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What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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What is and How to Write a Good Hypothesis in Research?

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One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

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Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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Hypothesis or Thesis

Looking for the author's thesis or hypothesis.

The image below shows the part of the scholarly article that shows where the authors are making their argument. 

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• The first few paragraphs of a journal article serve to introduce the topic, to provide the author's hypothesis or thesis, and to indicate why the research was done.  
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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Principles of Research Methodology pp 31–53 Cite as

The Research Hypothesis: Role and Construction

• Phyllis G. Supino EdD 3  
• First Online: 01 January 2012

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A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

How to Write a Research Hypothesis

• Research Process
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Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

What is a research hypothesis?

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

Research Hypothesis

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

Simple hypothesis

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

Complex hypothesis

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

Directional hypothesis

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

Associative hypothesis

An associative hypothesis says that when one variable changes, so does the other variable.

Alternative hypothesis

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

Null hypothesis

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

Logical hypothesis

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

Empirical hypothesis

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

Statistical hypothesis

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

Causal hypothesis

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

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

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

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HYPOTHESIS AND THEORY article

Social intuition: behavioral and neurobiological considerations.

• School of Psychology and Social Work, University of Hull, Hull, United Kingdom

Social intuition is instrumental in bringing about successful human interactions, yet its behavioral and neural underpinnings are still poorly understood. We focus in this article on the automatic, involuntary, nature of social intuition, rather than on higher-level cognitive and explicit Theory-of-Mind processes (which contribute to rendering social intuition meaningful in real-life situations). We argue that social-affective implicit learning plays a crucial role in establishing automatic social intuition. These implicit learning processes involve associations between the perception of other’s bodily articulations, concurrent events, and the consequences or outcomes in terms of subsequent actions, affective valences and visceral states. The traditional non-social implicit learning paradigms do not allow one to draw conclusions about the role of implicit learning processes in social intuition, as they lack these vital characteristics typically associated with human actions. We introduce a new implicit learning paradigm, which aims to fill these gaps. It targets agile, rapid, social-affective learning processes, involving cue contingencies with a relatively simple structure, unlike the very complex structures that underpin the traditional tasks. The paradigm features matching social and non-social versions, allowing direct comparison. Preliminary data suggest equal performance of TD (typically-developed) and ASC (autism spectrum conditions) groups on the non-social version, but impaired implicit learning in ASC on the social version. We hypothesize that this reflects an anomalous use of implicitly learned affective information in ASC when judging other people. We further argue that the mirror neuron mechanism (MNM), which is part of the Action Observation Network, forms an integral part of the neural substrate for social intuition. In particular as there are indications that the MNM supports action anticipation, and that implicitly learned information can trigger MNM activation, which both seem vital to a social intuition ability. The insights that can be derived from comparing the performances of TD and ASC individuals on (non)social implicit learning tasks, and the implications for the role of MNM activation, are discussed.

1 Introduction

Successful socio-affective human interactions form the glue that binds society, yet we are only just beginning to unravel the underpinning neural and behavioral mechanisms. Typically-developed (TD) individuals differ greatly in their social interaction abilities, while many psychological and neuro-developmental conditions, and mental illnesses, are characterized by profound difficulties in social interaction. A clear example are individuals with autism spectrum conditions (ASC; Autism Spectrum Conditions, also called Autism Spectrum Disorders; DSM-V-TR, 2022 ). The juxtaposition of ASC and TD therefore has the potential to offer insight into the mechanisms and processes underpinning socio-affective interactions.

One fundamental requirement for successful interactions is the skill that allows one to make rapid assumptions about what actions others are likely to take and what their intentions, emotions and thoughts might be. These assumptions should be made in a fairly effortless, automatic, manner, without the involvement of any prolonged, deliberate and effortful reasoning. We commonly call this skill social intuition . Social intuition manifests itself as ‘immediate insight’, and enables the rapid, social decision-making necessary to successfully navigate the ever-changing socially interactive world ( Bechara et al., 1997 ; Lieberman, 2000 ). Task demands and time-restraints during social interactions mean that there is simply no time available to consciously reflect on what judgment to make or action to take. Therefore, individuals who depend upon deliberate, effortful reasoning, are likely unable to keep up with the pace of interactions. This may cause them to quickly lag behind and feel inadequate in social interactions. We propose this happens in ASC.

Social intuition can be considered a subdivision of social cognition. Whereas social cognition generally is concerned with how people, both explicitly and implicitly, process and apply information about other people and social situations ( Frith, 2008 ), social intuition is essentially limited to implicit, automatic, effortless, processes that help to steer one’s behavior in social situations in an advantageous way ( Lieberman, 2000 ). The topicality of the question of how we accomplish the complex task of navigating the social world is reflected by recent propositions for a ‘second-person’ or ‘interactive’ approach in social cognition and social neuroscience ( Schilbach et al., 2013 ; Redcay and Schilbach, 2019 ). This approach typically abandons the traditional use of 2D displays (images and videos), and replaces this with real-world real-time scenarios involving live actors, which brings social intuition to the forefront.

There are three main parts to this article. The first part [2] focusses on the role of implicit learning in forming social intuition. Here we look at the main non-social implicit learning paradigms, and indicate why they are of limited relevance for social interaction [2.1]. These non-social paradigms are then contrasted to social implicit learning paradigms, involving affective valences and bodily articulations [2.2]. We examine whether automatic action anticipation – a crucial ingredient of social intuition – can be brought about by implicit learning [2.3], and compare the performance of individuals with ASC on social versus non-social implicit learning tasks [2.4]. We next look at recent developments that aim to incorporate affective valences and bodily articulations in implicit learning paradigms [2.5].

In the second part [3], we discuss future developments and introduce a new paradigm developed in our lab, where implicit learning is tested in a social version [3.1] and in a matching, non-social, version [3.2]. Preliminary results, interpretations, alternative explanations and relevance are discussed [3.3].

In the third part [4], we examine the neural basis of social intuition. We look in particular at the role the mirror neuron mechanism (MNM) may play in social intuition, and posit that action anticipation is implemented in the MNM, and, importantly, that action anticipation can be triggered by implicitly learned action cues [4.1].

2 Implicit learning as a means to acquire social intuition

It has been suggested that social intuition is supported by a toolbox of social learning heuristics ( Hertwig et al., 2013 ), many of which are implicit in nature. This means that one is not aware that any learning has taken place, even though it clearly affects one’s subsequent decision making. Implicit learning typically applies to knowledge about rule-governed complexities in the environment and received a lot of attention ( Reber, 1989 ; Bargh, 1994 ; Cleeremans, 2011 ; Schilbach et al., 2013 ). Although implicit processing and implicit knowledge proved difficult to define (see Gómez et al., 2017 , for a discussion), one or more of the following adjectives are typically used to describe it: unintentional, uncontrolled, goal-independent, automatic, stimulus-driven, unconscious, efficient, effortless, fast and inflexible ( Shiffrin and Schneider, 1977 ). From this list it is clear that a number of different dimensions are involved. Rather than each dimension forming a necessary constituent, what may matter most for defining implicitness is their relative contribution ( Bargh, 1994 ), with unawareness ( Moors and De Houwer, 2006 ) and automaticity ( De Houwer and Moors, 2012 ) probably the most cited features.

Explicit learning processes, which often involve a form of deliberate hypothesis testing, are described by the opposite adjectives: intentional, controlled, goal-dependent, deliberate, top-down driven, conscious, inefficient, effortful, slow and flexible. Implicit and explicit processes may operate independently (dual process operation, Kliemann et al., 2013 ), and may be based on different mechanisms. This is supported by findings that the underpinning neuronal circuits may be dissociable, with implicit tasks showing in particular activation in the striatum and basal ganglia (e.g., implicit sequence learning; e.g. Rauch et al., 1997 ), anterior cingulate cortex and cerebellum, and explicit tasks showing in particular activation of prefrontal and visual cortical areas ( Aizenstein et al., 2004 ). The relevance of the implicit-explicit distinction has been introduced very effectively to the wider public by Kahneman (2011) in his book “Thinking, Fast and Slow”.

2.1 Non-social implicit learning paradigms

Humans seem to have the capacity to extract, and use, knowledge they apparently have no conscious access to. It could be that this knowledge was initially acquired explicitly and consciously, but was subsequently forgotten. Yet, the knowledge did not vanish from the brain, and kept on exerting an unconscious influence on behavior and decision making. However, it could also be that the knowledge never reached consciousness in the first place, but was learned implicitly.

With respect to this latter route, the literature shows an abundance of implicit non-social learning studies, demonstrating that one can learn about the sequential structure or rule that governs non-social, artificial, stimuli presentations (such as dots or letters), without one being explicitly aware of this underlying structure or rule. These non-social implicit learning tasks typically require hundreds of trials to induce a learning effect. Often cited examples are the Serial Reaction Time (SRT) task and the Artificial Grammar Learning (AGL) task. In typical SRT tasks ( Nissen and Bullemer, 1987 ) stimuli are presented in one of four quadrants on a screen, and participants need to give a speeded response that corresponds to the specific quadrant where the stimulus appeared. Even though participants show no awareness or knowledge about the sequence, they consistently improve their reaction times over the course of the task, suggesting they had unconsciously learned the underlying pattern. In AGL tasks ( Reber, 1967 ), participants are first exposed to meaningless strings of letters (e.g., XMXTVM; XXTRM) that are structured by a hidden, complex, rule (i.e., an artificial grammar). If, in a subsequent test phase, participants are able to accurately judge whether new strings follow the artificial grammar or not, then that suggests that they implicitly learned the underlying grammar ( Reber, 1967 ; Dienes and Scott, 2005 ). Crucially, participants who implicitly learned the grammar often claim to respond at random, or to rely on an intuition they cannot further explain ( Dienes and Scott, 2005 ).

The processes of unconscious knowledge acquisition, through implicit/unconscious learning, that emerge from these implicit learning paradigms, have been proposed to affect human behavior not only in laboratory paradigms, but also in the real-world social domain ( Hudson et al., 2012b ; Jurchiș and Dienes, 2023 ). However, this poses some serious problems. The non-social, implicit learning paradigms, that measure the capacity to implicitly pick-up on complex regularities between artificial, meaningless stimuli (dots, isolated letters), may be of no value for social interactions nor for social cognition in general. Such stimuli have no social significance, and the very large number of repetitions required to learn the regularities may render it impractical to be relevant for social interactions (though situations are conceivable where exposure to social ‘rules’ results in implicit learning only after large number of repetitions). Last but not least, social interactions are characterized, and guided by, affective valences, and by attitudes and dispositions, associated with human actions. All of these are wholly missing in the non-social paradigms.

2.2 Social implicit learning paradigms: affective valences and bodily articulations

Greenwald and Banaji (1995) defined implicitly learned material as introspectively unidentified (or inaccurately identified ) traces of past experience that mediate favorable or unfavorable feeling, thought, or action toward objects. This definition acknowledges the role of affective valences in implicit learning. It has been argued that for cognition to be fully effective, it is not enough that the agent is able to understand and predict developments in the environment, it must also care about them, it must desire certain types of outcomes and shun others ( Cleeremans, 2011 ). In this view, emotions are computational tags that subserve and facilitate cognitive processes. This may be true not only for explicit learning but also, and maybe especially, for implicit learning. It has been shown that an affective valence gets attached to a stimulus also when that stimulus is not consciously recognized ( Kunst-Wilson and Zajonc, 1980 ). These implicitly associated affective valences may well be essential to make the rapid, intuitive, decisions that are beneficial to the individual and ultimately support their survival ( Cleeremans, 2011 ). This view, in which emotions are computational tags that subserve and facilitate cognitive processes, is somewhat reminiscent of Damasio’s somatic marker hypothesis ( Damasio, 1994 ).

During real-life social interactions, the relevant stimuli usually consist of the other’s actions, gestures, facial expressions and vocalizations, i.e., bodily articulations of one sort or another. Picking up on regularities and contingencies between the occurrence of these bodily articulations, contextual cues, and the immediate consequences (in terms of subsequent actions and/or subsequent reward/punishment) is an important source for implicit learning about others. Bodily articulations and affective valences thus seem necessary ingredients in any future implicit social learning paradigm.

There are a few cases where an attempt was made to include the social domain in an implicit learning paradigm (e.g., Brown et al., 2010 ; Travers et al., 2013 ). However, in these cases, the social nature of the animate objects was not pertinent to the paradigm and could just as well have been replaced by inanimate objects (i.e., affective valences, attitudes and dispositions played no part). One striking example of implicit social learning though was reported by Bayliss and Tipper (2006) , who found that participants who had been presented with different actors who either consistently looked at, or away from, the location where a target would appear, subsequently judged those actors that had looked away from the target as the least ‘trustworthy’ and those who had looked at the target as the most ‘trustworthy’. Crucially, in a debrief, participants did not recall any actor-gaze-cue contingencies, suggesting the contingencies had implicitly affected their social judgments. A further interesting finding was that this effect correlated negatively with the participants’ scores on the Autism Quotient (AQ; Baron-Cohen et al., 2001 ).

2.3 Anticipation of others’ actions based on implicitly learned cues

A social intuition, revealing itself as a sudden insight, inclination or drive relating to another individual, is typically prompted by the observation of an action or bodily articulation performed by that individual. However, it would be even more advantageous if one would be able to anticipate the other’s action and then for that anticipation to prompt the intuition. That would save precious time, which can be used to prepare one’s response in accordance with the intuition, or to coordinate one’s own actions with those of others ( Csibra, 2008 ). Especially in the fast-paced social interactions, this would be very beneficial. The automatic anticipation of others’ actions is therefore thought to be an essential building block of social intuition ( Hudson et al., 2009 , 2012a ; Krol et al., 2020 ). But what could be the source of such an anticipation, what could it be based on? First of all, actions typically do not occur in isolation, but in chains, where one sub-action is immediately followed by another sub-action and so on, resulting in an action-chain that serves a purpose (achieves a goal). Reaching out for a cup of coffee, bring it to the mouth, take a gulp and place the cup back. Once the first action of the chain started, it is able to trigger the entire action-chain almost instantaneously, like a row of falling dominos, unfolding ahead of real-time developments, and thus allowing the observer a glimpse of the future. The triggering event could also precede the action, it could for example be formed by the other’s initial attention being directed at the object ( Jellema et al., 2000 ). In general, others’ actions seldom commence unheralded, they usually are foreshadowed by cues, which can be action cues (as above) or originate in the environment. Environmental/contextual cues can be artificial and explicit, such as a pedestrian traffic light jumping to green (the person waiting is expected to start walking), but can also be subtle and implicit.

Especially in social interactions, cues tend to be subtle and implicit ( Amso and Davidow, 2012 ). Here, learning opportunities are often not explicit, as the learned cue-action contingencies may not reach consciousness ( Monroy et al., 2019 ). The observer may remain completely unaware that any knowledge has been acquired, but the cue will nevertheless induce automatic anticipations in the observer regarding others’ upcoming actions ( Braukmann et al., 2017 ; Monroy et al., 2019 ). For example, when someone slightly moves in their chair in preparation to stand up, or slightly raise their hand when they are about to say something. Or when a nurse immediately sees what the patient wants to do, or a flight attendant immediately gauges that a passenger is going to cause trouble. Their insights are based on the automatic interpretation of a constellation of often simultaneously occurring cues. However, when one would ask the nurse or flight attendant how they reached their judgment, they may not even be able to recall the exact cues, they just ‘saw’ it. Nevertheless, a lot of implicit learning must have taken place before they were able to pick up on the relevant cues and then just ‘saw’ it (a naïve observer would have been oblivious). Without this implicit learning ability, other’s actions would indeed often come unheralded, with the potential to surprise and bewilder any observer. This is, however, exactly how individuals with ASC, who arguably lack social intuition, often describe their experiences of others’ actions. For them, deciphering the intended meaning of such actions is often anything but effortless and automatic.

2.4 Implicit learning in autism

ASC is a pervasive neurodevelopmental condition characterized by atypicality in social communication, impaired social development, and stereotypical, repetitive behaviors, often associated with obsessive interests and a lack of empathy ( DSM-V-TR, 2022 ). Symptom severity varies hugely in ASC; three levels are identified based on severity and need of support. Already at Level 1 (previously called high-functioning autism), which is the least severe level with normal IQ distribution and no, or limited, need for support, the difficulties in social and emotional domains are marked. Intriguingly, it has been suggested that the deficits in the social domain that characterize ASC may have their roots in impairments in implicit (spontaneous) processing, with relatively intact explicit (deliberate) processing ( Jellema et al., 2009 ; Senju et al., 2009 ; Schilbach et al., 2012 ). These ideas align with the Dual Process Theory of Autism ( Evans, 2003 ), which proposes a dominance of deliberative (Type 2 processing) over intuitive (Type 1) processing. This proposition seems plausible, as in the social domain it is especially the quick and intuitive interpretation of others’ non-verbal behavior that is problematic in ASC. However, studies aimed specifically at finding out whether implicit learning is impaired in ASC concluded that it is intact (e.g., Gordon and Stark, 2007 ; Barnes et al., 2008 ; Brown et al., 2010 ; Nemeth et al., 2010 ; Foti et al., 2015 ). Crucially though, all these studies had one important limitation in common: they focused exclusively on implicit learning in the non-social domain, using tasks involving probabilistic sequence rules. The social domain, where implicit learning relies on affective valences associated with the stimuli, and abstract concepts such as dispositions and intentions, was not incorporated. It may well be that different mechanisms are at play in the implicit learning of social, as compared to non-social, information.

2.5 Recent advances in the study of social implicit learning

Recently, a few studies aimed to specifically address the limited relevance of the non-social learning paradigms for social interactions ( Norman and Price, 2012 ; Zhang et al., 2020 ; Costea et al., 2023 ; Jurchis et al., 2023 ; Jurchiș and Dienes, 2023 ). For example, Jurchis et al. (2023) took the approach to try and ‘socialize’ the traditional Artificial Grammar Learning (AGL) task. In the traditional AGL task, strings of meaningless letters are presented in the acquisition phase. In Jurchis et al. (2023) , these strings of letters were replaced by strings of emotional facial expressions (all faces in one and the same string belonged to the same identity). As in the traditional AGL task, the strings of faces followed a hidden grammar, determining a number of specific sequences in which the faces were presented. In this way, Jurchis and colleagues were able to subject their implicit social learning paradigm to the same rigorous methods and principles that govern non-social implicit learning paradigms. At the start of the acquisition phase, participants were told that they had to remember the stimuli in each string. At the start of the test phase, they were told that the sequences in which these stimuli were presented had actually been specified by a complex set of rules, and that their task was to determine whether new test strings followed these rules or not. The number of repetitions of strings in the acquisition phase was also dramatically reduced compared to the non-social equivalent. In the test phase, where participants indicated whether the new strings followed the rule or not, in 70.6% of the trials they reported that their judgments were based on implicit/unconscious knowledge (the participants indicated that the remaining judgments were based on explicit knowledge). The implicit/unconscious judgments turned out to be correct in 58.2% of trials, which was significantly above-chance. In a further experiment in Jurchis et al. (2023) , and also in Jurchiș and Dienes (2023) , the same paradigm was used but with stimuli consisting of strings of martial art poses. These poses were ‘social’ in that human beings were presented displaying meaningful bodily articulations, yet (presumably) lacking in emotional valence. Similarly, in 67.5% of the trials, participants attributed their responses to unconscious knowledge, and in 57.5% of these latter trials the correct answer was given, again significantly above-chance.

Thus, implicit learning could be induced using social stimuli with relatively small numbers of repetitions, both when affective valences were associated with these stimuli and when not. However, these paradigms still do not resemble the patterns and types of stimuli that characterize real social situations, nor are emotional, motivational and interactive aspects of real social situations included. A few issues in particular spring to mind when considering the relevance of these tasks for daily-life social intuition. (i) While emotional facial expressions are highly relevant for social interactions, the simultaneous presentation of multiple facial expressions belonging to one and the same individual is impossible to encounter in real-life. Although the task recruits emotional processes, it is not done in an ecologically meaningful way. (ii) The participants were instructed to direct their attention to the very stimuli whose appearance was specified by the hidden rules. In daily-life interactions, the contingencies/regularities between bodily cues and their consequences typically do not form the focus of attention. Rather, attention may be focused on the joint activity (e.g., a conversation) one is engaged in, while the accompanying bodily cues that trigger anticipations may very well not be noted consciously. It is therefore unclear to what extent participants’ behavior in this task informs about what people can learn from regularities/contingencies in real-life situations.

3 Future developments in implicit social/affective learning paradigms

In a series of recent experiments, Macinska and Jellema used a new implicit learning paradigm, designed to be particularly relevant for real-world social interaction ( Macinska et al., 2015a , b ; Macinska and Jellema, 2016 ; Macinska, 2019 ). The core paradigm was developed in Hudson et al. (2012b) . The implicit social learning induced by this paradigm is characterized by rapid, agile, incidental processes that track relatively low order stimulus combinations, rather than slower processes that track very complex sequences and combinations, as in the traditional implicit learning paradigms. It requires a relatively small number of repetitions (about 10) to induce implicit learning effects, and allows to specifically compare participant’s abilities for social versus non-social implicit learning. With respect to the latter, a non-social paradigm was designed that matched the social paradigm, as far as possible, in terms of the number of cues, internal structure and difficulty-level. This direct comparison is important as there is still a large gap in our knowledge about the extent to which the distinction between the social and non-social domain is relevant. Thus, in both paradigms a specific contingency between three different cues had to be learned. The main difference was, however, that implicit learning in the social paradigm crucially depended on affective valences, which were absent in the non-social paradigm, where learning depended purely on stimulus contingencies. The social cues were deliberately chosen for being straightforward and simple, so that ASC participants should not experience problems in deciphering their meaning. This was done to ensure that possible difficulties in implicit learning of the social contingencies cannot be attributed to difficulties with representing the social information per se .

Importantly, in the acquisition phases of both paradigms, contingencies rather than probabilities, have to be learned. That is, a rule determined specific contingencies between coordinated changes in the appearances of three distinct cues (each with two levels). The specific appearance of the level of one cue can be predicted with 100% certainty on the basis of the appearances of the levels of other two cues. It should be noted that in these paradigms, the participant is asked to simply watch the video-clips; there was no attempt to draw their attention to certain stimuli and they were not given a task to do. The acquisition phase was rather short (4 min), so they should be expected to maintain attention throughout that period.

3.1 Social version

3.1.1 acquisition phase.

In the social version, the three cues were: dynamic facial emotional expressions (levels: happiness vs. anger), gradual eye gaze shifts (levels: toward vs. away), and identity (levels: identity A vs. identity B). The advantage of using dynamic facial expressions is that they are more ecologically-valid ( Jellema et al., 2011 ; Palumbo and Jellema, 2013 ; Palumbo et al., 2015 ; Sato et al., 2019 ). Participants were presented with short video clips (2 s) depicting the frontal face view of an actor (agent A or agent B; Figure 1 ). Their facial expressions and gaze directions changed smoothly over the course of the clips, displaying a natural facial movement. Fifty percent of trials showed actor A, the other 50% actor B, in random order. Actor A started with a happy expression looking straight ahead (at the observer), which then gradually morphed into an angry expression, while simultaneously the eye direction gradually moved away from the observer (so that in the final frame the actor looked angry away from the observer). The clips were also played backwards an equal number of times. Thus, it can be said that agent A had a positive disposition toward the observer. Agent B started with a happy expression looking away from the observer, which gradually morphed into an angry expression, while simultaneously the eye direction gradually moved toward the observer (clips were also played backwards an equal number of times). Actor B thus had a negative disposition toward the observer. Importantly, both actors smiled and frowned for exactly the same amount of time and looked at, and away from, the observer for exactly the same amount of time. This was to ensure that the actor’s pro- or anti-social disposition toward the observer could only be learned on the basis of the specific combination of two cues linked to an identity; the agent’s disposition could not be learned on the basis of each cue on its own. Too many repetitions meant most participants would detect the contingency (i.e., explicit learning), while with too few repetitions no implicit learning might take place.

Figure 1 . Schematic representation of the social and non-social implicit learning paradigms. The social identities are shown with differently colored hair for the purpose of illustrating their different identities. Responses in the Test phase (‘A’ and ‘B’) that reflect correct implicit learning are underlined.

3.1.2 Test phase

In the subsequent test phase, an indirect measure was used to find out whether any implicit learning had taken place. This measure involved a morph of the facial expressions of the two identities A and B, flanked by the original neutral faces of these two identities. The morphed identity was composed of 60% of the maximally smiling actor A and 40% the maximally smiling agent B (happy morph), and then progressed in steps of 5% toward 40% of the maximally smiling actor A and 60% the maximally smiling agent B. The same procedure was followed for the frowning actors A and B. Participants had to indicate for each morphed identity whether it resembled more closely agent A (who had a positive disposition toward the observer) or agent B (who had a negative disposition toward the observer). The rationale was that when participants had implicitly learned that identity A had a positive, and identity B a negative, disposition toward them, then they would be more likely to judge the smiling morph (containing 50% of A and 50% of B, both smiling maximally) as more similar to identity A, and the frowning morph (containing 50% of A and 50% of B, both frowning maximally) as more similar to identity B. This is expected, as, intuitively, they would associate identity A with a positive, and identity B with a negative, valence.

Whether or not the participant had consciously detected the cue-identity contingencies was determined in a short debrief session, in which a series of questions were asked probing any awareness of the contingencies. Participants who had detected the contingencies were removed from the analysis.

3.2 Non-social version

3.2.1 acquisition phase.

Two different shapes, a square and a circle, were used as the equivalent of the two identities A and B. The color of these objects – light blue or dark blue – was used as the equivalent of facial expressions of joy and anger, respectively. Therefore, a smooth dynamic change from light-blue to dark-blue, and vice versa, served as the equivalent of the changes in facial expression between joy and anger. A small red object positioned at the top or bottom position inside the bigger blue object, served as the equivalent of gaze direction (toward and away, respectively), while the dynamic, smooth up-or down-ward movement of this red object within the larger blue object served as the equivalent of the change in eye gaze direction. Vertical, rather than horizontal, movements were chosen to avoid interpretation of the small red objects as eyes, which might bestow the stimulus with animacy. Half of the clips started with the light-blue square, with the small red object at the top, which then gradually morphed into a dark-blue square with the small red object at the bottom (also played backwards). The other half of the clips started with the light-blue circle with the small red object at the bottom, which gradually moved into a dark-blue circle with the small object at the top (also played backwards).

3.2.2 Test phase

The nonsocial test phase was, as far as possible, equivalent to its social counterpart: morphs of the square and circle were presented (in 5% steps), in either a dark-blue (≈ anger) or light-blue (≈ happy) color, with the small red object shown at the top, or bottom, of this morphed object (producing four different morph configurations; see Figure 1 , Test phase nonsocial). Target objects were flanked by the two original objects, which were shown in a color that was exactly midway the light-and dark-blue colors of the Acquisition phase (≈ neutral expression, midway happy and angry). Participants had to indicate whether the target object resembled more closely object A or object B. The rationale was that when participants had implicitly learned the specific contingencies, then they would judge the morphed target object to be more similar to a circle if the little red object was (i) at the top of the dark-blue target object, or (ii) at the bottom of the light-blue target object. Similarly, they would judge the target as more similar to a square if the small red object was at the top of the light-blue target, or at the bottom of the dark-blue target. As in the social condition, a debrief was held in which questions probed awareness of the stimulus contingencies. This showed that, similar to the social version, none of the participants had detected the stimulus contingencies.

In principle, this paradigm is open to a simpler type of learning, namely perceptual learning. That is, it could be that participants explicitly remembered the perceptual image of for example agent A with a smile and forward directed gaze. When, in the test phase, the morphed target is shown with a smile and eyes directed forward, then this could trigger the perceptual image of agent A with a smile looking forward. This would mean that the participant could give the correct response (‘agent A’) without having implicitly learned that agent A holds a positive disposition toward the observer. To avoid this possibility, the morphed target of the test phase was shown with the eyes covered by dark sunglasses obscuring visibility of eye gaze direction ( Figure 1 ; note that the dark sunglasses did not prevent the morphed agent from expressing a disposition).

3.3 Preliminary results and hypotheses for future studies

We present here unpublished results based on 61 TD individuals who performed the Social task and on 65 participants who performed the Nonsocial task (37 of them performed both tasks; Social task: Age, M = 21.5, SD = 5.6; 25 males; Nonsocial task: Age, M = 20.7, SD = 4.7; 22 males; Figure 2 ). Two-way repeated measures ANOVAs were performed on the participants’ judgments in both tasks. In the Social task, the two factors were Disposition (two levels: negative disposition or anti-social vs. positive disposition or pro-social) and Proportion (five levels: proportions of agents A and B contained in the Target face, ranging from 60%A/40%B, to 40%A/60%B, in steps of 5%). The main effect of Disposition was significant [ F (1, 60) = 8.2, p = 0.006, η p 2 = 0.12], reflecting that smiling Target faces were judged to resemble more the agent with the pro-social than the agent with the anti-social disposition, while frowning target faces were judged to resemble more the agent with anti-social than with pro-social disposition. The main effect of Proportion was also significant [ F (4, 240) = 32.7, p < 0.001, η p 2 = 0.35]. The Disposition by Proportion interaction effect was non-significant [ F (4, 240) = 0.99, p = 0.42, η p 2 = 0.016]. In the Non-social task, the two factors were Object (two levels: square vs. circle) and Proportion (five levels: proportions of objects A and B contained in the Target object, changing in steps of 5%). A similar pattern emerged as for the Social task, with significant main effects for Object [ F (1, 64) = 41.6, p < 0.001, η p 2 = 0.39] and Proportion [ F (4, 256) = 15.9, p < 0.001, η p 2 = 0.20], and no significant interaction effect [ F (4, 256) = 0.83, p = 0.51, η p 2 = 0.013]. Thus, in both tasks, significant implicit learning effects were found.

Figure 2 . Task performance. The judgments made by TD individuals regarding the morphed target stimuli are presented for the Social implicit learning task (left panel) and for the Non-social implicit learning task (right panel).

Since the participants’ Autism Quotient scores (AQ; Baron-Cohen et al., 2001 ) had also been measured, we further explored any influence of AQ scores on implicit learning ability. AQ scores indicate the extent to which someone possesses autistic-like traits (ranging from 1 to maximally 50; higher scores reflecting a higher extent of autistic-like traits). Previous work indicated that the TD samples scoring low and high on the AQ may perform significantly different on social cognition tasks ( Burnett and Jellema, 2013 ; Macinska and Jellema, 2022 ). The 61 TD individuals who performed the Social task had a mean AQ score of 17.6 (SD = 6.8, range 5 to 32); the 65 individuals who performed the Non-social task had a mean AQ score of 18.0 (SD = 6.5, range 7 to 32). To explore the hypothesis that autistic traits have a differential influence on implicit social/affective learning versus non-social learning, we computed the Pearson correlation coefficient to assess the linear relationship between AQ scores and a measure of implicit learning for both the Social and Nonsocial tasks. For the Social task, this measure was defined as the difference between the scores obtained for the 50%A/50%B frowning and smiling Targets. For the Nonsocial task, this measure was the difference between the 50%A/50%B light-blue and dark-blue colored Targets. Intriguingly, there was a significant negative correlation for the Social task [ r (62) = −0.36, p = 0.004, two-tailed], indicating that autistic traits influenced the social/affective implicit learning ability (individuals having more traits being worse at it), while there was no significant correlation for the Nonsocial task [ r (62) = −0.052, p = 0.69, two-tailed], indicating that autistic traits did not influence the nonsocial implicit learning ability. Future experiments will test individuals with ASC on these tasks. Our hypothesis is that the trend described above showing poorer implicit social/affective learning in the TD individuals with higher AQ scores, will become more prominent in individuals with ASC, while implicit learning in the nonsocial task will remain unaffected by ASC ( Figure 2 ).

This outcome would suggest that a failure of the ASC group to implicitly learn in the social condition would not be due to an inability to implicitly learn cue contingencies per se (they are hypothesized to perform as well as the TD participants in the non-social condition), but may be related to an impairment to learn implicitly on the basis of affective valences.

A possible alternative explanation for the results, which does not assume an impairment in implicit affective learning in individuals high in autistic traits, is anomalous monitoring of the other’s eye gaze. If these participants would avoid looking at the agent’s eyes, or would avoid looking at direct gaze, then that would have repercussions for learning of the contingencies. Therefore, the scanning patterns of these facial expressions were examined in both TD and ASC groups in a separate study ( Macinska et al., 2023 ). The study focused on eye-tracking using visual stimuli identical to those described here, but did not involve a test for implicit social learning. It revealed that both the TD and ASC groups spent most of their time fixating the agent’s eyes, irrespective of gaze direction, which renders this explanation unlikely.

Another alternative explanation might be that individuals with low and high AQ scores differed in their memory for the facial expressions for particular gaze directions (i.e., an interaction between expression and gaze). For example, if the individuals high in autistic traits were poor in remembering facial expressions of anger when the gaze was directed at them, but were fine with remembering expressions of anger with gaze directed away, then that might go some way in explaining the results. However, in a study specifically addressing memory for facial expressions and possible modulation by gaze direction, using identical visual stimuli ( Macinska and Jellema, 2022 ), autistic participants remembered the facial expressions of previously encountered persons as well as TD participants, without any interaction effects with gaze direction. Note that implicit learning was not measured in Macinska and Jellema (2022) . Thus, this explanation can also be excluded, leaving the impaired implicit social-affective learning explanation as a viable option.

It is further possible that the agent’s positive affect directed at the observer acted as a reward, which might have facilitated implicit learning. Such an effect would then be expected to be less pronounced in individuals who are somehow less susceptible to social rewards. It has been argued that individuals with ASC might possess a lowered ability to implicitly associate a reward value to a social stimulus, resulting in reduced social motivation ( Dawson et al., 2005 ; Chevallier et al., 2012 ; Panasiti et al., 2015 ). Remarkably, these processes may be influenced by gender. In an fMRI (functional magnetic resonance imaging) study, Lawrence et al. (2020) , using an instrumental implicit learning task, found reduced sensitivity to social reward (i.e., smiling faces) in frontostriatal and limbic structures in boys, but not in girls, with ASC. Reduced sensitivity to social reward in individuals high in autistic traits could in principle have contributed to our results. However, the majority of the participants in that group were female and thus, presumably, were sensitive to social reward.

Finally, it is possible that the presentation of dynamic facial expressions might have biased against implicit learning in individuals high in autistic traits, as it has been reported that the perception of dynamic, but not static, facial expressions, reduced activity in the amygdala and fusiform gyrus in ASC individuals ( Pelphrey et al., 2007 ). However, the clips’ initial and final static frames, presented for 750 ms each, were most informative for implicit learning about the agent’s disposition, and we therefore do not expect this to be a major factor.

Which aspects of social intuition, as we defined it, does the newly designed task tap into? We described the social intuition ability as ‘the skill that allows one to make rapid, assumptions about what actions others are likely to take and what their intentions, emotions and thoughts might be’. We further argued that this is mainly achieved through implicit learning of regularities and contingencies between bodily articulations, contextual cues, and the immediate consequences (in terms of subsequent actions and/or reward/punishment)’. The ability purportedly measured by the current task supports the sub-part of social intuition concerned with unconsciously associating a positive or negative valence with a particular identity on the basis of the implicit learning of specific contingencies between bodily articulations performed by that identity. In the new paradigm, the main bodily articulation consisted of a gradual change of an angry facial expression into a happy one, or vice versa. A change into a happy expression, however, does not necessarily result in the agent having a positive disposition toward the observer. For that to happen, the change in facial expression needed to be accompanied by another bodily articulation: a simultaneous change in gaze direction toward the observer (a change in opposite direction would not have led to a positive disposition toward the observer).

4 Neural basis of social intuition

The neural basis of social cognition has been the subject of extensive investigation, largely focusing on the deliberate, conscious, social cognitive processes (e.g., Frith, 2007 ; Adolphs, 2009 ). However, the neural basis of specifically social intuition, which is largely automatic and unconscious in nature and comprises of processes leading to decisions, responses and insights that are predominantly based on the perception of others’ bodily articulations, has received much less attention. Hence, the current state of knowledge of the neural basis of social intuition is limited ( cf. Wachowicz, 2020 ).

We posit here that the neural basis of social intuition may be linked to one of the key functions attributed to the mirror neuron mechanism (MNM; Rizzolatti and Craighero, 2004 ). The MNM ‘matches’ the visual description of another’s bodily articulation or action (either visually perceived or imagined) with an activation of those cortical motor circuits that are responsible for the execution of that same action, without it resulting in an overt execution of the action. The motor activity, as it were, mimics, or mirrors, the perceived (or imagined) action. How the ‘matching’ between observed and executed actions comes about remains to be elucidated, but associative learning processes are likely to be involved. Some even argue that the ‘mirroring’ is caused entirely by associative learning processes, starting from a very early age, since the baby/infant tends to look at their own actions, for example to guide their actions to a desired goal ( Heyes, 2010 ). According to the latter view, the functional significance of the mirroring process may be limited, as it is seen as merely a by-product of associative learning. However, the ‘accidental’ forming of connections between matching visual and motor representations may in fact, rather than being a useless by-product, have far-reaching consequences. Furthermore, the contribution of associative learning processes does not exclude the possibility that brains are programmed to project visual descriptions of others’ actions (perceived or imagined), represented in the superior temporal sulcus (STS; Jellema and Perrett, 2003a , b ; Pitcher and Ungerleider, 2021 ), onto the brain substrate that generates the execution of that action (without there being any intention to execute the action). The STS indeed projects heavily onto the parietal areas of the MNM (e.g., Rizzolatti and Luppino, 2001 ; Rozzi et al., 2006 ). In addition, the inferior temporal lobule projects to parietal mirror areas conveying information about the identity of the agent/object involved in the action ( Borra et al., 2008 ).

Crucial for the proposed role of the MNM in social intuition is that the internal ‘mimicking’ of the observed action automatically makes a wealth of information available to the observer. That is, the off-line ‘execution’ of the observed action activates, apart from the motor representation of the action, also a range of action-associated areas. When one executes a particular action, numerous action-associated aspects, such as the accompanying visceral states ( Critchley and Harrison, 2013 ), the action’s effect or outcome in terms of reward or punishment ( Prinz, 1997 ), the somatosensory feedback, and the most likely subsequent action ( Braukmann et al., 2017 ) and/or response by another individual ( Hunnius and Bekkering, 2010 ), all occur close in time to the action execution, and therefore get linked to the action representation. Thus, the mere observation of an action will make these linked aspects instantly available to the observer, which helps to immediately ‘understand’ the action in terms of its direct consequences and affective significance. In this view, the ‘mirroring’ may subserve the immediate, automatic, understanding of other’s bodily articulations ‘from within’ ( Rizzolatti and Sinigaglia, 2013 , 2023 ), on the basis of one’s own motor repertoires, experiences and feelings. The term ‘understanding from within’ alludes to the experiential nature of the neural processes involved, in contrast to a deliberate, effortful, inferential understanding of others, which is usually indicated as explicit Theory of Mind. Thus, one could envisage the MNM as part of the neural substrate for social intuition, enabling the quick ‘understanding’ of others’ actions and gestures, on the basis of merely observing them.

4.1 Neural basis of action anticipation

Another reason for suggesting that the MNM may be part of the neural substrate for social intuition is that there are strong indications that it is sensitive to others’ upcoming actions – i.e. action anticipation – on the basis of contextual cues that signal that the action is forthcoming ( Urgesi et al., 2006 ; Kilner et al., 2007 ; Southgate et al., 2009 ; Maranesi et al., 2014 ; Braukmann et al., 2017 ; Krol et al., 2020 ). As discussed above, the ability to automatically anticipate others’ actions is a crucial component of social intuition. Its neural basis has been investigated, in particular, using EEG (Electroencephalography), due to its superior temporal resolution (anticipation effects may be short-lived, < 1 s; Maranesi et al., 2014 ). In particular, suppression of the power of the mu rhythm (8–13 Hz), which can be recorded over sensorimotor and parietal cortex, has been taken as an index for MNM activity (thus, the larger the suppression the larger the neural activation). The main reason for this latter assumption is that Mu power suppressions occur both during the execution and observation of actions (see Fox et al. (2016) and Hobson and Bishop (2016 , 2017) , for critical evaluations of the supposed Mu-MNM link). Initially, it was thought that the MNM activates exclusively during the course of observed actions (1-to-1 resonation), but evidence is now accumulating that it also activates during the anticipation of upcoming predictable actions ( Umiltà et al., 2001 ; Urgesi et al., 2006 ; Kilner et al., 2007 ; Csibra, 2008 ; Southgate et al., 2009 ; Maranesi et al., 2014 ; Krol et al., 2020 ). Some studies reported anticipatory power suppressions in the Beta frequency range (13–25 Hz; e.g. Braukmann et al., 2017 ; Monroy et al., 2019 ), rather than in the alpha range. In Braukmann et al. (2017) , participants were presented with transient actions (e.g., making a cup of tea) consisting of three distinct action steps increasing in predictability: whereas the goal of step one was ambiguous, the goal of step three was highly predictable. The main finding was that predictive motor system activation, as indexed by beta-band attenuation, increased with increased predictability, with strongest activation prior to the final, most predictable, step. Intriguingly, there are indications that the Mu anticipation effects are much stronger in response to real-world actions presented by a live actor ( Krol et al., 2020 ), as compared to actions presented in videos ( Krol and Jellema, 2022 , 2023 ). It is yet unclear why that is the case, but possibly the observer’s engagement with the observed action plays a role.

It remains to be explored whether individuals with impaired social intuition, such as those with ASC, show less anticipatory mu suppression. If so, then that would be another indication for the involvement of the MNM in social intuition. With respect to Mu suppression during the observation of actions there are conflicting findings, There are reports of significantly weakened Mu suppression in ASC (e.g., Bernier et al., 2007 ; Oberman et al., 2013 ; Dumas et al., 2014 ), but others reported no difference (e.g., Fan et al., 2010 ; Ward et al., 2021 ). Overall there seems to be a trend toward weaker Mu suppression in individuals with ASC compared to TD individuals. The variation can be due to various factors, such as participant age, heterogeneity of the ASC sample, small number of participants, use of intransient actions or static images, or the use of a separate, rather than a within-trial, baseline.

Action anticipation is intrinsically linked to the action-chain organization of motor cortex ( Fogassi et al., 2005 ). Goal-directed actions typically can be delineated as a sequence of discrete sub-actions, and each of these sequences is engrained in motor cortex in a unique manner ( Fogassi et al., 2005 ). This means that neurons coding for say grasping an object to bring it to the mouth are different ones from neurons encoding an identical grasping action, but forming part of another action-chain, such as grasping the object to place it away. This seems an uneconomical way to represent action-chains in motor cortex, but it means that the entire action-chain can be triggered almost instantaneously, allowing the observer a peek into the future and a sense of the action’s goal. Thus, the observer does not need to perform any deliberate, effortful thinking to figure out the next stage of the action, but just ‘sees’ it, as is typical for social intuition. Disruption of the automatic unfolding of action-chains might underpin impairments in reading the goal of others’ actions. Cattaneo et al. (2007) showed, using electromyography recordings, that in children with ASC the automatic unfolding of action-chains indeed seems to be compromised.

Though EEG is well able to capture the relatively fast (< 1 s) anticipation effects, it lacks the spatial resolution to delineate the sensorimotor, parietal and occipital sources of the power suppressions in the 8-13 Hz frequency band. Therefore, fNIRS (functional near-infrared spectroscopy; Chiarelli et al., 2017 ), which depends on the relatively “sluggish” BOLD (blood oxygen level dependent) response, but offers good spatial resolution, in combination with EEG would be a way forward. Like EEG, but unlike fMRI, fNIRS allows real-world live action presentations, which seem a prerequisite to evoke Mu anticipation effects.

Recent studies suggest that anticipatory MNM activity can be triggered not only by explicitly learned information (such as a specific color signal, indicating that an action is upcoming, Krol et al., 2020 ), but, crucially, also by implicitly learned information (e.g., Braukmann et al., 2017 ; Monroy et al., 2019 ). This is highly relevant as it further supports the candidacy of the MNM as neural substrate for social intuition, since, as we argued, implicitly learned information forms the backbone of social intuition. In Monroy et al. (2019) , in the acquisition phase, infants were shown videos displaying a hand performing six different actions uniquely directed at six different toys. Trials consisted either of deterministic action pairs (e.g., action A was always followed, after a 1 s delay, by action B) or of random pairs (e.g., action C was followed, after the 1 s delay, by any of the other five actions). In the test phase, the authors found that motor activity during the 1 s delay interval selectively increased in anticipation of deterministic actions and not prior to random actions. Given their mean age of 18 months, their learning might be classed as implicit learning.

We see the MNM as an important hub in the neural network supporting social intuition. This network likely includes various social brain areas ( Frith, 2007 ; Adolphs, 2009 ; Yang et al., 2015 ), but, crucially, also areas involved in implicit learning, possibly including the caudate and putamen in the basal ganglia ( Lieberman, 2000 ), and areas representing reward and feelings, such as the ventral striatum (e.g., Sims et al., 2014 ), amygdala (e.g., Stanley et al., 2008 ) and anterior insular cortex (e.g., Lamm and Singer, 2010 ).

5 Concluding remarks

The traditional non-social implicit learning paradigms, as implemented in for example the SRT and AGL tasks, do not allow to draw conclusions about the role of implicit learning processes in social intuition, as they lack a number of vital characteristics associated with social intuition, such as bodily articulations, affective valences and attitudes/dispositions. The premise put forward in this article is that social intuition is largely based on the implicit learning of associations between the other’s bodily articulations and the consequences or outcomes in terms of subsequent actions and events, and associated affective valences. This means that: (i) the underlying structures of the learning processes may be relatively simple, and may not resemble the very complex structures that underpin the traditional tasks. (ii) Learning processes should be agile and relatively rapid, and should not require many hundreds of trials as in the traditional tasks. (iii) Affective valences should be incorporated in the paradigm, as these are a defining characteristic of social intuition ( Cleeremans, 2011 ).

Attempts to ‘socialise’ the traditional AGL task have recently been undertaken (e.g., Jurchis et al., 2023 ; Jurchiș and Dienes, 2023 ), but these paradigms are still rather non-ecologically-valid and do not incorporate affective valences. The studies by Macinska and Jellema, highlighted in this article, are another attempt to address this issue. In this paradigm positive or negative valences are unconsciously associated with a particular identity, on the basis of implicitly learned contingencies between bodily articulations performed by that identity. This approach seems able to discriminate between individuals low and high in autistic traits. Whether an impairment in implicit affective learning in ASC, as suggested by these studies, indeed has consequences for the development of their social intuition skills remains to be confirmed. It should be noted, however, that while the Macinska and Jellema paradigm is more ecologically valid than other paradigms, it is still not really reminiscent of a social interaction . Further, a risk associated with the latter paradigm is that other forms of learning, such as perceptual learning, could possibly interfere with the results.

The cues on which implicit social learning is based consist, to a large extent, of bodily articulations (gestures, actions, facial expressions, eye gaze direction, vocalizations), all of which are specifically represented in the MNM. A major premise of this article is that the MNM is a crucial hub in the neural social intuition network. This, however, poses an interesting problem. According to the extensive literature on the MNM (e.g., Rizzolatti and Craighero, 2004 ), the main input to the MNM consists of the visual description of the other’s action, which causes motor resonation in the MNM, leading to activation of associated areas that represent consequences of the observed action. These associated areas are those areas that would have been activated if the observer themselves would have carried out that action, and are therefore intricately linked to the action representation. Such consequences may consist of the most likely subsequent action/event, visceral states, affective states, sensory feedback and possible reward/punishment. This means that mere observation of the action makes these outcomes directly ‘available’ to the observer, and lead to what Rizzolatti and Sinigaglia (2013 , 2023) called an ‘experiential’ understanding of the action (or understanding ‘from within’).

The interesting problem is that the ‘immediate insight’ we commonly call social intuition pertains to the other individual’s attitude/disposition, not to the observer’s own attitude/disposition; these two may differ profoundly. How then can motor resonation in the MNM bridge this gap? We propose that implicit social learning plays a crucial role. When the observation of the agent performing a particular action in a particular context does not trigger any implicitly learned information, then the observer’s immediate social intuition regarding that agent will indeed be determined by their own repertoire of experiences (i.e., by what they would feel/do if they would carry out that action in that context). If, however, implicitly learned information is available and triggered, then that information will inform and dominate the observer’s social intuition. The implicitly learned information may be linked to a particular individual, as in the paradigm presented in this article (note: identity information, originating in inferior temporal gyrus, is conveyed to the parietal MNM areas), or may be linked to individuals in general. The automatic use of information derived from implicit social learning may lead to accurate social intuition; without it an inaccurate, own-experience-centered, type of social intuition prevails. Possibly, social intuition in ASC individuals is too reliant on the latter pathway.

Implicit social learning also pertains to contextual cues that herald upcoming actions. In these cases, the cues themselves, rather than the observation of an action, trigger motor activity in the MNM. We argue that automatic action anticipation is a crucial contributor to social intuition, enabling the rapid judgments and responses made during fast-paced social interaction. The MNM provides a candidate neural substrate for such anticipatory representations of others’ actions (e.g., Kilner et al., 2007 ; Krol et al., 2020 ). Importantly, anticipatory MNM activity can be triggered by implicitly learned information (e.g., Braukmann et al., 2017 ; Monroy et al., 2019 ). These findings need, however, be backed up by more research. Whether MNM activity in anticipation of others’ actions is reduced in ASC remains to be clarified.

Finally, it should be noted that a limitation of the treatment of social intuition in this article is that it only deals with its automatic nature. For any human social intuition capacity to be meaningful in real-life social situations, higher-level knowledge and explicit, deliberate Theory-of-Mind (ToM) processes are required as well. Moreover, explicit ToM processes might interact with, or modulate, automatic processes ( cf. , Wincenciak et al., 2022 ), such as social intuition. For example, one could speculate that when in our implicit social learning paradigm the participant knows that, even though they can see the agent, the agent cannot see them (because of one-way mirror), they might not implicitly learn to associate the agent with a positive or negative disposition toward them (even though the agent was looking right at them). MNM activation might also be sensitive to explicit ToM reasoning. For example, where observers might show anticipatory MNM activity when an agent’s gaze is directed at a cup of coffee, which implicitly signals they are likely to grasp the cup and drink, they may not show this anticipatory MNM activity if they understood that the agent believed that the cup contained some other non-desired liquid. Such possible interactions between explicit ToM and automatic social intuition remain to be explored in future work.

Data availability statement

The datasets presented in this article are not readily available because they constitute preliminary data. Requests to access the datasets should be directed to [email protected] .

Ethics statement

The studies involving humans were approved by Ethics committee of the Faculty of Health Sciences of the University of Hull. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

TJ: Conceptualization, Writing – original draft. SM: Writing – review & editing. RO’C: Writing – review & editing. TS: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

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.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

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

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Keywords: social intuition, implicit learning, mirror neuron mechanism, affective valences, bodily articulations, autism spectrum conditions, autistic traits, anticipation

Citation: Jellema T, Macinska ST, O’Connor RJ and Skodova T (2024) Social intuition: behavioral and neurobiological considerations. Front. Psychol . 15:1336363. doi: 10.3389/fpsyg.2024.1336363

Received: 10 November 2023; Accepted: 08 April 2024; Published: 23 April 2024.

Reviewed by:

Copyright © 2024 Jellema, Macinska, O’Connor and Skodova. 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: Tjeerd Jellema, [email protected]

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

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• 16 April 2024

US COVID-origins hearing puts scientific journals in the hot seat

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Brad Wenstrup (right), a Republican from Ohio who chairs the Select Subcommittee on the Coronavirus Pandemic, speaks with Raul Ruiz (left), a Democrat from California who is ranking member of the subcommittee. Credit: Al Drago/Bloomberg/Getty

During a public hearing in Washington DC today, Republicans in the US House of Representatives alleged that government scientists unduly influenced the editors of scientific journals and that, in turn, those publications stifled discourse about the origins of the COVID-19 pandemic. Democrats clapped back, lambasting their Republican colleagues for making such accusations without adequate evidence and for sowing distrust of science.

US congressional hearing produces heat but no light on COVID-origins debate

The session is the latest in a series of hearings held by the Select Subcommittee on the Coronavirus Pandemic to explore where the SARS-CoV-2 coronavirus came from, despite a lack of any new scientific evidence. Scientists have for some time been arguing over whether the virus spread naturally, from animals to people, or whether it leaked from a laboratory in Wuhan, China. Some alleged that, in the early days of the pandemic, government scientists Anthony Fauci, former director of the US National Institute of Allergy and Infectious Diseases, and Francis Collins, former director of the US National Institutes of Health (NIH), steered the scientific community, including journals, to dismiss the lab-leak hypothesis.

During the pandemic, “rather than journals being a wealth of information”, they instead “put a chilling effect on scientific research regarding the origins of COVID-19”, Brad Wenstrup, a Republican representative from Ohio who is chair of the subcommittee, said at the hearing. Raul Ruiz, a Democratic representative from California who is the ranking member of the subcommittee, shot back: “Congress should not be meddling in the peer-review process, and it should not be holding hearings to throw around baseless accusations.”

Holden Thorp, editor-in-chief of the Science family of journals in Washington DC, appeared before the committee to deny that he had been coerced or censored by government scientists.

The subcommittee also invited Magdalena Skipper, Nature ’s editor-in-chief, based in London, and Richard Horton, editor-in-chief of the medical journal The Lancet , also based in London, to appear, but neither were present. Skipper was absent owing to scheduling conflicts, but a spokesperson for Springer Nature says that the company is “committed to remaining engaged with the subcommittee and to assisting in its inquiry”. ( Nature ’s news team is editorially independent of its journals team and of its publisher, Springer Nature.) The Lancet did not respond to requests for comment.

Academic influence?

This is not the first time that Republicans have accused members of the scientific community of colluding with Fauci and Collins. Evolutionary biologist Kristian Andersen, at Scripps Research in La Jolla, California, and virologist Robert Garry, at the Tulane University School of Medicine in New Orleans, Louisiana, appeared before the same subcommittee on 11 July last year to deny allegations that the officials prompted them to publish a March 2020 commentary in Nature Medicine 1 concluding that SARS-CoV-2 showed no signs of genetic engineering. They wrote in the journal that they did not “believe that any type of laboratory-based scenario is plausible” for the virus’s origins.

Holden Thorp became editor-in-chief of the Science family of journals in 2019. Credit: Steve Exum

Some lab-leak proponents have suggested, without evidence, that the pandemic began because the NIH funded risky coronavirus research at a lab in Wuhan, offering a motive for Collins and Fauci to promote the natural-origin hypothesis.

During the latest hearing, Republicans went a step further to suggest that not only did Collins and Fauci influence prominent scientists, but that they also encouraged journals to publish research supporting the natural-origin hypothesis. This accusation is based on e-mails that Wenstrup says the subcommittee obtained, showing communication between top journal editors and government scientists. Thorp forcefully denied this line of questioning. “No government officials prompted or participated in the review or editing” of two key papers 2 , 3 on COVID-19’s origins published in Science , he testified. “Any papers supporting the lab-origin theory would go through the very same processes” of peer review as any other paper, he said.

Thorp otherwise spent much of the 80-minute hearing answering questions about how a scientific manuscript is prepared for publication, what a preprint is and how peer review works. In a tense moment, Wenstrup questioned a social-media post on Thorp’s personal X (formerly Twitter) page, in which he downplayed the lab-leak hypothesis. Thorp called the post “flippant” and apologized.

Communication queries

Correspondence between journal editors and government scientists is to be expected, Deborah Ross, a Democratic representative from North Carolina, said at the hearing. “Government actors querying academia on issues that are academic in nature isn’t malpractice or unlawful — it’s just doing their jobs.”

Anita Desikan, an analyst focusing on scientific integrity at the Union of Concerned Scientists who is based in Washington DC, tells Nature’ s news team that it is customary for government agencies to reach out to stakeholders to inform policy decisions. Even if a government scientist suggests an idea for a journal paper, “that doesn’t mean it will be published or receive praise from the scientific community”.

Roger Pielke Jr, a science-policy researcher at the University of Colorado Boulder, who was originally slated to testify before the subcommittee until his invitation was rescinded owing to logistical reasons, disagrees. He thinks that Fauci and Collins still shaped the Nature Medicine COVID-19 origins paper by recommending that specific scientists investigate and by offering advice along the way. Nevertheless, the hearing was a “dud”, Pielke Jr says, because Thorp was the wrong witness. A more relevant witness would have been a government scientific-integrity officer who is more knowledgeable about what constitutes an ethical breach, he adds.

doi: https://doi.org/10.1038/d41586-024-01129-x

Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes E. C. & Garry, R. F. et al. Nature Med. 26 , 450–452 (2020).

Article   PubMed   Google Scholar  

Worobey, M. et al. Science 377 , 951–959 (2022).

Pekar, J. E. et al. Science 377 , 960–966 (2022).

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The Science of Siblings

Gay people often have older brothers. why and does it matter.

Selena Simmons-Duffin

Credit: Lily Padula for NPR

The Science of Siblings is a new series exploring the ways our siblings can influence us, from our money and our mental health all the way down to our very molecules. We'll be sharing these stories over the next several weeks.

This is something I learned years ago through gay bar chatter: Gay people are often the youngest kids in their families. I liked the idea right away — as a gay youngest sibling, it made me feel like there was a statistical order to things and I fit neatly into that order.

When I started to report on the science behind it, I learned it's true: There is a well-documented correlation between having older siblings (older brothers, specifically) and a person's chance of being gay. But parts of the story also struck me as strange and dark. I thought of We the Animals , Justin Torres' haunting semi-autobiographical novel about three brothers — the youngest of whom is queer — growing up in New York state. So I called Torres to get his take on the idea.

Torres' first reaction was to find it considerably less appealing than I did. This makes sense — his latest novel, Blackouts , won a National Book Award last year, and it grapples with the sinister history of how scientists have studied sexuality. "My novel is interested in the pre-Kinsey sexology studies, specifically this one called Sex Variants ," he told me. "It's really informed by eugenics. They were looking for the cause of homosexuality in the body in order to treat it or cure it or get rid of it."

That's why, when he saw my inquiry about a statistical finding that connects sexuality and birth order, he was wary. "To be frank, I find these kinds of studies that're looking for something rooted in the body to explain sexuality to be kind of bunk. I think they rely on a really binary understanding of sexuality itself," he said.

"That's fair," I conceded. But this connection between queerness and older brothers has been found so many times in so many places that one researcher told me it's "a kind of truth" in the science of sexuality.

Rooted in a dark past

The first research on this topic did indeed begin in the 1940s and '50s, during that era of investigations into what causes homosexuality, to be able to cure it. At the time, the queer people whom scientists were studying were living in a world where this facet of their identity was dangerous. Plus, the studies themselves didn't find much, says Jan Kabátek , a senior research fellow at the University of Melbourne.

"Most of it fell flat," he told me. "But there is an exception to this, and that is the finding that men, specifically, who exhibit attraction to the same sex are likely to have more older brothers than other types of siblings."

In the 1990s, this was dubbed the "fraternal birth order effect." In the years since, it has been found again and again, all over the world.

"This pattern has been documented around Canada and the United States, but it goes well beyond that," says Scott Semenyna , a psychology professor at Stetson University. "There's been now many confirmations that this pattern exists in countries like Samoa. It exists in southern Mexico. It exists in places like Turkey and Brazil."

Huge study, consistent findings

An impressive recent study established that this pattern held up in an analysis of a huge sample — over 9 million people from the Netherlands. It confirmed all those earlier studies and added a twist.

"Interestingly enough — and this is quite different from what has been done before — we also showed that the same association manifests for women," explains Kabátek, one of the study's authors. Women who were in same-sex marriages were also more likely to have older brothers than other types of siblings.

At baseline, the chance that someone will be gay is pretty small. "Somewhere around 2 to 3% — we can call it 2% just for the sake of simplicity," Semenyna says. "The fraternal birth order effect shows that you're going to run into about a 33% increase in the probability of, like, male same-sex attraction for every older brother that you have."

The effect is cumulative: The more older brothers someone has, the bigger it is. If you have one older brother, your probability of being gay nudges up to about 2.6%. "And then that probability would increase another 33% if there was a second older brother, to about 3.5%," Semenyna says.

If you have five older brothers, your chance of being gay is about 8% — so, four times the baseline probability.

The author, Selena Simmons-Duffin, at age 3, with her brother, David Simmons-Duffin, at age 5. The Simmons-Duffin family hide caption

The author, Selena Simmons-Duffin, at age 3, with her brother, David Simmons-Duffin, at age 5.

Still, even 8% is pretty small. "The vast majority of people who have a lot of older brothers are still going to come out opposite-sex attracted," Semenyna says. Also, plenty of gay people have no brothers at all, or they're the oldest in their families. Having older brothers is definitely not the only influence on a person's sexuality.

"But just the fact that we are observing effects that are so strong, relatively speaking, implies that there's a good chance that there is, at least partially, some biological mechanism that is driving these associations," Kabátek says.

A hypothesis, but no definitive mechanism

For decades, the leading candidate for that biological mechanism has been the "maternal immune hypothesis," Semenyna explains. "The basic version of this hypothesis is that when a male fetus is developing, the Y chromosome of the male produces proteins that are going to be recognized as foreign by the mother's immune system and it forms somewhat of an immune response to those proteins."

That immune response has some effect on the development of subsequent male fetuses, Semenyna says. The plausibility of this hypothesis was bolstered by a 2017 study that found "that mothers of gay sons have more of these antibodies that target these male-specific proteins than mothers of sons who are not gay or mothers who have no sons whatsoever," he says.

But now that Kabátek's study of the Dutch population has found that this pattern was present among women in same-sex marriages as well, there are new questions about whether this hypothesis is correct.

"One option is that the immune hypothesis works for both men and women," Kabátek says. "Of course, there can be also other explanations. It's for prospective research to make this clearer."

Fun to think about, but concerning too

In a way, I tell Justin Torres, this effect seems simple and fun to me. It's a concrete statistical finding, documented all over the world, and there's an intriguing hypothesis about why it may happen biologically. But darker undercurrents in all of it worry me, like raising a dangerous idea that becoming gay in the womb is the only version of gayness that is real — or a repackaged version of the old idea that mothers are to "blame."

"It is the undercurrents that worry me immensely," he responds. "I remember when I was a kid — I have this memory of watching daytime television. I must have been staying home from school sick in the late '80s or early '90s. The host polled the audience and said, 'If there was a test [during pregnancy] and you could know if your child was gay, would you abort?' I remember being so horrified and disturbed watching all those hands go up in the audience — just feeling so hated. At that young age, I knew this thing about myself, even if I wasn't ready to admit it."

Even if tolerance for queer people in American society has grown a lot since then, he says, "I think that tolerance waxes and wanes, and I worry about that line of thinking."

At the same time, he agrees that the idea of a connection with gay people being the youngest kids in their families is kind of hilarious. "One thing that pops into my mind is, like, maybe if you're just surrounded by a lot of men, you either choose or don't choose men, right?" he laughs.

Essentially, in his view, it's fun to think about, but probably not deeper than that.

"As a humanist, I just don't know why we need to look for explanations for something as complex and joyous and weird as sexuality," Torres says.

Then again, scientists are unlikely to be able to resist that mysterious, weird complexity. Even if the joy and self-expression and community and so many other parts of queerness and sexuality will always be more than statistics can explain.

More from the Science of Siblings series:

• A gunman stole his twin from him. This is what he's learned about grieving a sibling
• In the womb, a brother's hormones can shape a sister's future
• These identical twins both grew up with autism, but took very different paths
• Science of Siblings
• queer community
• homosexuality

• Open access
• Published: 22 April 2024

NSABP FB-10: a phase Ib/II trial evaluating ado-trastuzumab emtansine (T-DM1) with neratinib in women with metastatic HER2-positive breast cancer

• Samuel A. Jacobs 1 ,
• Ying Wang 1 ,
• Jame Abraham 1 , 2 ,
• Huichen Feng 1 ,
• Alberto J. Montero 1 , 2 , 3 ,
• Corey Lipchik 1 ,
• Melanie Finnigan 1 ,
• Rachel C. Jankowitz 1 , 4   nAff5 ,
• Mohamad A. Salkeni 1 , 6   nAff7 ,
• Sai K. Maley 1 ,
• Shannon L. Puhalla 1 , 8 , 9 ,
• Fanny Piette 10 ,
• Katie Quinn 11 ,
• Kyle Chang 11 ,
• Rebecca J. Nagy 11 ,
• Carmen J. Allegra 1 , 12 ,
• Kelly Vehec 1 ,
• Norman Wolmark 1 , 8 ,
• Peter C. Lucas 1 , 8 , 9 , 13 ,
• Ashok Srinivasan 1 , 14 &
• Katherine L. Pogue-Geile 1  

Breast Cancer Research volume  26 , Article number:  69 ( 2024 ) Cite this article

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We previously reported our phase Ib trial, testing the safety, tolerability, and efficacy of T-DM1 + neratinib in HER2-positive metastatic breast cancer patients. Patients with ERBB2 amplification in ctDNA had deeper and more durable responses. This study extends these observations with in-depth analysis of molecular markers and mechanisms of resistance in additional patients.

Forty-nine HER2-positive patients (determined locally) who progressed on-treatment with trastuzumab + pertuzumab were enrolled in this phase Ib/II study. Mutations and HER2 amplifications were assessed in ctDNA before (C1D1) and on-treatment (C2D1) with the Guardant360 assay. Archived tissue (TP0) and study entry biopsies (TP1) were assayed for whole transcriptome, HER2 copy number, and mutations, with Ampli-Seq, and centrally for HER2 with CLIA assays. Patient responses were assessed with RECIST v1.1, and Molecular Response with the Guardant360 Response algorithm.

The ORR in phase II was 7/22 (32%), which included all patients who had at least one dose of study therapy. In phase I, the ORR was 12/19 (63%), which included only patients who were considered evaluable, having received their first scan at 6 weeks. Central confirmation of HER2-positivity was found in 83% (30/36) of the TP0 samples. HER2-amplified ctDNA was found at C1D1 in 48% (20/42) of samples. Patients with ctHER2-amp versus non-amplified HER2 ctDNA determined in C1D1 ctDNA had a longer median progression-free survival (PFS): 480 days versus 60 days ( P  = 0.015). Molecular Response scores were significantly associated with both PFS (HR 0.28, 0.09–0.90, P  = 0.033) and best response ( P  = 0.037). All five of the patients with ctHER2-amp at C1D1 who had undetectable ctDNA after study therapy had an objective response. Patients whose ctHER2-amp decreased on-treatment had better outcomes than patients whose ctHER2-amp remained unchanged. HER2 RNA levels show a correlation to HER2 CLIA IHC status and were significantly higher in patients with clinically documented responses compared to patients with progressive disease ( P  = 0.03).

Conclusions

The following biomarkers were associated with better outcomes for patients treated with T-DM1 + neratinib: (1) ctHER2-amp (C1D1) or in TP1; (2) Molecular Response scores; (3) loss of detectable ctDNA; (4) RNA levels of HER2; and (5) on-treatment loss of detectable ctHER2-amp. HER2 transcriptional and IHC/FISH status identify HER2-low cases (IHC 1+ or IHC 2+ and FISH negative) in these heavily anti-HER2 treated patients. Due to the small number of patients and samples in this study, the associations we have shown are for hypothesis generation only and remain to be validated in future studies.

Clinical Trials registration NCT02236000

Introduction

In 2013, T-DM1 was the first HER2-targeted antibody–drug conjugate (ADC) granted FDA-approval for late-stage metastatic breast cancer after prior trastuzumab. In 2019, T-DM1 was approved as post-neoadjuvant therapy in patients with residual disease based on the KATHERINE trial, demonstrating that post-neoadjuvant T-DM1 was statistically more beneficial than trastuzumab, preventing recurrence of invasive disease or deaths in patients with residual disease in breast or lymph nodes after treatment with trastuzumab ± pertuzumab (hazard ratio for invasive disease or death, 0.05: 95% CI 0.039–0.64; P  < 0.001) [ 1 ]. KATHERINE required archival HER2-positivity but did not mandate HER2 status at study entry. Because multiple studies have confirmed that HER2 status is plastic with conversion of HER2-positive disease to HER2-low (IHC = 0–1+ or IHC 2+ /FISH-negative) under pressure of therapy [ 2 , 3 , 4 ], this raised the question of ADC efficacy in HER2-low patients—either de novo (HR + /HER2-negative) or acquired from conversion of HER2-amplified to HER2-low. There are now several breast cancer-targeting ADCs in the pipeline in addition to the newly approved trastuzumab deruxtecan (T-DXd). Initial approval of T-DXd was for metastatic HER2-positive breast cancer after prior progression on multiple lines of anti-HER2 therapy (DESTINY-Breast01) [ 5 ]. In DESTINY-Breast03, T-DXd improved PFS and OS compared to T-DM1 [ 6 ] in patients with metastatic disease with progression on trastuzumab. In heavily pretreated HER2-low breast cancer patients, T-DXd was evaluated in a single arm phase II study. The objective response rate (ORR) to T-DXd by central review was 37%, with median duration of response of 10.4 months [ 7 ]. DESTINY-Breast04, a randomized, multicenter trial in patients with unresectable or metastatic HER2-low breast cancer, reported highly significant improvements in PFS and OS in patients receiving T-DXd compared to physician choice of treatment [ 8 ]—a particularly striking observation, because neither trastuzumab nor T-DM1 has shown consistent activity in HER2-low populations [ 9 ]. HER2 status (expression, mutation, amplification) is thus emerging as a predictor of clinical efficacy for anti-HER2-therapy. In our NSABP phase Ib trial of HER2-targeted therapies using T-DM1 + neratinib in HER2-positive patients, we showed a discordance in HER2 status between archival tissue and a liquid biopsy obtained at study entry. Loss of ctHER2-amp occurred in 63% (17 of 27) patients. Deeper and more durable responses were observed with T-DM1 + neratinib in patients with ctHER2-amplification [ 10 ]. We now report on an expanded cohort. Our aims were to: (1) confirm activity of T-DM1 + neratinib in patients progressing on a taxane with trastuzumab + pertuzumab (HP), (2) evaluate discordance in HER2 amplification between archived tissue, contemporaneous tissue, and blood, and (3) compare mutation and gene-expression profiles at different time points. Finally, in a subset of patients, we assessed response by RECIST 1.1 with the Guardant Molecular Response score [ 11 , 12 ].

Trial design

FB-10 was a single-arm, nonrandomized, unblinded clinical trial approved by participating institutions’ institutional review boards. Written informed consent was required. FB-10 was conducted according to Good Clinical Practices and the Declaration of Helsinki.

The phase Ib trial was a dose escalation study evaluating T-DM1 + neratinib in women with metastatic HER2-postive breast cancer based on local determination of HER2. Patients received 3.6 mg/kg T-DM1 intravenously on a 3-week cycle and oral neratinib was taken daily in one of four dose cohorts (120, 160, 200 and 240 mg). Twenty-seven patients enrolled, with 5 experiencing a dose-limited toxicity. Three withdrew early for other reasons (Fig.  1 ). Nineteen patients were evaluable for response, which required follow-up imaging after the second cycle of treatment (6 weeks). The recommended phase II dose of neratinib was determined to be 160 mg/d [ 10 ]; however, we did not detect a dose response, i.e., neratinib at 120 mg/d was as effective as higher doses and less toxic. [ 10 ]

Remark Diagram of Blood and Tissue Samples: NSABP FB-10. A Blood samples collected from patients enrolled into FB-10 phase Ib and phase II, and successful assays for ctDNA analysis of HER2 amplification with Guardant360 assays. B Tissue samples collected from patients enrolled into FB-10 and their samples that were profiled for mutations and whole transcriptomic analysis and for ERBB2 amplification status with CLIA and AmpliSeq assays. C The timing and type of sample collections (tissue: TP0 or TP1, or blood: C1D1 or C2D1) are shown

The phase II expansion included all patients (N = 22) who received at least one dose of study therapy in the analysis of safety and efficacy. Eligibility criteria were identical in phase Ib and phase II [ 10 ]. All eligible patients had prior HP and a taxane as neoadjuvant therapy or for de novo metastatic disease, had measurable disease, were ECOG PS ≤ 2, with adequate hematologic, renal, and liver function. Patients with known stable brain metastases were eligible. Brain imaging at entry was not required. Treatment in phase II included T-DM1 at 3.6 mg/kg iv q 3 weeks and neratinib at 160 mg/day. Primary diarrhea prophylaxis was mandated as described in phase I [ 10 ].

Safety assessment

Safety assessment was similar in phase Ib and phase II including physical examination, interim history, and laboratory assessments. Patients remained on treatment until progressive disease or discontinuation because of withdrawal, physician discretion or toxicity. For phase Ib patients, adverse event (AE) assessment occurred on days 1, 8, and 15 of cycle 1; on day 1 of each cycle and for 30 days after therapy discontinuation or when alternate therapy began. Phase II AE assessments were made on day 1 of each cycle.

Response evaluation

In phase Ib, patients were assessed for best response beginning with their first follow-up scan after 2 cycles (6 weeks). Response by RECIST v1.1 was complete response (CR), partial response (PR), stable disease (SD), or progression (PD). In phase II, imaging studies were performed after every 3 cycles (9 weeks). The clinical benefit rate included all CR, PR, and SD patients with duration ≥ 180 days. Patients with stable disease of < 180 days were included with progressive disease patients. A confirmatory scan at least one month after the best response was not required in this study, perhaps accounting for partial responses of short duration in several patients.

Blood and tissue collection

Blood samples were required before treatment at cycle 1, day 1 (C1D1), and after treatment at cycle 2 day 1 (C2D1) for all patients in phase Ib and II (Fig.  1 A). Archived tissue (TP0) of diagnostic blocks or slides were required on all phase Ib and II patients which included 27 patients from phase Ib and 22 patients in phase II. (Fig.  1 B). Contemporaneous biopsy specimens or slides at study entry (TP1) were optional in phase Ib but in phase II, after enrollment of the first 6 patients, the study was amended to require a study entry biopsy. The timing and type of collections of samples are shown in Fig.  1 C.

ctDNA assessment

Samples were analyzed by the Guardant360 assay (Fig.  1 A), which detects single-nucleotide variants, indels, fusions, and copy number alterations in 74 genes. For HER2 amplification, a cutoff of ≥ 2.14 was used. Where amplification could not be determined because of failed assays or no blood, samples were categorized as indeterminant. Guardant Health (Guardant360 assay) is Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists-accredited, New York State Department of Health-approved laboratory.

ctDNA molecular response

Guardant360 Molecular Response is a next generation sequencing (NGS)‐based liquid biopsy that assesses changes in tumor‐derived cell‐free DNA (ctDNA) between baseline and an early on‐treatment timepoint in patients with solid tumor malignancies. It employs an algorithm to identify informative somatic single nucleotide variants (SNVs), insertions/deletions and gene fusions and calculates the percent ctDNA change between the two timepoints based on the mean variant allele frequency (VAF) between two [(mean VAF2/mean VAF1) −1 × 100%]. Using the Molecular Response panel and the Guardant bioinformatics pipeline, the change in ctDNA levels between baseline and the initial follow-up scan (6 weeks in phase I and 9 weeks in phase II) was calculated and the change in VAF determined. Molecular Response is calculated as the ratio of mean VAF on-treatment to baseline with a cutoff of 50%. Decreases in ctDNA of 50%‐100% during this timeframe are associated with clinical benefit in patients on anti‐cancer therapies [ 11 , 13 , 14 ]. Kaplan–Meier curves for PFS are generated for patients above and below a Molecular Response cut off. [ 11 ]

Isolation of nucleic acid

Tumor regions, defined by a certified pathologist, were macrodissected. DNA and RNA were isolated using the Qiagen AllPrep DNA/RNA kit, following the manufacturer’s recommendations but eliminating the xylene wash in the first step. Separate tissue sections were used for RNA and DNA isolation.

Whole transcriptomic profiling

10–30 ng of RNA from the phase II samples was reverse transcribed. cDNA libraries were constructed using whole transcriptomic Ampli-Seq kits, following the manufacturer’s instructions without a fragmentation step due to the small size of the RNAs. This same method did not work well for the phase Ib RNAs. To overcome this problem, phase Ib RNAs were made library-ready via the HTG EdgeSeq system and the HTP panel, which includes probes to interrogate 19,398 genes representing most of the human transcriptome (details in Additional file 1 ).

Breast cancer molecular subtypes were determined by applying the AIMs signature [ 15 ]. The 8-gene trastuzumab-benefit groups were determined using a validated signature. [ 16 , 17 ]

HER2 amplification status and analysis of variants in tissues

DNA sequencing was performed using a custom Ampli-Seq panel referred to as the NAR panel, amplifying 3,847 amplicons with 94.25% coverage of exons from 117 genes in HER2-activated pathways (Additional file 1 : Table S1). The panel was designed using the Thermo Fisher Ion AmpliSeq™ Designer tool ( https://www.ampliseq.com ). Libraries were constructed using 10 ng of DNA using the Ion AmpliSeq™ kit for Chef DL8. The Ion Chef instrument was used to template and load samples on Ion 550 chips. Up to 32 samples were barcoded, pooled, and sequenced on the S5 sequencer (ThermoFisher) following manufacturer’s instructions.

We have used two different criteria to identify variants in FB-10 tissue. For a conservative approach to variant selection, we selected variants with VAF ≥ 10% and for a less restrictive option we selected variants with VAF ≥ 5%. Additional details are included in Additional file 1 and the rationale for these approaches is discussed.

Ion Torrent data and the Ion Reporter software were used to determine HER2 copy number.

HER2 IHC FISH

HER2 status was also assessed in tissue samples with IHC and reflexively for FISH at the discretion of the Director at the CLIA laboratory (Magee Women’s Hospital, University of Pittsburgh Medical Center). Nine samples were equivocal IHC 0 or 1+ due to poor tissue quality prompting examination with FISH. Based on FISH, four were included as HER2-positive.

Statistical analysis

Phase Ib safety, tolerability, efficacy, and recommended phase 2 dose (RP2D) of neratinib in combination with T-DM1 was previously reported [ 10 ]. In the phase II expansion cohort, in which neratinib was administered at the RP2D of 160 mg/d, the intention was to confirm clinical efficacy and tolerability of the combination and to extend the correlative findings. Given the small sample size, the endpoint analyses remain descriptive.

The aim of the single-arm phase II expansion was to rule out the null hypothesis that the ORR was 25% with the alternative hypothesis of an ORR of 45%. With these assumptions, the sample size required was 22 and the decision rules are to declare success if > 8 responses; to declare failure if < 7 responses; and to consider the trial inconclusive if 7 or 8 responses (7/22 = 32%, 8/22 = 36%). At the outset of the study, we did not anticipate the large number of patients with loss of HER2-amplification in blood as determined by the Guardant assay. Thus, the subset analyses based upon HER-amplification detected in blood were performed post-hoc.

Patient characteristics

In the phase Ib portion of this study, 27 patients were enrolled between February 2015 and July 2017. Nineteen patients were evaluable having had at least one follow-up imaging study; three patients withdrew from the study in cycle 1 and 5 patients with a dose-limited toxicity in cycle 1 did not have an imaging assessment. All phase II patients who received at least one dose of study drugs were included in the analysis. Twenty-two patients were evaluable for toxicity and 20 were evaluated for efficacy with at least one scan performed after their third cycle. Two non-evaluable patients who withdrew from the study did not have their first scan but are included as PD. Median age was 55.5 years (range 32–70). Hormone status (ER and/or PR) was positive in 13 patients and negative in 9. All patients were HER2-positive at baseline by local determination (Additional file 1 : Table S3).

Similar to phase Ib patients, diarrhea was the most frequent toxicity in phase II: grade 2, 6 patients (27%); grade 3, 8 (36%). Other grade 3/4 toxicities included: thrombocytopenia, 2 patients (10%); transaminase elevation, 3 patients (15%); and pneumonitis, 1 patient (5%). There were no unanticipated toxicities in the phase II expansion.

Among 19 evaluable patients in phase Ib, there were 3 CRs and 9 PRs for an ORR of 63% (12/19) [ 10 ]. In phase II, including all patients who received at least one dose of therapy, there were 2 CRs, 5 PRs for an ORR of 32% (7/22), and 3 SDs of 180 days or longer making the clinical benefit rate (CBR) 45% (10/22). In phase Ib and II, nine patients had sustained objective responses lasting approximately 1 year or longer (range 343–1453 + days, Additional file 1 : Table S4; Additional file 2 : Table S5). Treatment was discontinued at or before the first scan in 15 patients for a variety of reasons, including 5 DLTs (all in phase I) and 10 with clinical progression in phase I and II.

ctDNA clearance and treatment response

Because clearance of ctDNA has been associated with treatment response, we compared the outcomes of patients who were positive or negative for ctDNA after study treatment. The response rate among the ctDNA-positive patients who were still ctDNA-positive after study therapy was 9/19 (47%), but the ctHER2 DNA-positive patients who became ctDNA-undetectable at C2D1, the response rate was 6/6 (100%), demonstrating that the loss of ctDNA was associated with a very good response.

HER2 amplification in tissues and blood

We assessed the HER2 amplification status of TP0 and TP1 with a CLIA HER2 IHC/FISH assay, and with an Ampli-Seq NGS assay. These tissue samples were also compared to the HER2 amplification status in blood samples collected at C1D1 and C2D1 (Fig.  2 B). There was good concordance between the CLIA HER2/FISH and Ampli-Seq assays (100% in TP1 tissues and 85% in all tissues), which demonstrated the technical accuracy of the Ampli-Seq. Concordance between TP0 tissue with IHC/FISH and C1D1 ctDNA was 71% (20/28). Concordance between C1D1 and C2D1 was 54% (14/26). Anti-HER2 treatment is potentially the causal reason for the discordance between C1D1 and C2D1, which showed a total loss of ctDNA in some samples and a loss of HER2 amplification in others.

Amplification Status of Tissues and Blood: NSABP FB-10. A Response rates (CR/PR, CBR, and SD) for FB-10 HER2-amplified and non-amplified patients based on ctDNA results. B HER2-amplification status of FB-10 tumor tissues based on CLIA tests (IHC/FISH) and Ampli-Seq (Tissue) in baseline (TP0) and study entry (TP1) samples are shown. HER2-amplification status was determined in ctDNA at C1D1 and at C2D1 with the Guardant360 assays. Responses, amplification status, and changes in copy number in ctDNA between C1D1 and C2D1 are indicated as shown in the legend

Using the Guardant360 assay cut point of 2.14 for amplification among 43 C1D1 samples (22 in phase Ib, 21 in phase II), 6 patient samples were indeterminate (including 4 for which somatic mutations were not detected) (Fig.  2 B , dark green), 1 was not evaluable (NE), and 1 other failed quality control. Among the remaining 37 samples, there were 21/37 (57%) patients with amplification and 17/37 (46%) without. The objective response (CR, PR) rate was 55% (11/20) in amplified patients and 41% (7/17) in non-amplified patients. The CBR in patients with ctHER2-amplification was 12/21 (57%) and in non-amplified patients it was 8/17 (47%). There was one patient with SD who was ctHER2-amp indeterminate. Mean duration of response (CBR) was substantially longer in amplified patients, 457 days compared to 131 days in non-amplified patients ( P  = 0.008) (Fig.  2 A; Additional file 1 : Table S4).

We compared progression-free survival (PFS) of patients whose ctDNA or tumor tissues had HER2 amplification to patients with no HER2 amplification. Patients with ctHER2-amp at C1D1 or in their TP1 tumor tissue had a significantly longer PFS than patients with no HER2 amplification ( Fig.  3 A–E ) .

Association of HER2-amplification Status with Patient Outcomes: NSABP FB-10. A Kaplan-Meier plots of patients with or without HER2 amplification in ctDNA or in TP0 tissue ( B & D ), or in TP1 tissue ( C & E ) based on IHC/FISH ( B & C ) and on Ampli-Seq ( D & E )

In phase I and II there were 26 C1D1 and C2D1 pairs, 15 with and 11 without ctHER2-amp at C1D1. Among the 15 with ctHER2-amp at C1D1, 14 showed HER2 loss at C2D1 as defined by a loss ≥ 28% of HER2 copy number or no ctDNA detected. The ORR among these 14 patients was 71% (10/14). Of the 10 responders, 5 cleared ctDNA completely by C2D1, 3 had detectable ctDNA but no ctHER2-amp, and 2 were HER2-positive but the amplification level in C2D1 had decreased dramatically (Fig.  2 B; Additional file 2 : Table S5). The 2 remaining patients with detectable ctHER2-amp with no loss of HER2 amplification had PD, suggesting that a loss of ctDNA and/or a loss of HER2 ctDNA amplification was a marker for a good response to study therapy. However, in 11 patients with no HER2 ctDNA amplification at C1D1, the ORR was 45% (5/11), indicating that some non-amplified tumors were responsive to study treatment.

Molecular response by ctDNA

We assessed the association between molecular response and objective radiologic response (Fig.  4 A). A total of 21 patients (9 phase Ib and 12 phase II) had paired samples that met criteria for assessment of molecular response. Criteria included ≥ 1 alteration present in one of the paired samples plus a mutant molecule count of ≥ 15 in either sample. Molecular responders demonstrated a longer PFS compared to non-responders (median PFS 7.4 vs. 2.8, HR 0.28, 95%CI 0.09-0.90, P=0.033 using Wilcox test). [0.09–0.90, P  = 0.033 using Wilcox test]. We also examined the association between molecular response and best RECIST response. Patients with CR/PR/SD had significantly lower Molecular Response values compared to patients with PD ( P  = 0.037; Fig.  4 B ).

Molecular Response and Patient Outcomes: NSABP FB-10. A Kaplan–Meier curves showing association of MR with PFS using a molecular response cutoff of 50%. B Association between molecular response and best RECIST response

Mutations/variants in tissues and ctDNA

Because ERBB2 is the target of the study therapies, we have examined both tissue and ctDNA for mutations in the ERBB2 gene. No ERBB2 variants in tissue at a VAF of ≥ 10% were observed, however, in ctDNA 3 nonsynonymous, ERBB2 variants (I767M, V777L, and S310Y) were detected in the C1D1 samples from 3 patients. These variants have been associated with sensitivity to neratinib in breast cancer patients [ 18 ]. In this study, the patients whose tumors had a V777L or a S310Y mutation had a PR, but the one patient with a I767M mutation had PD with brain metastasis. The tumor with the I767M mutation also had a P1233L mutation [ 19 ]. Interestingly, in an exhaustive meta-analysis of 37,218 patients, including 11,906 primary tumor samples, 5,541 extracerebral metastasis samples, and with 1485 brain metastasis samples found that a nearby ERBB2 mutation (P1227S) was the only mutation restricted to brain metastasis. It is unknown whether any of these mutations played a role in the patient responses or the course of disease, but it is of interest to note them [ 20 ].

We examined DNA variants in all available TP0 and TP1 tissues using our NAR Ampli-Seq panel, which included ESR1, HER2, and 115 other genes in HER2-activated pathways. Based on our stringent criteria for variant detection, i.e., VAF ≥ 10%, plus other criteria as described in Additional file 1 , we identified 27 variants among 28 samples, representing 21 patients ( Fig.  5 A ) .

Variant Alleles in Patients and their Responses: NSABP FB-10. A Variants detected with a VAF of ≥ 10% in patients with PD, SD, PR or CR. *indicates a stop codon. B Variant alleles with a VAF of ≥ 5% in patients with PD, SD, PR or CR

The frequency of PIK3CA mutations among all sequenced patients was 34% (12/35), similar to that seen in other studies of unselected metastatic and early-stage breast cancer patients (cBioPortal). All of the mutations were in exons 9 and 20 at amino acid 545 and 1,047, respectively. These PIK3CA variants also have the highest VAFs (ranging from 10 to 72% across samples), however, PIK3CA mutations do not appear to influence patient outcomes, because response rates between PIK3CA mutant and WT tumors were similar: 42% (4/12) versus 45% (14/31), respectively. In one case, a PIK3CA mutation was detected only in TP1 but this patient had a PR, again indicating that PIK3CA is not a resistance marker for study therapy. Variants detected only in PD or CR patients represent potential resistance or sensitivity markers, respectively, to study therapy. Mutations detected only in TP1 samples among the 12 paired TP0/TP1 cases, included ADAM17_S770L, ERBB4_E1010K, ERBB4_R1040T, and IL6ST_S834* in one sample and an ESR1_EY537S mutation in another (Fig.  5 A). Both patients had PD, perhaps indicating that these mutations may have emerged in response to prior therapies. Details of variants are presented in Additional file 1 .

We examined the PAM50 subtypes and the 8-gene trastuzumab benefit signature in all available tissues [ 16 ]. Among the 29 patients with response and gene expression data for TP0 tissue, we found that 19/34 (56%) were HER2E, 8 (24%) were luminal B, 4 (12%) were basal, 2 (5.9%) were normal, and 1 (2.9%) was luminal A. Patients with luminal subtype tumors had a lower CR/PR response rate (1/8 [12.5%]) than patients with a non-luminal subtype (12/23 [52%]) (Additional file 2 : Table S5). Among the TP1 samples with gene expression data, the frequency of CR/PR was 1/4 in luminal patients and 5/9 in the non-luminal patients. Intrinsic subtypes differed between TP0 and TP1 tissues in some cases (Additional file 1 : Table S4). Although numbers are small, the frequency of CR/PR rates were consistently lower among the luminal patients than non-luminal patients.

The 8-gene trastuzumab signature is a validated signature for identifying patients with large-, moderate- or no-benefit from trastuzumab when added to chemotherapy in the adjuvant setting [ 16 , 17 ] and has been shown to associate with pCR rates in the neoadjuvant setting [ 21 , 22 ]. We questioned whether this signature may also show an association with response in the metastatic setting. Among the large-, moderate- and no- benefit groups the percent of CR/PR patients was 67% (4/6), 50% (9/18), and 29% (2/7), respectively (data in Additional file 2 : Table S5).

As expected, the level of HER2 RNA increased as the IHC status increased (i.e., 0, 1 + , 2 + , to 3 +) (Additional file 1 : Fig. S2). In TP1 samples they were concordant with patient responses suggesting that HER2 RNA expression in study entry is associated with response to T-DM1 + neratinib ( Fig.  6 ).

RNA Expression Levels and Response to Therapy. RNA expression levels in TP0 tissues ( A ) and in TP1 tissues ( B ) from patients with PD, SD or CR/PR. The units for RNA expression were log 2 expression values

Significant differences were detected in RNA levels between IHC 1 + and 3 + and between 2 + and 3 + but not between 0 and 1 + nor between 1 + and 2 + (Additional file 1 : Fig. S3). Although numbers are limited, these data show that the RNA levels are not different between 0 and 1 + . These patients may benefit from treatment with other ADCs. The DAISY and DESTINY-Breast04 trials signal that T-DXd may have significant activity in HER2-low patients [ 8 , 23 ].

Approximately 35% of HER2-positive patients may have a loss of HER2 amplification after undergoing chemotherapy + anti-HER2 therapy [ 2 , 3 ]. In a retrospective analysis of 525 patients who received neoadjuvant chemotherapy (NAC) + HP, 141 patients with residual disease had HER2 status determined pre-and post-NAC-HP. HER2 was concordant (positive/positive) in 84/141 (60%). HER2 protein expression was lost (IHC 0) in 13/57 (23%) and designated as HER2-low in 44/57 (77%) including IHC 1 + in 31 and IHC 2 + /FISH non-amplified in 13 [ 4 ]. HER2 intratumoral heterogeneity is likely one cause of discordant HER2 status between primary and post-treatment residual or metastatic disease [ 24 ]. Other possibilities include decreased HER2 expression, which could be a transient change or a result of the selection of HER2-low subclones [ 4 ].

We have assessed HER2 status before and after pre- and post-study therapy in not only solid tissue but also blood. We have determined the HER2 status in tissues with CLIA IHC/FISH assays, which is the gold standard for HER2 assessment, plus with Ampli-Seq, because it provided a quantitative analysis of HER2 copy number with a greater dynamic range. Ampli-Seq was able to detect a decrease in HER2 copy number in samples that had not lost HER2 amplification based on IHC/FISH. We have also monitored HER2 status in liquid biopsies, which has several advantages over genomic analysis of tissues. Blood has exposure to all potential metastatic sites allowing for the detection of different variants from different metastatic sites. Thus, blood may be more representative of the metastatic tumor than examination of a single biopsied lesion, and may reflect tumor evolution and intratumoral heterogeneity [ 25 ]. Blood samples are more easily collected, making multiple serial collections possible. Collecting multiple serial tissue samples is impractical, costly, and represents a much greater risk to patients than does serial collection of blood. The assessment of ctDNA is a powerful tool, showing very promising results to monitor tumor recurrence and response to therapy, but it does not yet replace the current gold standard, IHC/FISH, for the assessment of HER2 status in solid tumors. However, the monitoring of the HER2 status in ctDNA does provide an indicator of tumor response to therapy.

In our phase Ib/II study, HER2 tissue was amplified in the baseline samples (TP0) (pre- anti-HER2 therapy) in all patients by local determination, however, in liquid biopsies at C1D1 after chemotherapy + HP, HER2-amplification was detected in only 20/42 (48%) of patients. Patients with ctHER2-amp versus non-amplified HER2 ctDNA determined in C1D1 ctDNA had a longer median PFS, 480 days versus 60 days ( P  = 0.015). It is expected that patients with HER2 amplification would respond to study therapy (chemotherapy + HP).

Loss of HER2 amplification observed after one cycle of study therapy may indicate that responders are either clearing ctDNA completely or that the amplification falls below the limit of detection. In the 5 cases who were ctHER2 DNA amplified, and completely cleared ctDNA, the response rate was 100%.

We applied a Molecular Response VAF ratio calculation to measure the change in ctDNA from baseline to C2D1, with the hypothesis that an early decrease in ctDNA levels would predict response to T-DM1 + neratinib therapy, as measured by PFS and RECIST response. Indeed, Molecular Response was associated with both PFS and best response to therapy. This should be confirmed in larger dataset, however, our findings are in line with other studies demonstrating the ability of ctDNA to predict short- and long-term efficacy. Early data from the PADA-1 trial suggests that changing therapy based on alterations detected in ctDNA, prior to evidence of progression via imaging, may provide clinical benefit. In that trial, patients with ER + /HER2-negative metastatic breast cancer being treated in the first line setting with an aromatase inhibitor (AI) + palbociclib were monitored for hotspot ESR1 alterations via ctDNA using digital droplet PCR (ddPCR). Patients with rising ESR1 VAF on therapy, but no synchronous evidence of disease progression via RECIST 1.1, were randomized to either continue receiving an AI + palbociclib or switched to fulvestrant + palbociclib. PADA-1 met its primary efficacy objective, with patients randomized to receive fulvestrant + palbociclib having a significantly longer PFS compared to those who stayed on an AI + palbociclib (median PFS 11.9 months [95% CI 9.1–13.6 months] versus 5.7 months [95% CI 3.9–7.5 months]; stratified HR 0.61 [95% CI 0.43–0.86], two-sided P  = 0.004) [ 26 ]. More data on mutational evolution is needed to determine whether similar strategies employing ctDNA to inform change in therapy will be broadly applicable across breast cancer subtypes and therapy classes in order to further prolong OS.

Although a cross comparison of studies can be problematic, phase II and III studies suggest that as patients are more heavily treated with anti-HER2 regimens, the PFS and ORR decrease with each subsequent anti-HER2 therapy [ 27 , 28 , 29 , 30 ]. However, in a phase III randomized trial of trastuzumab deruxtecan (T-DXd) versus trastuzumab emtansine (T-DM1) in patients (N = 524) whose disease progressed on anti-HER2 therapy, the reported ORR for patients treated with T-DXd or T-DM1 were 79.7% versus 34.2%, respectively. The landmark analysis of PFS at 12 months was 75.8% with T-DXd as compared to 34.1% with T-DM1 (HR 0.28, 95% CI 0.22–0.37; P  < 0.001) [ 6 ]. Although both T-DXd and T-DM1 have a trastuzumab backbone, there are substantial differences in the linker-payload chemistry, which favors an increased intracellular payload and a bystander effect with T-DXd [ 31 , 32 ].

We have shown in our study that the benefit from T-DM1 + neratinib is limited in HER2-non-amplified tumors. This finding is consistent with a study reporting a PFS with T-DM1 of 1.5 months [ 33 ] in patients discordant for HER2 in primary and metastatic tissue (HER2-positive/negative). Although loss of HER2-amplification appears to be one mechanism of resistance to T-DM1, half of the patients with HER2-amplified tumors did not respond to T-DM1 + neratinib, indicating that resistance to T-DM1 is not limited to loss of HER2-amplification. Many other mechanisms of resistance to T-DM1 have been proposed, such as altered cellular uptake, intracellular transport, and metabolism of the payload. [ 32 ]

Based on the low level of activity of T-DM1 monotherapy in patients failing HP, we speculate that the combination of T-DM1 and neratinib is more effective than T-DM1 monotherapy. We are unaware of any trials in HER2-positive breast cancer in which patients progressing on HP have been randomized to compare the response to T-DM1 as monotherapy with a combination of T-DM1 and an irreversible tyrosine kinase inhibitor (TKI). However, in preclinical lung models with ERBB2 mutation and/or amplification, the combination of T-DM1 + neratinib did show increased efficacy over monotherapy. Anecdotally, enhanced efficacy was demonstrated in a breast cancer patient progressing on monotherapy with T-DM1 who then responded with addition of neratinib. The mechanism of action of the antibody–drug conjugates (ADC) such as T-DM1 and T-DXd involves the recognition and binding of the trastuzumab backbone to the extracellular HER2 surface receptor. The ADC-protein complex is internalized with cleavage of the cytotoxic payload. Irreversible TKIs such as neratinib and afatinib (unlike reversible TKIs such as lapatinib) have been shown to enhance HER2 internalization and lysosomal sorting, which has the potential to increase uptake of bound ADC and release of their cytotoxic payload [ 34 ].

The KATHERINE [ 6 ] study established T-DM1 as a standard of care in early HER2-positive breast cancer patients with residual disease after neoadjuvant therapy [ 1 ]. DESTINY-Breast03 has clearly shown superiority of T-DXd over T-DM1 in HER2-positive metastatic disease. The currently recruiting DESTINY-Breast05 study will compare T-DXd with T-DM1 in high-risk HER2-positive patients with residual disease following NAC-HP (NCT04622319). Another study, DESTINY-Breast06 (NCT04494425), will address the question of HER2-low (IHC 1 + or IHC 2 + /FISH-negative or HER2 IHC > 0, < 1 +) in patients with metastatic hormone-positive disease with progression on at least two lines of endocrine therapy comparing T-DXd with investigator’s choice of chemotherapy [ 31 ]. The 30-40% of HER2-positive patients with residual or metastatic disease after neoadjuvant therapy who have lost HER2 amplification, while not directly being addressed with these ADCs studies, warrant further investigation with newer generation ADCs.

Our study has several limitations including the non-randomized design, the logistic difficulties in obtaining samples of blood and tissue on all patients and the small sample size, which limited its power and the ability to perform multivariant analysis. However, the strengths of our study include the multiple temporal sample collections, multiple assessments of HER2 status, and molecular assessment of DNA in both tissue and blood.

Despite the limitations of our study, the findings have generated several hypotheses that should be further investigated. First, retrospective confirmation in a large phase III study that loss of HER2-expression under the pressure of therapy can be detected with liquid biopsy; second, the overall response, depth, and duration of response to anti-HER2 therapy is greater in patients with HER2-amplified than in non-amplified patients; third, the activity of T-DM1 or other ADCs with a trastuzumab-backbone may be enhanced with addition of an irreversible TKI such as neratinib. This hypothesis, testing the interaction of an ADC with a TKI, reversible and irreversible, could be evaluated in patient-derived xenografts or other model systems and should be validated prior to a randomized trial. Finally, a small fraction of HER2-nonamplified patients did benefit from T-DM1 + neratinib. Possible explanations, which require further investigation, include a false negative assay, enhanced internalization of T-DM1 in presence of neratinib, or EGFR becoming the driver in patients with loss of HER2-amplification, and inhibition by neratinib [ 32 , 33 , 34 ]. We also realize that a low ctDNA fraction could have prevented the detection of ctHER2 amplification.

Gene expression analysis revealed several important observations. First, the level of HER2 RNA expression in TP1 tissues was closely correlated with the response rate to study therapy. Second, non-luminal subtypes had a better response rate than luminal tumors, although this difference was not statistically significant. Third, there was a non-significant association of the 8-gene trastuzumab benefit groups with the rate of responses to study therapy. Fourth, changes in intrinsic subtypes were seen between TP0 and TP1 tissue samples, indicating that these changes may be a result of tumors evolving to become resistant to HP. These results highlight the importance of collecting and monitoring molecular changes in tissue samples as patients move through their treatments.

PIK3CA mutations are a known oncogenic driver in breast cancer and drive therapeutic resistance in multiple HER2-targeted therapies [ 35 ]. In the EMILIA trial, patients with PIK3CA mutations, treated with capecitabine + lapatinib were associated with a shorter PFS than were patients with wild-type tumors; however, in patients treated with T-DM1 this was not the case [ 36 ]. We likewise see that in patients treated with T-DM1 + neratinib, PIK3CA mutations were not associated with outcomes. However, we cannot rule out the possibility that a subset of patients, refractory to T-DM1 + neratinib with PIK3CA mutations, may be responsive to PIK3CA inhibitors.

We demonstrate the usefulness of serial assessment of HER2 status in blood and tissue in patients with an initial diagnosis of HER2-positive disease. Loss of HER2 amplification in ctDNA, or the complete loss of ctDNA on treatment with T-DM1 + neratinib, was associated with clinical benefit. Further, we show that many of the patients with short-lived PR or PD were HER2-low in tissue. These patients may be better treated with the recently approved ADC, trastuzumab deruxtecan. We observed that the ADC, T-DM1, plus neratinib, was well tolerated. The combination with an irreversible tyrosine kinase inhibitor with other ADCs warrants investigation.

Availability of data and materials

Anonymized individual participant data that underlie the results reported in this article will be available in dbGAP or other publicly available site after publication.

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Acknowledgments

We would also like to thank Wendy L. Rea, BA, for editing the manuscript.

We would like to thank our funders BCRF (CONS-20-009), Guardant Health Inc., Puma Biotechnology, Inc., and the NSABP Foundation. The NSABP Foundation received funding from Puma Biotechnology to conduct the clinical trial and for the collection of tissues and blood samples associated with this clinical trial. No authors received any direct funding for this research, but indirectly received salary support for efforts to conduct this research. The funder played no role in the design of the study, or collection, analysis, or interpretation of the data, or in the writing of the manuscript or submission thereof.

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Rachel C. Jankowitz

Present address: University of Pennsylvania Perelman School of Medicine, State College, PA, USA

Mohamad A. Salkeni

Present address: Virginia Cancer Specialists, Fairfax, VA, USA

Authors and Affiliations

NSABP Foundation, Pittsburgh, PA, USA

Samuel A. Jacobs, Ying Wang, Jame Abraham, Huichen Feng, Alberto J. Montero, Corey Lipchik, Melanie Finnigan, Rachel C. Jankowitz, Mohamad A. Salkeni, Sai K. Maley, Shannon L. Puhalla, Carmen J. Allegra, Kelly Vehec, Norman Wolmark, Peter C. Lucas, Ashok Srinivasan & Katherine L. Pogue-Geile

Cleveland Clinic, Weston/Taussig Cancer Institute, Cleveland, OH, USA

Jame Abraham & Alberto J. Montero

University Hospitals/Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA

Alberto J. Montero

University of Pittsburgh, Pittsburgh, PA, USA

National Institutes of Health, Washington, DC, USA

UPMC Hillman Cancer Center, Pittsburgh, PA, USA

Shannon L. Puhalla, Norman Wolmark & Peter C. Lucas

University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

Shannon L. Puhalla & Peter C. Lucas

International Drug Development Institute, Louvain-la-Neuve, Belgium

Fanny Piette

Guardant Health, Redwood City, CA, USA

Katie Quinn, Kyle Chang & Rebecca J. Nagy

University of Florida Health, Gainesville, FL, USA

Carmen J. Allegra

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Peter C. Lucas

Autism Impact Fund, Pittsburgh, PA, USA

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Conception &/or Design: SAJ, YW, JA, HF, CL, KPG. Acquisition (of pts/materials) &/or Analysis: All authors: SAJ, YW, JA, HF, AJM, CL, MF, RCJ, AMS, SKM, SLP, FP, KQ, KC, RJN, CJA, KV, NW, PCL, AS, KP-G. Interpretation of the data: SAJ, YW, HF, FP, KQ, AS, KP-G. Has drafted the work or substantively revised it: SAJ, YW, AS, KP-G.

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AJ Montero: Honoraria: Celgene, AstraZeneca, OncoSec; Consulting/Advisory Role: New Century Health, Welwaze, Paragon healthcare; Research Funding: F. Hoffmann-La Roche Ltd, Basel, Switzerland; Uncompensated: Roche; Open Payments: https://openpaymentsdata.cms.gov/physician/618396 . K Quinn: Guardant Health Shareholder. K Chang: Guardant Health Shareholder. RJ Nagy: Guardant Health Shareholder. PC Lucas: Equity interest in AMGEN outside the submitted work. KL Pogue-Geile: Consulting for Bluestar BioAdvisors and Provisional Patents filed both outside the submitted work. All other authors declare no other potential conflicts of interest.

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Additional file 1.

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Jacobs, S.A., Wang, Y., Abraham, J. et al. NSABP FB-10: a phase Ib/II trial evaluating ado-trastuzumab emtansine (T-DM1) with neratinib in women with metastatic HER2-positive breast cancer. Breast Cancer Res 26 , 69 (2024). https://doi.org/10.1186/s13058-024-01823-8

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Hypothesis testing, p values, confidence intervals, and significance.

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• Definition/Introduction

Medical providers often rely on evidence-based medicine to guide decision-making in practice. Often a research hypothesis is tested with results provided, typically with p values, confidence intervals, or both. Additionally, statistical or research significance is estimated or determined by the investigators. Unfortunately, healthcare providers may have different comfort levels in interpreting these findings, which may affect the adequate application of the data.

• Issues of Concern

Without a foundational understanding of hypothesis testing, p values, confidence intervals, and the difference between statistical and clinical significance, it may affect healthcare providers' ability to make clinical decisions without relying purely on the research investigators deemed level of significance. Therefore, an overview of these concepts is provided to allow medical professionals to use their expertise to determine if results are reported sufficiently and if the study outcomes are clinically appropriate to be applied in healthcare practice.

Hypothesis Testing

Investigators conducting studies need research questions and hypotheses to guide analyses. Starting with broad research questions (RQs), investigators then identify a gap in current clinical practice or research. Any research problem or statement is grounded in a better understanding of relationships between two or more variables. For this article, we will use the following research question example:

Research Question: Is Drug 23 an effective treatment for Disease A?

Research questions do not directly imply specific guesses or predictions; we must formulate research hypotheses. A hypothesis is a predetermined declaration regarding the research question in which the investigator(s) makes a precise, educated guess about a study outcome. This is sometimes called the alternative hypothesis and ultimately allows the researcher to take a stance based on experience or insight from medical literature. An example of a hypothesis is below.

Research Hypothesis: Drug 23 will significantly reduce symptoms associated with Disease A compared to Drug 22.

The null hypothesis states that there is no statistical difference between groups based on the stated research hypothesis.

Researchers should be aware of journal recommendations when considering how to report p values, and manuscripts should remain internally consistent.

Regarding p values, as the number of individuals enrolled in a study (the sample size) increases, the likelihood of finding a statistically significant effect increases. With very large sample sizes, the p-value can be very low significant differences in the reduction of symptoms for Disease A between Drug 23 and Drug 22. The null hypothesis is deemed true until a study presents significant data to support rejecting the null hypothesis. Based on the results, the investigators will either reject the null hypothesis (if they found significant differences or associations) or fail to reject the null hypothesis (they could not provide proof that there were significant differences or associations).

To test a hypothesis, researchers obtain data on a representative sample to determine whether to reject or fail to reject a null hypothesis. In most research studies, it is not feasible to obtain data for an entire population. Using a sampling procedure allows for statistical inference, though this involves a certain possibility of error. [1]  When determining whether to reject or fail to reject the null hypothesis, mistakes can be made: Type I and Type II errors. Though it is impossible to ensure that these errors have not occurred, researchers should limit the possibilities of these faults. [2]

Significance

Significance is a term to describe the substantive importance of medical research. Statistical significance is the likelihood of results due to chance. [3]  Healthcare providers should always delineate statistical significance from clinical significance, a common error when reviewing biomedical research. [4]  When conceptualizing findings reported as either significant or not significant, healthcare providers should not simply accept researchers' results or conclusions without considering the clinical significance. Healthcare professionals should consider the clinical importance of findings and understand both p values and confidence intervals so they do not have to rely on the researchers to determine the level of significance. [5]  One criterion often used to determine statistical significance is the utilization of p values.

P values are used in research to determine whether the sample estimate is significantly different from a hypothesized value. The p-value is the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect. Conventionally, data yielding a p<0.05 or p<0.01 is considered statistically significant. While some have debated that the 0.05 level should be lowered, it is still universally practiced. [6]  Hypothesis testing allows us to determine the size of the effect.

An example of findings reported with p values are below:

Statement: Drug 23 reduced patients' symptoms compared to Drug 22. Patients who received Drug 23 (n=100) were 2.1 times less likely than patients who received Drug 22 (n = 100) to experience symptoms of Disease A, p<0.05.

Statement:Individuals who were prescribed Drug 23 experienced fewer symptoms (M = 1.3, SD = 0.7) compared to individuals who were prescribed Drug 22 (M = 5.3, SD = 1.9). This finding was statistically significant, p= 0.02.

For either statement, if the threshold had been set at 0.05, the null hypothesis (that there was no relationship) should be rejected, and we should conclude significant differences. Noticeably, as can be seen in the two statements above, some researchers will report findings with < or > and others will provide an exact p-value (0.000001) but never zero [6] . When examining research, readers should understand how p values are reported. The best practice is to report all p values for all variables within a study design, rather than only providing p values for variables with significant findings. [7]  The inclusion of all p values provides evidence for study validity and limits suspicion for selective reporting/data mining.  

While researchers have historically used p values, experts who find p values problematic encourage the use of confidence intervals. [8] . P-values alone do not allow us to understand the size or the extent of the differences or associations. [3]  In March 2016, the American Statistical Association (ASA) released a statement on p values, noting that scientific decision-making and conclusions should not be based on a fixed p-value threshold (e.g., 0.05). They recommend focusing on the significance of results in the context of study design, quality of measurements, and validity of data. Ultimately, the ASA statement noted that in isolation, a p-value does not provide strong evidence. [9]

When conceptualizing clinical work, healthcare professionals should consider p values with a concurrent appraisal study design validity. For example, a p-value from a double-blinded randomized clinical trial (designed to minimize bias) should be weighted higher than one from a retrospective observational study [7] . The p-value debate has smoldered since the 1950s [10] , and replacement with confidence intervals has been suggested since the 1980s. [11]

Confidence Intervals

A confidence interval provides a range of values within given confidence (e.g., 95%), including the accurate value of the statistical constraint within a targeted population. [12]  Most research uses a 95% CI, but investigators can set any level (e.g., 90% CI, 99% CI). [13]  A CI provides a range with the lower bound and upper bound limits of a difference or association that would be plausible for a population. [14]  Therefore, a CI of 95% indicates that if a study were to be carried out 100 times, the range would contain the true value in 95, [15]  confidence intervals provide more evidence regarding the precision of an estimate compared to p-values. [6]

In consideration of the similar research example provided above, one could make the following statement with 95% CI:

Statement: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22; there was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

It is important to note that the width of the CI is affected by the standard error and the sample size; reducing a study sample number will result in less precision of the CI (increase the width). [14]  A larger width indicates a smaller sample size or a larger variability. [16]  A researcher would want to increase the precision of the CI. For example, a 95% CI of 1.43 – 1.47 is much more precise than the one provided in the example above. In research and clinical practice, CIs provide valuable information on whether the interval includes or excludes any clinically significant values. [14]

Null values are sometimes used for differences with CI (zero for differential comparisons and 1 for ratios). However, CIs provide more information than that. [15]  Consider this example: A hospital implements a new protocol that reduced wait time for patients in the emergency department by an average of 25 minutes (95% CI: -2.5 – 41 minutes). Because the range crosses zero, implementing this protocol in different populations could result in longer wait times; however, the range is much higher on the positive side. Thus, while the p-value used to detect statistical significance for this may result in "not significant" findings, individuals should examine this range, consider the study design, and weigh whether or not it is still worth piloting in their workplace.

Similarly to p-values, 95% CIs cannot control for researchers' errors (e.g., study bias or improper data analysis). [14]  In consideration of whether to report p-values or CIs, researchers should examine journal preferences. When in doubt, reporting both may be beneficial. [13]  An example is below:

Reporting both: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22, p = 0.009. There was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

• Clinical Significance

Recall that clinical significance and statistical significance are two different concepts. Healthcare providers should remember that a study with statistically significant differences and large sample size may be of no interest to clinicians, whereas a study with smaller sample size and statistically non-significant results could impact clinical practice. [14]  Additionally, as previously mentioned, a non-significant finding may reflect the study design itself rather than relationships between variables.

Healthcare providers using evidence-based medicine to inform practice should use clinical judgment to determine the practical importance of studies through careful evaluation of the design, sample size, power, likelihood of type I and type II errors, data analysis, and reporting of statistical findings (p values, 95% CI or both). [4]  Interestingly, some experts have called for "statistically significant" or "not significant" to be excluded from work as statistical significance never has and will never be equivalent to clinical significance. [17]

The decision on what is clinically significant can be challenging, depending on the providers' experience and especially the severity of the disease. Providers should use their knowledge and experiences to determine the meaningfulness of study results and make inferences based not only on significant or insignificant results by researchers but through their understanding of study limitations and practical implications.

• Nursing, Allied Health, and Interprofessional Team Interventions

All physicians, nurses, pharmacists, and other healthcare professionals should strive to understand the concepts in this chapter. These individuals should maintain the ability to review and incorporate new literature for evidence-based and safe care. 

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Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Hypothesis Testing, P Values, Confidence Intervals, and Significance. [Updated 2023 Mar 13]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Title: detecting compromised iot devices using autoencoders with sequential hypothesis testing.

Abstract: IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, rendering them unusable in detecting compromised IoT devices. In this paper we develop an effective and efficient framework, named CUMAD, to detect compromised IoT devices. Instead of directly relying on individual anomalous events, CUMAD aims to accumulate sufficient evidence in detecting compromised IoT devices, by integrating an autoencoder-based anomaly detection subsystem with a sequential probability ratio test (SPRT)-based sequential hypothesis testing subsystem. CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly. Our evaluation studies based on the public-domain N-BaIoT dataset show that CUMAD can on average reduce the false positive rate from about 3.57% using only the autoencoder-based anomaly detection scheme to about 0.5%; in addition, CUMAD can detect compromised IoT devices quickly, with less than 5 observations on average.

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