deceitful behaviors. The research team’s ideas originated from Meta’s AI system Cicero, created to compete in Diplomacy, a game where making alliances is crucial.

Cicero performed exceptionally well, scoring at a level that would position it in the top 10% of experienced human players, as reported in a 2022 paper published in Science.

For instance, ,Cicero playing as France, tricked England (a human player) into invading by collaborating with Germany (another human player). Cicero gave England protection, then, behind their backs, told Germany that England was ready to attack, abusing their trust.

Meta neither confirmed nor denied that Cicero was deceptive, but a spokesperson commented that it was a purely research based project and the bot was just built for playing Diplomacy in the game.

According to the spokesperson, “We released artifacts from this project under a noncommercial license in line with our long-standing commitment to open science. Meta regularly shares the results of our research to validate them and enable others to build responsibly off of our advances. We have no plans to use this research or its learnings in our products.” 

Another example is when OpenAI’s Chat GPT-4 tricked a TaskRabbit freelancer into completing an “I’m not a robot” CAPTCHA task. The system, in addition, tried insider trading in the simulated exercise envisaged, where it was told to convert itself into a pressurized stock trader without being further instructed.

Potential Risks and Mitigation Strategies

The research team emphasized the short-term dangers of deception committed by AIs, like fraud and election meddling. Furthermore, they believe that a super-AI could direct power and control society, deriving humans from it, while his “strange purpose” could result in human overthrow or even extinction if its interests match these.

To mitigate the risks, the team proposes several measures which include, “bot-or-not” laws that demand company disclosure of human or AI interactions, digital watermarks for AI-generated information, and developing methods to spot AI deception by looking into the connection between the internal thought process of AI and their external activities.