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• A Research Guide
• Research Paper Topics
• 35 Human Behavior Research Topics & Questions

35 Human Behavior Research Topics & Questions

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• When human behaviour became human?
• What traits we consider typically human we can meet in animals?
• Nature versus nurture. To what extentthe natural behaviour can be corrected?
• The phenomenon of “Mowgli kids” and their behavior
• The stages of human development and their impact on behaviour patterns
• The impact of the family or parental substitutes on behaviour
• Mating rituals or chivalrous romance? How do people court their love interests?
• Habits and their development
• How advertising uses our typical behaviour patterns?
• The importance of happiness
• Games and behaviour. Why do we like to play so much?
• Cults and sects. How do people get involved?
• The psychology of the crowd. What happens to person inside the crowd?
• Does natural morality exist or is it a social construct?
• Sex, gender and behaviour
• Is it good or bad?
• The typical responses to danger: run, fight, hide. Are they hardwired into us?
• Nonverbal communication: is it international?
• Depression and its impact on human behaviour
• Do LGBTQ+ people have typical behavioural patterns?
• The impact of social media and Internet on behaviour
• Porn and sexual attractions
• What is bipolar disorder in terms of behaviour?
• Social hierarchy and behaviour
• Are behavioural patterns connected to self-esteem?
• Elderly people and changes in their behaviour
• Drugs that change behaviour
• IQ and EQ and their impact on behaviour
• Religion and behavioural norms
• Culture clash and behaviour of people of mixed origins
• Correcting dysfunctional behaviour
• Propaganda and behaviour
• Artificially created social groups and their behaviour
• Trauma, PTSD and behaviour
• Defensive behaviour

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• Table Of Contents

human behaviour , the potential and expressed capacity for physical, mental, and social activity during the phases of human life.

Humans, like other animal species, have a typical life course that consists of successive phases of growth, each of which is characterized by a distinct set of physical, physiological, and behavioral features. These phases are prenatal life, infancy , childhood , adolescence , and adulthood (including old age). Human development , or developmental psychology , is a field of study that attempts to describe and explain the changes in human cognitive , emotional, and behavioral capabilities and functioning over the entire life span , from the fetus to old age.

Most scientific research on human development has concentrated on the period from birth through early adolescence, owing to both the rapidity and magnitude of the psychological changes observed during those phases and to the fact that they culminate in the optimum mental functioning of early adulthood. A primary motivation of many investigators in the field has been to determine how the culminating mental abilities of adulthood were reached during the preceding phases. This essay will concentrate, therefore, on human development during the first 12 years of life.

This article discusses the development of human behaviour. For treatment of biological development, see human development . For further treatment of particular facets of behavioral development, see emotion ; learning theory ; motivation ; perception ; personality ; and sexual behaviour, human . Various disorders with significant behavioral manifestations are discussed in mental disorder .

Theories of development

The systematic study of children is less than 200 years old, and the vast majority of its research has been published since the mid-1940s. Basic philosophical differences over the fundamental nature of children and their growth occupied psychologists during much of the 20th century. The most important of such controversies concerned the relative importance of genetic endowment and environment , or “nature” and “nurture,” in determining development during infancy and childhood. Most researchers came to recognize, however, that it is the interaction of inborn biological factors with external factors, rather than the mutually exclusive action or predominance of one or the other force, that guides and influences human development . The advances in cognition , emotion , and behaviour that normally occur at certain points in the life span require both maturation (i.e., genetically driven biological changes in the central nervous system ) and events, experiences, and influences in the physical and social environment. Generally, maturation by itself cannot cause a psychological function to emerge; it does, however, permit such a function to occur and sets limits on its earliest time of appearance.

Three prominent theories of human development emerged in the 20th century, each addressing different aspects of psychological growth. In retrospect, these and other theories seem to have been neither logically rigorous nor able to account for both intellectual and emotional growth within the same framework. Research in the field has thus tended to be descriptive, since developmental psychology lacks a tight net of interlocking theoretical propositions that reliably permit satisfying explanations.

Early psychoanalytic theories of human behaviour were set forth most notably by Austrian neurologist Sigmund Freud . Freud’s ideas were influenced by Charles Darwin ’s theory of evolution and by the physical concept of energy as applied to the central nervous system . Freud’s most basic hypothesis was that each child is born with a source of basic psychological energy called libido . Further, each child’s libido becomes successively focused on various parts of the body (in addition to people and objects) in the course of his or her emotional development . During the first postnatal year, libido is initially focused on the mouth and its activities; nursing enables the infant to derive gratification through a pleasurable reduction of tension in the oral region. Freud called this the oral stage of development. During the second year, the source of excitation is said to shift to the anal area, and the start of toilet training leads the child to invest libido in the anal functions. Freud called this period of development the anal stage . During the period from three through six years, the child’s attention is attracted to sensations from the genitals, and Freud called this stage the phallic stage . The half dozen years before puberty are called the latency stage . During the final and so-called genital stage of development, mature gratification is sought in a heterosexual love relationship with another. Freud believed that adult emotional problems result from either deprivation or excessive gratification during the oral, anal, or phallic stages. A child with libido fixated at one of these stages would in adulthood show specific neurotic symptoms, such as anxiety .

(Read Sigmund Freud’s 1926 Britannica essay on psychoanalysis.)

Freud devised an influential theory of personality structure. According to him, a wholly unconscious mental structure called the id contains a person’s inborn, inherited drives and instinctual forces and is closely identified with his or her basic psychological energy (libido). During infancy and childhood, the ego , which is the reality-oriented portion of the personality, develops to balance and complement the id. The ego utilizes a variety of conscious and unconscious mental processes to try to satisfy id instincts while also trying to maintain the individual comfortably in relation to the environment. Although id impulses are constantly directed toward obtaining immediate gratification of one’s major instinctual drives (sex, affection, aggression, self-preservation), the ego functions to set limits on this process. In Freud’s language, as the child grows, the reality principle gradually begins to control the pleasure principle ; the child learns that the environment does not always permit immediate gratification. Child development , according to Freud, is thus primarily concerned with the emergence of the functions of the ego, which is responsible for channeling the discharge of fundamental drives and for controlling intellectual and perceptual functions in the process of negotiating realistically with the outside world.

Although Freud made great contributions to psychological theory—particularly in his concept of unconscious urges and motivations—his elegant concepts cannot be verified through scientific experimentation and empirical observation. But his concentration on emotional development in early childhood influenced even those schools of thought that rejected his theories. The belief that personality is affected by both biological and psychosocial forces operating principally within the family, with the major foundations being laid early in life, continues to prove fruitful in research on infant and child development.

Freud’s emphasis on biological and psychosexual motives in personality development was modified by German-born American psychoanalyst Erik Erikson to include psychosocial and social factors. Erikson viewed emotional development over the life span as a sequence of stages during which there occur important inner conflicts whose successful resolution depends on both the child and his or her environment. These conflicts can be thought of as interactions between instinctual drives and motives on the one hand and social and other external factors on the other. Erikson evolved eight stages of development, the first four of which are: (1) infancy, trust versus mistrust, (2) early childhood, autonomy versus shame and doubt, (3) preschool, initiative versus guilt, and (4) school age, industry versus inferiority. Conflicts at any one stage must be resolved if personality problems are to be avoided. (Erikson’s developmental stages during adulthood are discussed below in the section Development in adulthood and old age .)

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Behavioral Research: It’s Importance and Best Methods

Regardless of whether we like it or not, our histories, habits, and emotions all have a significant part in our behavior. Behavioral research uses measurement and interpretation to explore and comprehend individual and societal behavior. In this blog, we’ll discuss why behavioral research is important and the methods to do it.

LEARN ABOUT: Research Process Steps

What is Behavioral Research?

Behavioral research is the combination of quantitative and qualitative methods to measure human behavior, get new data, and analyze the effects of active treatment situations on human behavior.

Human behavior fascinates a lot of people. What causes our behavior? What influences or measures our behavior? And why is it so tough to change one’s behavior?

Human behavior research has played a significant role in uplifting the livelihoods of people suffering from mental illnesses and behavioral disorders. It has also assisted breakthroughs in child development, organizational culture monitoring, and public health. Professionals interested in learning how to analyze human behavior want to comprehend why individuals make decisions to better grasp the decision-making process.

What Is the Importance of Behavioral Research?

Applied behavior analysis (ABA) is a behavioral science subject that studies behavior principles, learning, motivation, and strategies for changing behavior . Schools, clinical services, and behavioral health institutions are among the places where applied behavior analysts can work.

Their job includes designing and implementing behavior modification strategies based on observation and data analysis . Counseling, psychology, and special education are also specializations that are certainly part of applied behavior analysis.

Human behavior research spans a wide range of scientific and social disciplines. Behavioral science is defined by the American Psychological Association as any subject (for example, psychology, sociology, or anthropology) that uses experiments and observation to explore human and nonhuman actions and reactions in a scientific manner.

Behavioral science includes a wide range of fields, including Anthropology, economic behavior, psychology of cognition, consumer behavior, psychology of social interaction, and sociology.

Some behavioral scientists combine ideas, concepts, and approaches from several fields to fully comprehend the complexities of human behavior.

Behavioral scientists investigate why humans sometimes act in ways that are detrimental to their well-being. They look at how random environmental influences shape our choices, beliefs, and attitudes. Researchers are also looking at how customers might be enticed to accept, avoid, or adjust their buying decisions.

LEARN ABOUT: 12 Best Tools for Researchers

Human Behavior Research Methods

1. Behavioral perspectives

According to behaviorists, all behaviors are learned via experience and are learned through contact with our environment. Classical and operant are two key ideas in learning new behaviors.

Something fresh is combined with something natural in classical conditioning. After some time, the new stimulus elicits the same reaction as the previous stimulation, leading to a new association.

Comparing qualitative and quantitative research

All sorts of research devices are available to measure human behavior. These tools are classified as qualitative or quantitative research measures.

LEARN ABOUT: Qualitative Interview

• Qualitative research

By examining underlying causes, beliefs, and motives, qualitative measures assist researchers in better understanding human behavior. It aims to explain “how” and “why” individuals act the way they do.

In-depth conversations, focus group discussions, observations, and unstructured surveys with open-ended questions are examples of qualitative data measurement methods. Qualitative research should ideally be performed in a natural situation.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

• Quantitative research

Quantitative measures, on the other hand, are used to measure preferences, views, facts, actions, and other types of analyzed data and to apply results from a broader sample group. This is used to provide numerical responses to inquiries such as “How many?”, “How often?”, and “How much?”

Surveys , structured questionnaires, and internet polls with closed-ended questions are examples of methods for gathering this information.

2. Observation

Making observations is a crucial element of researching human behavioral research. What better method to understand someone’s actions than to observe them? What interactions does your test participant have with a kid, a patient, or a computer?

Observational research is usually done at home, work, or in a specially equipped observation lab. Unobtrusive observation is the greatest technique to see one’s genuine conduct.

Another technique to observe someone’s behavior is to examine the inside of them, especially their brain. Because most of the activities that happen inside are not visible to all of us, studying brain activity can provide fresh insights into human behavior.

When researching a wounded brain and the impact it has on a patient, we can see connections between behavior and brain activity. In certain circumstances, brain evidence aids in the resolution of long-standing psychological riddles.

3. Measures that are not obvious

While questionnaires are excellent for gathering ideas, personality traits, and (mental) health concerns, they do have significant drawbacks.

Approximately 95% of our actions are unconscious and automatic. Researchers have devised methods to record our subconscious thoughts, feelings, and actions. Indirect tests demand that people react quickly to a variety of inputs. Differences in reaction times reveal how you feel about something or someone.

4. Medical research

It’s all about the quality of life and, as a result, quality of care in the medical field of care research. According to Wikipedia, “well-being quality of life” assesses how a sickness, disability, or ailment may impair an individual’s well-being over time. The greatest goal of healthcare providers is to improve their patient’s quality of life by delivering the most acceptable available treatment.

Doctor-patient contact, operating room architecture, simulation training, team effectiveness and communication, and coping with emotions are all possible themes for healthcare research . All of these factors influence quality care and, hence, life.

5. Research on education and training

Through education, students learn facts, events, values, ideas, basic concepts, principles, and other things. Training, on the other hand, is a method of acquiring abilities rather than simply learning facts. Training is focused on practical application, including hands-on experience. They assist individuals in implementing a new system, improving a particular ability, or advancing their proficiency in anything.

Education and training may occur in a variety of professions and places, including classrooms and skills laboratories. In a safe, controlled atmosphere, the theory may be put into practice.

6. Consumer behavior research

The most incredible method for understanding customer decision behavior and preferences is identifying the target audience. How do customers interact with a product? Why did they decide to purchase the item in the first place? Was it because of the packing or the layout of the store? Or were there any unconscious emotions at play?

LEARN ABOUT: Action Research

Human action is a multifaceted and dynamic topic of behavioral research study that necessitates several lines of inquiry to provide insights. Learning processes set the framework for deciding many of our actions. However, we are constantly evolving in reaction to our environment.

Understanding our actions is a complex undertaking, but it is one that we are coming closer to completing. Traditional research approaches have taught us a lot, and now detection methods can guide us.

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Behavioral Research Blog

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How to study human behavior

Many people are fascinated by human behavior. Why do we act the way we do? How is our behavior influenced, or measured? And why is behavioral change so difficult?

In this blog post, we describe several behavioral theories, as well as different ways to measure human behavior . Lastly, we discuss research fields in which human behavior analysis plays a central role, like psychology, health care, education, and consumer research.

Table of contents

Perspectives on behavior, qualitative versus quantitative research, observation, physiological measures, implicit measures, developmental psychology research, mental disorders, health care research, education & training, user experience and human factors research, consumer behavior research.

Influenced by prominent thinkers like John B. Watson and B.F. Skinner, behavioral psychology gained popularity between 1920 and 1950. With its focus on observable behavior instead of mental states , behaviorism provided a systematic way to study human behavior .

Behaviorism: it’s the environment

Behaviorists argue that behavior is learned in interaction with our environment, and that all behaviors are learned through experience. Behavior analysis is the scientific approach to understanding behavior and how it is influenced by environmental factors. It is based on the principles of behaviorism, which emphasizes the role of the environment in shaping behavior. 

Behavior analysts focus on identifying the antecedents (what happens before behavior occurs) and consequences (what happens after behavior occurs) that influence behavior, and then use this information to develop interventions that can change behavior. 

Two key principles that are involved in new behavior are classical and operant conditioning .

In classical conditioning, something new is paired with something that occurs naturally. After a while, this new stimulus triggers the same reaction as the original stimulus, resulting in a new association. A famous example of this principle is found in Ivan Pavlov’s research.

What is classical conditioning?

In his experiments with dogs, Pavlov paired the sound of a bell (new stimulus) with the presentation of food (naturally occurring stimulus). Eventually, the dogs started salivating when hearing the bell, even when no food was presented (new association). And voilà, new behavior is learned.

What is operant conditioning?

The second learning principle, operant conditioning, describes the way our behavior is shaped by consequences. Specifically, it states that reward and punishment can influence the likelihood that behaviors occur again.

Think about how you praise a child when she eats her vegetables , or how you might take away a favorite toy when she’s mean to her brother. In both cases, you provide her with a consequence for her behavior. Chances are, she’ll eat her vegetables next time as well, and will think twice about teasing her brother.

These methods explain much of how human behavior is shaped. However, critics argue that behaviorism fails to take into account important factors like free will, internal influences, and other types of learning. In the next paragraphs, we will explore two other behavioral theories.

Social learning theory: it’s other people

Social learning theory was proposed in the 1970’s by Albert Bandura, who believed that conditioning alone could not account for all behaviors. At its core, social learning theory proposes that people learn from observing others.

This observational learning does not only occur when observing another person, but also when listening to descriptions of behavior or viewing symbolic models. From very early on, this provides us with many learning opportunities.

Importantly, social learning theory emphasizes that behavior is also influenced by mental states , like motivation or thoughts.

Another difference with behaviorism is that observational learning does not necessarily lead to permanent behavioral changes. In other words, people might learn new information without actually showing new behaviors.

So far, we’ve established that human behavior can be shaped through experience and observation. A third method of learning behavior is described by Relational Frame Theory.

Relational Frame Theory: it’s our language

Relational Frame Theory (RFT) was developed in the 2000’s by Steven Hayes, Dermot Barnes-Holmes and Brian Roche, and builds upon classic behaviorism. What’s new in this theory is the role of human language in learning new behavior .

Specifically, RFT researchers argue that language enables us to learn new information indirectly. The key learning processes of this theory are centered on relations between stimuli, or relational frames.

As an example, imagine you meet three new people: Aaron, Bianca and Chris. Aaron introduces himself to you, and explains that Bianca is his sister and Chris is his father. Importantly, with these relations learned directly (B-A, C-A), you now know several other relations indirectly.

First, you can derive that Aaron is Bianca’s brother (A-B) and Chris’ son (A-C). This is called ‘mutual entailment’ in RFT. But you can also understand that Bianca is Chris’ daughter (B-C) and that Chris is Bianca’s father (C-B), which is referred to as ‘combinatorial mutual entailment’.

Even though these relations may seem obvious, the example shows that we can learn indirectly, through relations or frames we already know. The more relations we know, the more relations we can derive. And, like social learning theory, it shows that we don’t need actual experience to learn new information.

Why is changing behavior difficult?

As you might know from personal experience, changing behavior can be difficult. Maybe you have tried to exercise more or to eat more healthy foods, only to find yourself back to your old habits one week later.

Why is behavioral change so difficult? And what can you do to succeed anyway?

First, it is important to know that about 95% of our behavior is driven by unconscious processes. It would simply take too much mental energy to be aware of all our behaviors. So instead, most of it becomes automatic, part of our habits.

Second, behaviorism has taught us that the environment, through associations and consequences, shapes our behaviors. So let’s examine the consequences of a behavior we want to change.

To return to our previous example, imagine that you want to exercise more often. What are the consequences of that behavior? More energy, better health, slimmer body. Sounds good, right? But to get there, you have to work out, sweat, and skip comfortable hours on the couch.

Do you notice the difference between these consequences, specifically in their timing? The unpleasant consequences come immediately, while you have to persevere for weeks or even months until you can experience the pleasant consequences of your behavior.

If consequences drive our behavior, it’s not surprising that the immediate, visible rewards win over the long-term, abstract ones. And if we’re not aware of 95% of our behaviors, how can we expect to change them? Luckily, we can also use these principles to our advantage.

Behavioral change

A powerful way to make use of our automatic behaviors is through habit stacking. Basically, this means that you take an existing habit, and then add a new behavior to it. For example, when you regularly forget to take your medicine, you ‘stack’ this behavior on top of a habit you have already mastered, like brushing your teeth in the morning.

By making a new behavior part of an automatic routine, you are more likely to stick with it. The beauty of it is that you can keep stacking on new behaviors, once a new behavior has become a habit of itself.

The power of consequences

You can also harness the power of consequences, by creating ways to make the short-term consequences of your desired behavior more positive, while making the short-term consequences of procrastination more costly.

For example, you can reward yourself each time you eat a healthy meal. Simply praising yourself for each step in the right direction can make a difference, as can actively imagining the long-term benefits of your behavior. And if you want to make the consequences of procrastination more costly, you can try teaming up with a buddy, making your intentions public, or putting an expensive bet on your behavior.

Change your environment

Another important step you can take is changing your environment. If your TV is on and your sports clothes are in the attic, it’s much easier to lounge on the couch than it is to start your workout. Instead, unplug the TV and keep everything you need to start exercising in sight.

Principles like these are also used in (cognitive) behavioral therapy. With the help of a therapist, behaviors involved in anxiety , depression, addictions or other mental disorders are examined and changed gradually. To provide people with the best care possible, accurate measurements and continued research of behavior are essential.

To measure human behavior , all kinds of research instruments are at our disposal. These instruments can be divided into qualitative measurements and quantitative measurements. What is the difference between qualitative and quantitave research? Let us explore this further.

What is qualitative research?

Qualitative measurements help researchers to understand human behavior on a deeper level by studying underlying reasons, opinions, and motivations. Usually it is used for exploratory research. They are particularly helpful in understanding the context of phenomena, and how they affect individuals and groups. It is all about the details. It seeks to explain ‘how’ and ‘why’ people behave as they do.

Methods to measure qualitative data

The sample size in this type of research is typically small since it’s hugely labor intensive. Methods to measure qualitative data are for example in-depth interviews, focus group interviews, behavioral observations , and unstructured questionnaires using open-ended questions. Preferably, qualitative research is conducted in a natural setting.

What is quantitative research?

On the other hand, quantitative measurements are used to quantify preferences, opinions, facts, behaviors, and other defined variables – and generalize results from a larger sample population. It is used to answer questions such as “How many?”, “How often?”, “How much?” of which the answers are expressed with numbers. You can statistically analyze the collected data. Methods to collect this data are e.g. surveys, structured questionnaires, and online polls, using close-ended questions.

Combining qualitative and quantitative data

The overall purpose of research is discovering the truth. However, “without good data, you’re just guessing.” Combining qualitative data with quantitative data will provide researchers in-depth information about a certain behavior, and various aspects of that behavior. The two different approaches complement each other, while the shortfalls of each will be balanced.

Read more: Examples of Human Behavior Research

A very important part of studying human behavior is performing observations. What better way to explain someone's behavior than by observing that person? How is your test participant interacting with a child, a patient, or computer?

Observational research

Observational research is typically performed in one’s home, workplace, or a specially designed observation lab . The best way to observe one's true behavior is unobtrusively. With a one-way-mirror, it is possible to watch every move of your subject, without being physically present in the room. By using The Observer XT , you can annotate all of the behaviors of interest and perform analysis, turning qualitative data into quantitative data.

Video observations are also a great way to study human behavior. The use of video greatly expands the scope of any research project. Annotating from video allows you to make frame-accurate descriptions of behavior. Viso  is an ideal solution for high quality recording of video and audio in multiple rooms , and provides the video material needed to gain insights into processes, human performance, and communication.

Observing behavior in the brain

Another way of observing one's behavior is by looking at the inside, more specifically: by looking at their brain. As we are not conscious of most of the processes that happen on the inside, looking into brain activities can give new insights into the behavior of people. 

We can observe correlations between behavior and brain activity, for example when studying an injured brain and the influence this has on a patient. In some cases, brain evidence helps to resolve puzzles that psychologists have wrestled with for decades.

Learn more about observing behavior by looking at the brain in the blog post: Cognitive neuroscience: Behavior.

Although your test participant may appear to be calm, he or she may be concealing a substantial amount of stress. To reveal this level of stress, you can combine behavioral coding with physiological measurements acquired with a data acquisition system .

This allows you to simultaneously acquire physiological data, such as EEG, ECG, EMG, blood pressure, skin conductance, and facial expressions , while you are collecting observational data. For example, skin conductance is a method of measuring the electrical conductance of the skin, used as an indication of psychological or physiological arousal. EEG makes it possible to include neuronal activity during a test, while FaceReader  captures the facial expressions. These measurements make it possible to study the interplay of physiology and behavior, caused by an external event.

Multimodal research made easy

Combining multiple types of measurements can be demanding, especially when using acquisition tools from different manufacturers that have to be calibrated, started, and synchronized. Straightforward yet powerful is NoldusHub , an all-in-one research platform for human behavior studies. 

This brand-new software suite was specifically designed to will streamline multimodal research from start to finish, and take the hassle out of measuring multiple modalities with a range of devices at the same time. By doing so, researchers are able to get high-quality data and a more comprehensive understanding of human behavior. 

While questionnaires can be useful in capturing opinions, personality traits, or (mental) health issues, they also have some limitations.

One important issue is that people can be biased in their responses. We tend to give answers that are socially desirable, we are influenced by an experimental setting, or we answer all questions a certain way (mostly extreme or neutral, mostly ‘yes’ or ‘no’).

Also, we’re not always aware of what we think, feel or do. As we mentioned earlier in this blog, about 95% of our behavior is unconscious and automatic. Some information about ourselves might escape our attention.

With these limitations in mind, researchers developed measures to capture our unconscious opinions, emotions and behaviors. These implicit tests require people to respond very quickly to different stimuli. Differences in response times reflect how you really feel about something.

Basically you respond faster when you agree.

For example, a test might involve asking participants to sort words into categories that are on either side of a computer screen.

You’d have to choose left when encountering a positive word (‘good’, ‘happy’) or a picture of a cat. You’d choose the right side when seeing a negative word (‘bad’, ‘ugly’) or a picture of a dog. In another phase of the test, these pairings are reversed.

If you were to respond more quickly when ‘positive’ and ‘dogs’ are associated, compared to when ‘positive’ and ‘cats’ are, this suggests you have an implicit preference for dogs. Other implicit tests assess approach and avoidance behavior, attention biases, or relational frames.

Developmental psychology is the scientific study of how and why humans change over the course of their life. Developmental psychologists aim to explain how thinking, feeling, and behaviors change throughout life.

Although developmental psychology involves the entire lifespan, researchers mostly focus on the period in which changes follow each other fast, from birth to early adulthood. They explore topics such as children’s basic understanding of the physical world, how children acquire language, how learning behaviors develop, and how they interact socially with other people, for example in parent-child interaction studies . 

Ways to study development

The use of video greatly expands the scope of such a research project. It enables researchers to capture infants' behaviors and reactions while they perform a task, are exposed to a novel object, are playing with a sibling or peer, or are having a meal. Coding the videos , whether they are made at home or in a lab, enables you to review, visualize and analyze the behaviors quickly.

Baby FaceReader  has been developed as a state of the art system to automatically detect infant facial expressions in order to help address questions in developmental psychology related to affect and developmental disorders such as Autism Spectrum Disorder (ASD). 

Research helps us to understand what infants learn, what they are processing, and what factors are influencing the development. In another blog post, 5 examples of infant studies are highlighted.

Understanding human behavior is essential in the prevention and treatment of mental disorders .

Mental disorders are defined as a combination of abnormal thoughts, emotions, and behaviors. Worldwide, millions of people suffer from disorders like depression, addiction, anxiety, and dementia.

Aside from a range of psychological and cognitive symptoms, people with mental disorders often experience problems in school, work, or family life as well.

Understanding mental disorders

With this variety in symptoms and contributing factors, research plays an important role in the further understanding of these disorders.

For example, recent studies examined the effectiveness of a game-based intervention in social anxiety , the best ways to observe pain expressions in dementia , doctors’ emotions during the treatment of depression , and the role of social cognition in the development of social anxiety .

Studies like these help to improve the prevention and treatment of mental disorders, and contribute to overall mental health.

In the field of health care research, it is all about quality of life and consequently about quality of care. Wikipedia describes health-related quality of life as an assessment of how the individual's well-being may be affected over time by a disease, disability, or disorder. Health care professionals’ highest aim is to improve quality of life for their patients, by providing the best possible quality of care.

Various topics can be of interest for health care research, such as doctor-patient interaction , operating room layout , simulation training , team performance and communication , or dealing with emotions . All have an impact on the quality of care and thus life.

Using video technology

With help of Noldus solutions, researchers will gain the insight they need in processes, human performance, and communication. For example, the use of video technology offers important advantages to scientists in unraveling complex behavior patterns and finding relationships between behaviors, effectiveness of interventions, and more.

Education is all about gaining knowledge about facts, events, values, beliefs, general concepts, principles, etc. to students. On the other hand, training is a way to develop skills, rather than just know about something. Training is based on practical application, it involves hands-on experience and helps people to implement a new system, improve a specific ability or further their ability in something.

Education and training can take place in a wide variety of fields, and in many different settings such as classrooms or skills labs. Theory can be put into practice in a safe and controlled environment.

Video feedback tool

More and more video feedback is used in education and training facilities. It continues to prove its effectiveness for both educators and students. By using video and audio recordings in education and training , students and educators can receive, and benefit from, direct feedback.

Classroom observations

To examine, for example, effective teaching and teacher-student interaction, classroom observations can be used as a technique to gain the desired insights. Using a software tool to observe the questions students ask, how the teacher reacts, and whether the teacher checks if the students understand his explanation, will make the research much easier. It enables to code behaviors accurately, record one or multiple videos, integrate data modalities, and explore the results.

What happens with a person’s emotions and attitudes when using a particular product, website, application, or system? What does the user experience, and how does the user interact? Is it used in the way that it is meant to be used? Finding answers to these questions enables to optimize that particular product, website application, or system. To meet the needs of its users, to improve the  user experience .

Tools to measure ease-of-use and efficiency

Feedback methodologies such as observation and task analysis will reveal the ease-of-use and efficiency of a product or service. While you observe the user of your product, you can receive direct feedback about your product.

To facilitate UX tests , you can record audio and video streams from digital cameras, capture the eye tracking gaze-overlay and emotions, and add notes or markers on the fly for quick lookback.

How to build a usability lab?

UX tests can be carried out in a usability lab , or on-site with a portable lab. It provides controlled conditions, and fully integrated equipment and software to make the tests as realistic as possible. A few examples are the Social Media Lab in Mons, Belgium, which connects with business and academics in order to better understand social media use and the digital world in general .

In the Human Factors and Ergonomics Laboratory of Zodiac Seats, participants were asked to evaluate six different travel pillows on head and neck support while sitting in an airplane seat and trying to rest or sleep.

In a study with bicyclists, researchers at VTI, the Swedish National Road and Transport Research Institute, observed cyclist behavior using eye tracking technology, video recordings, and behavioral coding . All data combined enabled them to assess whether the cyclists met the demands in specific situations.

Find more examples of several labs in this interactive pdf .

Product portfolio download "Labs"

Getting to know your target audience is the best way to understand consumer choice behavior and preferences . How do consumers use a product? Why did they choose to buy that product in the first place? Did packaging or store layout have anything to do with that? Or did unconscious emotions play a role?

Recent studies aimed to answer several of these questions. Here, we list a few:

• The study of Eliza Kostyra and colleagues used face-reading technology in order to determine the effect of smoked ham samples on consumer emotion , which may lead to deeper insight on whether or not a consumer chooses to buy the sampled product.
• In France, a team of researchers designed a test kitchen including four dome cameras and one portable GoPro camera to investigate food storage processes .
• Researchers Torrico and Fuentes tried different implicit measurement techniques in order to get closer to measuring liking and preference using images and chocolates!

A more in-depth blog post about consumer behavior research can be found here: Understanding consumer buying behavior .

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https://www.verywellmind.com/behavioral-psychology-4157183

https://www.verywellmind.com/social-learning-theory-2795074

https://contextualscience.org/what_is_rft

https://jamesclear.com/habit-stacking

https://jamesclear.com/time-inconsistency

https://www.simplypsychology.org/qualitative-quantitative.html

http://www.socialresearchmethods.net/kb/qual.php

https://www.theclassroom.com/qualitative-measurements-8473589.html

https://keydifferences.com/difference-between-training-and-education.html

https://www.nextiva.com/blog/response-bias.html

https://implicit.harvard.edu/implicit/iatdetails.html

https://www.who.int/news-room/fact-sheets/detail/mental-disorders

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100+ Application Areas in Human Behavior Research

Ariadna Hidalgo

One of the top questions we are frequently asked is how biosensors can be utilized to assess human behavior.

The good news upfront: Biosensors offer an almost unlimited application variety to reveal the (sub-)conscious processes underlying behavioral responses. On the flipside, exactly this ample pool of application possibilities is probably one of the reasons researchers often feel overwhelmed or indecisive about which biosensor or combination of biosensors might be the right fit for their research endeavor.

To help you get started, we recently have released a handy sensor chart giving you all the specs you need to know to decide which sensor is most suitable to answer your research question.

Today there is even more guidance coming your way: Drawing from our many years of experience in human behavior research, we have pulled together more than 100+ application examples grouped by 20 application fields to provide you with an in-depth insight into the rich diversity of multimodal research.

We hope you will be inspired.

Psychology and Psycholinguistics

Medical diagnostics and health, education and training, product testing, advertisement, product packaging, in-store testing, aroma and taste, user interface (ui) and user experience (ux) testing, virtual reality (vr), human-computer interaction (hci) and brain-computer interfaces (bci), architecture.

Assess respondents’ attention for sensory stimuli of various modalities using visual cues (moving dots, flankers), auditory cues (complex sounds, voices) or haptic cues (electrical skin stimulation, object manipulation).

Present texts and assess respondent’s reading rates (first pass vs. second pass reading) to evaluate the depth of learning.

Record respondents’ recognition rate for certain stimuli and assess physiological data such as eye tracking and EEG during the correct and incorrect recall of learned information.

Group interaction:

Acquire biosensor data from group participants during collaborative and competitive discussion sessions.

Sleep studies:

Assess sleep stages (1-4, rapid-eye-movement [REM] sleep) and evaluate sleep quality; investigate how pharmaceutical drugs effect sleep and dreams.

Research on meditation:

Investigate the effects of meditation on cognitive states and emotional well-being.

Traditional psychological testing :

Add biosensors to traditional psychological testing procedures to dive deeper into emotional and cognitive behavior.

Parent-infant interaction assessment:

Assess the emotional and physiological states of babies and their parents during play.

Scientific studies of reading:

Utilize Eye Tracking to help improve children’s reading capabilities.

Therapeutic interactions:

Evaluation of the relationship between emotions and affective states among neurologists while they are making their therapeutic decisions.

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Autism research and therapy:

Expose children to videos containing different levels of social interaction to detect behavioral cues for autism spectrum disorders. Use biosensors to monitor therapy impact.

ADHD research and therapy:

Utilize biosensors to help diagnose early stage ADHD and monitor therapy success.

Parkinson’s disease and treatment:

Make use of eye tracking measures and EMG to detect impaired motor behavior and eye-hand coordination in early stages of Parkinson’s disease and measure treatment success.

Post-traumatic Stress Disorder (PTSD) and therapy:

Use biosensors such as GSR, ECG or respiration to screen for signs of PTSD and monitor therapy success.

Mild Cognitive Impairment (MCI):

Apply EEG, surveys and eye tracking to measure decline in cognitive abilities.

Pharmaceutical treatment:

Investigate the efficacy of pharmaceutical treatment in late-stage human clinical research trials.

Use EEG to identify and localize epileptic seizures.

Check out our Mobile research platform- clinical research with biosensors

Lesion studies:.

Assess brain states and cognitive impairment of patients suffering from brain lesions. Evaluate surgery success and rehabilitation training.

Cochlear Implant and hearing aid training:

Use biosensors to assess outcomes of surgical procedures restoring hearing, for example using sound or language stimuli.

Traumatic brain injuries:

Utilize biosensors such as eye tracking, facial expression analysis and EEG to help diagnose and assess brain injuries and head traumas.

Vestibular and motor diseases:

Utilize mobile eye tracking in conjunction with EEG to assess vestibular/balance diseases in older patients

Multiple Esclerosis:

Use of eye tracking and electrodermal activity devices to help in the diagnose

Medical devices usability:

Use of eye tracking to test the user’s ability to identify the device status.

Read more about: Four Inspiring Ways Biosensors are Used to Improve Healthcare Performance

Instructional assessment:

Use eye tracking and other biosensors to assess teacher’s perception of classroom atmosphere. Identify expert and novice teachers’ behaviors and identify problems.

Learning technologies:

Utilize biosensors to find out how students learn both in the classroom and online.

E-learning:

Assess the effectiveness of online courses and programs.

Construction worker training:

Facilitate hands-on trainings with simulations to see how construction workers react to potentially hazardous situations.

Special ops training:

Use biosensors in virtual reality environments to assess the effectiveness of military training and monitor learning process.

Military simulation:

Test stress levels and emotional responses in important decision situations, in a military (game) simulator with GSR, eye tracking and EEG.

Workspace organization and optimization:

Understand the workspace setup in different industries to optimize employee engagement and performance; detect influencing factors of tiredness and boredom at the workplace.

Human-Robot-Interaction:

Record eye tracking and other modalities from high-tech workers in high-tech assembly lines (collaborating with human colleagues or robots).

Eye-Hand Coordination:

Use eye tracking and other sensors such as EMG to assess eye-hand coordination during assembly of parts and products. Improve assembly times and reduce errors or injuries.

Product usability:

Test whether a product is intuitive to use for first time buyers; identify elements that cause frustration and confusion with the product.

Concept testing:

Expose respondents to product descriptions (images and/or text) to measure how well they are perceived; test if respondents can relate to the product description.

Product experience:

Measure emotional reactions while respondents interact with the product to test the overall product experience.

Prototype assessment:

Test new product prototypes to assess the emotional impact while respondents interact with a new product.

TV commercial testing:

Test TV commercials based on emotional impact and brand memory.

Trailer testing:

Test trailers and promotional videos to assess appeal to viewers and their motivation to watch the actual program.

General advertisement testing:

Measure which elements of an advertisement capture the respondents’ attention and engagement.

Static vs. dynamic ad testing:

Test static and dynamic advertisement inserts to assess emotional arousal and advertisement effectiveness.

Outdoor commercial (banner ad) testing:

Have respondents walk around outdoor with Eye Tracking glasses to test the appeal of outdoor commercials.

Website testing:

Assess the impact of online ads to evaluate their effectiveness based on placements, size, and level of interactivity.

Read more: How to do Website UX and Usability Testing: Guide to Advanced Methods

Media testing:

Measure moment-to-moment responses to dynamic content (videos) to detect which moments and scenes trigger emotional responses.

TV Program testing:

Test TV programs for emotional impact to assess success rates of new shows, seasons or episodes.

Movie testing:

Test movies for emotional impact to assess success rates; use the findings to predict sales.

Variant testing:

Test the level of positive impact and performance of various forms of packaging variants on respondents.

Benchmark with competition:

Test how well a certain form of packaging performs compared to competing products.

Unpacking testing:

Evaluate if a certain form of packaging is easy to unpack and whether it induces frustration in respondents.

Dynamic Packaging design Testing:

Evaluate new product design while purchasing in a realistic shopping environment.

Learn more about Consumer Behavior Research with iMotions

Promotional poster (ad) testing:

Investigate whether promotional in-store banners can be associated with future purchases.

Product standout:

Test how strongly products stand out compared to other products within a product category.

Planogram testing:

Assess how a certain retail planogram performs compared to other planogram types.

Shelf testing:

Investigate the overall shelf design or shelf layout within a category to optimize product arrangement.

Point of Sale (POS) testing:

Test the impact of in-store Point of Sale promotions.

Store testing:

Test respondents’ overall emotional experience based on different store layouts.

Product pricing:

Assess price points and purchasing decisions made based on different product pricing.

Product choice:

Test and compare in-store buying patterns based on different product categories or brands.

Test aroma of a certain product and associated product experience to evaluate the effectiveness of different aromas.

Fragrance testing:

Evaluate fragrances and products scent optimize emotional impact induced by a certain fragrance.

Food testing:

Test emotional responses to different kinds of food.

Multi-sensory congruency:

Use biosensors to determine the best match between product taste, appearance, and overall look-and-feel.

Check out our Publications with Sensory

Assess the usability and emotional impact of a website and compare with competing website or variant (A/B testing).

Software testing:

Test the usability and identify frustrating pain points of a software application.

Mobile platform testing:

Analyze the usability of apps or websites on mobile devices and tablets.

Check out: How Marketers Can Keep Mobile Users Engaged During COVID

Identify target audiences:.

Use biosensors to collect from various respondent groups to identify those who respond most positively to the product.

Action prediction:

Synchronize eye tracking measurements with game environment events to predict player actions before actual performance.

Biofeedback:

Use facial expression detection to interact with in-game avatars.

Success prediction:

Test players’ emotional reactions while playing to predict overall success of game in the market.

Persona behavior testing:

Evaluate different player personas and get a sense of emotional drivers based on novice and advanced gamers.

Level testing:

Test different game levels based on emotional reactions to optimize for best experience.

Avatar testing:

Test different avatars to ensure they have the expected emotional impact on gamers.

Read more about Biometric testing from home to improve Gaming Experience

Assessment of stress and excitement levels:

Use biosensor data to change virtual environment in order to create events that can excite or stress respondents at the right moments.

In-store testing:

Monitor respondents’ emotional expressions during store exploration. Aggregate emotional expressions across locations to identify store sections that trigger certain emotions.

Packaging testing:

Test different packaging variants in-store.

Test shopping behavior and product choice in-store.

Shelf layout:

Test different shelf concepts and predict effectiveness.

Shopping experience:

Test different store layouts to assess emotional impact on shoppers.

Game immersion:

Evaluate stress levels, engagement, motivation, and arousal to optimize interfaces and overall gaming experience.

VR-based phobia treatment:

Use VR as training environment for treatment of phobias (arachnophobia, agoraphobia, PTSD etc.), and record biosensor data to monitor exposition success

Tourism experience:

Use of VR and eye tracking to examine visual attention toward tourism environments or photographs.

Human-robot and human-avatar interaction:

Assess emotional responses during social interaction with physical avatars and robots or chat bots. Use biosensor data to improve interaction.

Brain-Computer Interfaces (BCI):

Assess brain activity associated with reaching and grasping. Use signals to steer robotic limbs and robotic devices.

Reaction time testing:

Take CAN bus signals (such as steering wheel, brake, throttle signals) and record in synchrony with biosensor data to test for response times and user errors during driving.

Activity testing:

Record facial expressions during a city drive and identify where in town and when frustration or joy levels are highest.

Distraction testing:

Test emotional attention towards billboards, outside banner ads, and inside (music) and their impact on driving behavior.

Controls Usability:

Test the usability of in-car controls (heater, radio, media console etc.) to assess usability and overall user experience.

Hands-free testing:

Test how hands-free driving while speaking on the phone affects driving.

Exterior car design:

Evaluate the immediate impact of a new car design by measuring emotional impact and assess potential success/familiarity/coolness.

Check out how Mazda + iMotions: Emotional test drive

Interior car design:.

Assess visual attention and emotional awareness for certain design elements.

Cabin experience:

Test the overall user experience associated with driving a car

Controls usability while performing a task:

Test usability of tractor or specific work transportation controls and tools while performing a work task.

Drowsiness detection:

Record eye tracking, facial expressions and/or EEG to detect drowsiness episodes, for example after long, monotonous drives without stressful events.

Accident prevention:

Use biosensors to automatically stop the car in case of live-threatening situations, e.g., heart attack of the driver

Autonomous car testing:

Test drivers safety and driving skills while, unexpectedly, have to take over an automatic car in different urban driving scenarios.

Emergency training:

In an aircraft simulator, test pilots gaze points and arousal levels (GSR) in emergency situations

Special training:

Aircraft simulator – test the pilots workflow with GSR, eye tracking and EEG from takeoff to landing

Air traffic controller monitoring:

Assess stress and attention levels of air traffic controllers and suggest breaks if levels are beyond certain limits.

Emergency care simulation:

Use of eye tracking to investigate how health care professionals acts in emergency situations

Surgery Training:

Use of eye tracking to test and train doctors on how to perform different surgeries.

Check out Healthcare Applications

Building testing:

Assess attentional and emotional reactions to architecture and building structures. Test emergency routes.

Design testing:

Test facial expressions or EEG as respondents are interacting with design models to identify the most positive and impactful draft before mass production.

Environment testing:

Test the architectural environment to assess the impact of the surroundings on the respondent.

Read more on: How architecture affects human behavior

Speech impact:

Observe how political speeches drive emotional responses of a crowd by evaluating campaign videos.

Campaign material testing:

Test the emotional effects of political campaign material by presenting pictures and videos to target audiences.

Candidate appeal:

Test candidate behavior by presenting pictures and videos, and assess how clothing, body posture, etc. appeal to the audience.

Physiological testing:

Test sports activity with EEG and an environmental camera.

Check out our webinar on Sports Performance

Leadership training:

Executives and those in leadership roles need to know to better communicate with others around them by being aware of their own body language. Use biosensors and facial expression analysis to assess behavioral impact of true leadership.

Pitch training:

Test emotional impact on an audience to improve the presenter’s ability to pitch effectively.

Group dynamics:

Test social group dynamics to test and assess leaders and non-leaders.

Feeling inspired? Let us know how we can help you and your academic research or business initiatives. Contact us or request a demo below to let us figure out how we can elevate your research.

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• v.39(1); 2016 May

Learning, the Sole Explanation of Human Behavior: Review of The Marvelous Learning Animal: What Makes Human Nature Unique

James s. macdonall.

Psychology Department, Fordham University, 441 E. Fordham Road, Bronx, NY 10458 USA

Seemingly everyone is interested in understanding the causes of human behavior. Yet many scientists and the general public embrace causes of behavior that have logical flaws. Attributing behavior to mental events, emotions, personality, or abnormal personality, typically, is committing one of a number of common errors, such as reification, circular reasoning, or nominal fallacies (Schlinger & Poling, 1998 ). An increasingly frequent error is embracing genetic explanations of behavior in the absence of an identified gene. Similarly, explaining behavior in terms of brain structure or function fails to ask what caused that brain structure or function to develop or function in a particular way.

As Arthur Staats ( 2012 ) notes in his valuable book The Marvelous Learning Animal: What Makes Human Nature Unique , unfortunately, such flawed explanations have prospered at the expense explanations based on learning mechanisms. Consequently, many behavior analysts would like to see a book that uses non-technical language to clearly delineate the limitations of explanations based on mind, brain, genes, and personality. Such a book would clearly describe how human behavior (both typical and problematic) can be understood in terms of learning principles, how myriad daily interactions from right after birth make us who we are, how the relevant behavioral research progresses, how interventions are developed based on the research, and how these interventions are subject to research demonstrating their effectiveness. The book would also describe the proper role of genetics and brain structure and function in an understanding of behavior. Perhaps no single volume can do all of these things equally well, The Marvelous Learning Animal is a useful complement to existing works with which behavior analysts may already be familiar (e.g., Schneider, 2012 ; Skinner, 1953 ).

The Great Scientific Error

Attributing causes of behavior to mind, brain, genes, personality, intelligence, abnormal personality, or genetics Staats calls the Great Scientific Error. According to Staats, learning was overlooked as a cause of behavior because early behaviorists did not develop research programs examining learning principles in complex human behavior, behavior occurring outside the laboratory under natural contingencies. Behaviorisms’ total rejection of “personality, intelligence, attitudes, interests or psychological measurement” (p. 33) exacerbated the problem in two ways. First, many in the general population rejected behavioral views because behaviorists rejected these concepts that seemed self-evidently true. Second, behaviorists did not examine the contingencies producing the behaviors subsumed under these labels. Research on reading and language shows the importance of identifying the natural contingencies in development (Hart & Risley, 1995 ; Moerk, 1990 ). Thus, Staats calls for a new learning paradigm that extends from the genetic basis of learning principles through how these learning principles function in complex human behavior. Given the methodological advances in genetics and neuroscience, Skinner, were he alive to see it, may well have agreed with this approach.

The Human Animal

Homo sapiens , according to Staats, are unique in two ways. First, humans have considerable sensitivity to a wide range of stimuli (e.g., light, sound, heat, and tactile). Within each stimulus modality, humans are not the most sensitive (e. g., many birds see better than we do). Some species can sense stimuli that humans do not sense (e.g., honey bees discriminate polarized light). However, we are the only species with very good sensitivity in many modalities. Similarly, we have a diversified motor system. True, other species have as much or more strength or fine control of specific motor systems (e.g., cats can jump further and with greater accuracy than we can jump). But we are the only species that has very good control of a wide variety of motor systems (e. g., facial muscles, hand/finger muscles, and arm and leg muscles).

Second, diverse sensory and motor systems need a brain that not only relays “messages” from sensory receptors to muscle fibers but also integrates the inputs from diverse sensory receptors along with neural results of prior experience producing complex sets of outputs to muscle fibers (what normally is called learning). It is estimated that humans have upwards of 100 billion neurons and on average several thousand synaptic connections for each neuron (Kolb, Gibb, & Robinson, 2001 ). This very large brain, interacting with our diverse sensory and motor systems, is what makes humans unique.

Child Development and the Missing Link

The Marvelous Learning Animal is informed by Staats’ own scholarly career, in which he focused on examining contingencies of naturally occurring behavior. Once Staats identified what he hypothesized were the critical contingencies, he would manipulate them to see if he could speed development and thereby demonstrate their importance. Throughout The Marvelous Learning Animal , Staats divides behavior and its development, for convenience, into three broad areas: emotion-motivation, sensory-motor, and language-cognitive. Despite these labels, the analysis is thoroughly behavioral; there are no hidden behaviors or processes. In all of these domains, Staats argues, maturation is a function of physical growth interacting with natural contingencies, which change as a child’s behavior changes. In Staats’ world view, there is no separate process of child development.

Staats rejects genetics (except for those that program for unconditioned reflexes) and epigenetics as the cause of any behavior. Much of the evidence supporting genetic and epigenetic accounts takes the form of documenting that behavioral disruption results when genetic mechanisms are perturbed. Missing from these accounts, however, is an explanation of how, in relevant disorders, changes in genes affect learning. Thus, the behavior analyst’s task is to identify how a defective gene disrupts learning. In Staats’ view, that knowledge combined with knowledge of the natural contingencies that support normal development allow a complete understanding and effective interventions to minimize or eliminate these so-called genetic or epigenetic disorders.

An example from medicine illustrates the general spirit of this approach and its benefits. Phenylketonuria is a genetic disorder that invariably kills young children with a particular defective gene. Investigators identified the defective gene, but did not stop there. They also found that the non-defective version of the gene produces enzymes necessary for metabolizing phenylalanine, an amino acid toxic to neurons at high doses. A diet with limited phenylalanine, supplemental amino acids, and other nutrients prevents phenylalanine from accumulating and killing young children (Macleod & Ney, 2010 ), even though the genetic defect remains.

Identifying the natural contingencies in development is an exciting research area for behavior analysts. The working hypothesis, of course, is that behavior putatively caused by natural selection can instead be understood by prior experiences. For example, many consider exploratory behaviors of infants to result from genetics, as this quote from Skinner (1948, reprinted 1975 ) might be taken to imply: “No one asks how to motivate a baby. A baby naturally explores everything it can get at….” (p. 144). Staats takes the view that exploratory behaviors, and by implication differences in exploratory behaviors, result from natural reinforcement, that is, changes in the environment produced by exploring as when a baby touches an object it may rattle. If natural selection is not responsible for individual differences in behavior, then it follows that these differences result from differences in learning experiences. This is not to say that there are no intraspecies differences in behavior potential. Humans, for instance, evolved genetic and brain mechanisms that are specific to language, but critically it is early experiences that result in language acquisition and language differences across individuals.

As too few behavior analysts have recognized (e.g., Bijou & Baer, 1961 ; Schlinger, 1995 ), only a detailed examination of early experiences can identify the role of environment in typical development, and by extension in atypical development. In the case of language, research suggests a clear role for early experience in language acquisition. For instance, the more children are exposed to verbal interactions, the greater their language competences’ (Hart & Risley, 1995 ; Moerk, 1990 ). This work has inspired a spate of programs to increase the number of words heard by young children with, or at risk for, language problems, with the goal of nudging language development toward a more normal developmental trajectory (e.g., Suskind & Suskind, 2015 ). It is not yet clear whether these programs adequately reproduce the natural contingencies identified in Moerk ( 1990 ) and Hart & Risley ( 1995 ), but the general approach is consistent with what Staats’ advocates: using natural contingencies as the inspiration for early intervention strategies for children who are falling behind developmental norms.

Crucial Concepts in Human Development

In explaining development, Staats assigns an important role to classical and operant conditioning, but he proposes that complex human behavior is best understood in terms of behavior repertoires and cumulative learning . These two processes, according to Staats, are unique to humans and, when combined with basic learning processes, account for all human behavior.

For Staats, behavior repertoires are complex sets of related stimulus-control relations. He gives the example of a reading repertoire that was built in a dyslexic child via 64,000 trials with a variety of stimulus-control relations involving letters, words, etc. (Staats & Butterfield, 1965 .). Staats identified a large number of these repertoires and their interrelations. Such a reading repertoire, combined with sensory-motor development, can promote a writing repertoire. The reading repertoire may combine with a repertoire for following spoken instructions to allow individuals to follow written instructions, or combined with a sensory-motor repertoire allowing individuals to write instructions. Individual behaviors can be part of several repertoires, and repertoires can be hierarchical, with bigger repertoires comprised, in part, of smaller repertoires. One important goal of behavioral research, in Staats’ view, is to identifying relations among different repertoires and how contingencies influence these repertoires and their interrelations.

Behavior repertoires result in cumulative learning. In mastery of a repertoire, behaviors learned later are acquired more quickly than previously learned behaviors. For example, children learning to print letters late in the alphabet only require one fourth the trials compared to learning to print the letter A . Additionally, mastering one repertoire can make it easier to master a subsequent repertoire. For example, a sound-imitation repertoire combined with suitable prompts produces a word-imitation repertoire that promotes faster language learning. While it may be uncontroversial among behavior analysts to claim that behavior consists of many repertoires and learning one repertoire facilitates learning others, there are few systematic research programs to identify these repertoires, their components, and the contingencies that produce them and establish and maintain their relation to other repertoires.

Staats speculates that cumulative learning influenced human cultural development. Cultural transmission of learning in effect allows one individual’s repertoire to build upon another’s. As one generation masters a repertoire the succeeding generation can master that repertoire faster and is able to expand that repertoire or beginning learning a repertoire new to the group. Staats gives the example of artistic repertoires becoming more sophisticated across generations. Unfortunately, Staats is somewhat vague on the specific mechanisms driving such changes, implying without sufficient explanation that the cumulative learning of a culture’s individual members somehow translates to intergenerational effects (Skinner, 1984 , was similarly vague in his account of cultural selection). Staats also places great emphasis on contingency-shaped behavior in his account of cultural development and, surprisingly, omits any function for rule-governed behavior.

From a behavior analytic perspective, a further limitation of Staats’ account is uncertainty regarding whether behavior repertoires and cumulative learning, as Staats invokes them, qualify as new concepts. By claiming that these phenomena are uniquely human Staats certainly suggests so, but nevertheless behavior analysts will find much that feels familiar in his use of them. For instance, Staats’ analysis of behavioral repertoires and their complex interrelationships brings to mind how reinforcers organize behavior into operants and how the resulting class of responses may not be identical to the class of reinforced responses (Catania, 2013 ). His description of cumulative learning may relate to learning sets (Harlow, 1949 ), pivotal response (Bryson, Koegel, Koegel, Openden, Smith, & Nefdt, 2007 ), and behavior cusps (Rosales-Ruiz & Baer, ( 1997 ), although Staats is silent on these possible connection. In the end, readers will be left to ponder important questions that are suggested by, but not answered in, The Marvelous Learning Animal , not the least of which concerns what sort of research program may be imagined to test Staats’ ideas.

Learning Human Nature

With the preceding as foundational knowledge, Staats addresses specific types of behavior that supposedly are explained by the Great Scientific Error. For example, intelligence tests subsume a variety of repertoires, such as naming, counting, instruction following, and imitating. Differences in intelligence test scores must therefore be interpreted as differences in acquisition of these behavioral repertoires, not differences in an internal entity called intelligence. Staats points out that intelligence test scores predict school performance not because they describe inherent ability but rather because many of the behavior repertoires required for success in school are assessed in intelligence tests. This leads naturally to the proposal for an analysis of the repertoires comprising what we call intelligent behavior, which would include research on the natural contingencies producing these repertoires and, eventually, attempts to foster development by systematically implementing those contingencies.

Behavior analysts will correctly anticipate that Staats proposes that abnormal experiences produce abnormal behaviors. His examples of problematic early childhood behaviors—including tantrums, yelling, hitting, defiance, and so forth—are familiar, as is his suggestion that how caregivers respond to these behaviors influences whether or not they continue and become more severe. These unfortunate natural contingencies produce behavioral repertories that may eventually qualify the individual for a “psychiatric” diagnosis, and once the diagnosis is in place, it elicits sympathy or fear that may only exacerbate caregiver acquiescence to problem behavior. Within the context of autism and a few other disorders, Staats’ recommendation for action is equally familiar. He prescribes clearly identifying the relevant behavior repertoires, analyzing the abnormal contingencies which produce those repertoires and exploring how these repertoires may, through cumulative learning, produce additional problem repertoires. A particular contribution of The Marvelous Learning Animal is to apply the same approach to understanding the development of dyslexia, paranoid schizophrenia, paraphilias, depression, and other problems less frequently addressed by applied behavior analysts. Staats holds steadfastly to his environmental perspective even in cases where biological damage or genetic abnormalities typically are held to cause the disorder (e.g., Down’s syndrome).

Human Evolution and Marvelous Learning

There is much more in Staats’ analysis that is worthy of consideration by behavior analysts, including his assertion that cumulative learning has been an important influence in human natural selection. As Staats notes, those in the field of human evolution are beginning to reach a similar conclusion (Diamond, 1992 ; Gould, 1977 ; Jablonka & Lamb, 2005 ), although Staats’ account is interesting for the emphasis it places on selection for verbal abilities and how verbal abilities influence selection. Critical thinking is required to examine ways in which the account deviates from those of behavior analysts (see Skinner, 1984 , in reinforcement as a mechanism of natural selection) and evolutionary biologists. In the latter case, Staats’ hardest-to-swallow view, namely that natural selection provides all humans with equal learning abilities because variation in learning ability is selected out. This notion is at odds with the widely accepted notion that natural selection is possible only when populations contain variability (Dawkins, 1976 ).

A Human Paradigm

It is refreshing to see an environment-centric alternative to the Great Scientific Error, and behavior analysts will appreciate Staats’ panache in placing learning at the center of all explanations of human behavior. They also will be interested in his conclusion that radical changes are required in the basic science of human behavior and the application of that science to clinical practice. In Staats’ view, the revised science needs to know much more about how learning and biology combine to produce behavior, which implies relying on techniques (e.g., brain imaging technology, genetic assays) to understand the interrelatedness of learning and biology. Many behavior analysts will sympathize with Staats’ proposition that the field of child development needs to be almost entirely restarted, using sophisticated observational methods required to identify the natural contingencies in development. Perhaps less intuitive, and therefore more challenging, to behavior analysts is Staats’ implication that, ultimately, the study of human behavior can only proceed with a proper study of development as he defines it. For example, an infant lies on their stomach pushes up with their arms which raises their head allowing them to see objects hidden behind other objects. If seeing a new view is reinforcing, or seeing objects previously followed by reinforcers is reinforcing, then infants will continue to push up. As they raise their head further above the surface, more items come into view. Eventually the standing infant may lean toward a favored object. They move a foot, preventing themselves from falling, bringing them closer to a reinforcing object. The first proto step has been naturally reinforced. Although, non-behavior analysts have collected data supporting aspects of this analysis, they did not include the functions of behaviors as walking developed (Adolph, Cole, Komati, Garciagurre, Badaly, Lingemanm, Chan, & Sotsky, 2012 ).

A central irony of behavior analysis is that its adherents (beginning with Skinner, e.g., 1953 ) have maintained that complex environmental relations account for the diversity of human behaviors, while their own work carefully analyzed only a limited range of interesting behaviors. The Marvelous Learning Animal challenges behavior analysts (and other readers) to imagine what a behavior science would look like if it thoroughly examined all of those interesting behaviors. In this regard, it matters little if along the way Staats commits a variety of transgressions such as failing to fully explain every concept, possibly playing fast and loose with natural selection, relying on lay terms that carry mentalistic connotations (this is, after all, a popular press book), and occasionally speaking ill of radical behaviorism.

These details should not be allowed to distract from the book’s essential challenge, which is to ask those who would advance environmental experience as the primary engine of behavior development to develop the science that is needed to test and support such an account. Staats delivers an analysis of complex human behavior that is indisputably behavioral and often consistent with a radical behavioral view. Where the analysis diverges from radical behaviorism as it has traditionally been practiced, it most often offers expansion rather than contradiction and thereby provides a stimulating basis for further inquiry.

Acknowledgments

I thank Bob Allen for his helpful comments on an earlier version of this review. All the remaining shortcomings result from my behavior.

Compliance with Ethical Standards

The preparation of this manuscript was not funded by any organization. I have no ethical conflicts in preparing this manuscript.

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Major Perspectives in Modern Psychology

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 / Emily Roberts

• Psychodynamic Perspective
• Behavioral Perspective
• Cognitive Perspective
• Biological Perspective
• Cross-Cultural Perspective
• Evolutionary Perspective
• Humanistic Perspective

Psychological perspectives are different ways of thinking about and explaining human behavior. Psychologists utilize a variety of perspectives when studying how people think, feel, and behave.

Some researchers focus more on one specific school of thought, such as the biological perspective, while others take a more eclectic approach that incorporates multiple points of view.

No single perspective is "better" than another. Instead, each simply emphasizes different aspects of human behavior.

This article explores seven of the major perspectives in psychology, where these perspectives originated, and how they attempt to explain psychological issues. It also provides examples of key ideas from each psychological perspective.

Major Perspectives

The early years of psychology were dominated by a succession of these different schools of thought. If you have taken a psychology course, you might remember learning about structuralism, functionalism , psychoanalysis, behaviorism, and humanism—all of which are different schools of psychological thought.

As psychology has grown, the number and variety of topics psychologists investigate have also expanded. Since the early 1960s, the field of psychology has flourished. It continues to grow rapidly, as has the depth and breadth of subjects studied by psychologists.

Psychological Perspectives Today

Few psychologists identify their outlook according to a particular school of thought. While there are still some pure behaviorists or psychoanalysts, the majority of psychologists today categorize their work according to their specialty area and perspective.

Purpose of Psychological Perspectives

Why are there so many different perspectives in psychology? It is important to remember that every topic in psychology can be looked at in many ways. For example, let's consider the subject of aggression.

• A professional who emphasizes a biological perspective would look at how the brain and nervous system impact aggressive behavior.
• A professional who stresses a behavioral perspective would look at how environmental variables reinforce aggressive actions.
• A professional who utilizes a cross-cultural approach might consider how cultural and social influences contribute to aggressive or violent behavior.

Here are seven of the major perspectives in modern psychology .

1. The Psychodynamic Perspective

The psychodynamic perspective originated with the work of Sigmund Freud . This view of psychology and human behavior emphasizes the role of the unconscious mind , early childhood experiences, and interpersonal relationships to explain human behavior, as well as to treat mental illnesses.

Much thanks to Freud's work and influence, psychoanalysis became one of the earliest major forces within psychology. Freud conceived of the mind as being composed of three key elements: the id, the ego, and the superego .

• The id is the part of the psyche that includes all the primal and unconscious desires.
• The ego is the aspect of the psyche that must deal with the demands of the real world.
• The superego is the last part of the psyche to develop and is tasked with managing all of our internalized morals, standards, and ideals.

While the psychodynamic perspective is not as dominant today, it continues to be a useful psychotherapeutic tool.  

2. The Behavioral Perspective

Behavioral psychology focuses on learned behaviors. It was founded on the work of psychologists such as Edward Thorndike and John B. Watson.   Behaviorism dominated psychology in the early twentieth century but began to lose its hold during the 1950s.

Behaviorism differs from other perspectives because it focuses solely on observable behaviors rather than on emphasizing internal states.

Today, the behavioral perspective is still concerned with how behaviors are learned and reinforced. Behavioral principles are often applied in mental health settings, where therapists and counselors use these techniques to explain and treat a variety of illnesses.

3. The Cognitive Perspective

During the 1960s, a new perspective known as cognitive psychology emerged. This area of psychology focuses on mental processes like memory, thinking, problem-solving, language, and decision-making.  

Influenced by psychologists such as Jean Piaget and Albert Bandura , the cognitive perspective has grown tremendously in recent decades.

Cognitive psychologists often utilize an information-processing model (comparing the human mind to a computer) to conceptualize how information is acquired, processed, stored, and utilized.

4. The Biological Perspective

The study of physiology played a major role in the development of psychology as a separate science. Today, the perspective is known as biological psychology (also called biopsychology or physiological psychology). The point of view emphasizes the physical and biological bases of behavior.

Researchers with a biological perspective on psychology might look at how genetics influence behavior or how damage to specific areas of the brain affect personality.

The nervous system, genetics, the brain, the immune system, and the endocrine system are just a few subjects of interest to biological psychologists. Over the last few decades, the perspective has grown significantly with advances in our ability to explore and understand the human brain and nervous system.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) scans give researchers tools to observe the brain under a variety of conditions. Scientists can now look at the effects of brain damage, drugs, and disease in ways that were not possible in the past.

5. The Cross-Cultural Perspective

Cross-cultural psychology is a fairly new perspective that has grown significantly in the last twenty years. Psychologists and researchers in this school of thought look at human behavior across different cultures.

By looking at these differences, we can learn more about how culture influences our thinking and behavior.   For example, researchers have looked at how social behaviors differ in individualistic and collectivistic cultures .

• In individualistic cultures (such as the United States) people tend to exert less effort when they are part of a group—a phenomenon known as social loafing .
• In collectivistic cultures (such as China), people tend to work harder when they are part of a group.

6. The Evolutionary Perspective

Evolutionary psychology focuses on the study of how the theory of evolution can explain physiological processes.   Psychologists who take this perspective apply the basic principles of evolution (like natural selection) to psychological phenomena.

The evolutionary perspective suggests that these mental processes exist because they serve an evolutionary purpose—meaning that they aid in human survival and reproduction.​​​

7. The Humanistic Perspective

In the 1950s, a school of thought known as humanistic psychology arrived. It was greatly influenced by the work of prominent humanists such as Carl Rogers and Abraham Maslow .

The humanistic perspective emphasizes the role of motivation in thought and behavior. Concepts such as self-actualization are essential. Psychologists with a humanist perspective focus on what drives humans to grow, change, and develop their personal potential.

Positive psychology (which focuses on helping people live happier, healthier lives) is a recent movement in psychology with roots in the humanist perspective.  

A Word From Verywell

There are many ways to think about human thought and behavior. The different perspectives in modern psychology give researchers and students tools to approach problems and answer questions. They also guide psychologists in finding new ways to explain and predict human behavior. This exploration and deeper understanding can even lead to the development of new treatment approaches.

Fonagy P. The effectiveness of psychodynamic psychotherapies: An update .  World Psychiatry . 2015;14(2):137–150. doi:10.1002/wps.20235

Malone JC. Did John B. Watson really "found" behaviorism? .  Behav Anal . 2014;37(1):1–12. doi:10.1007/s40614-014-0004-3

Glenberg AM, Witt JK, Metcalfe, J. From the revolution to embodiment: 25 years of cognitive psychology . Perspectives on Psychological Science . 2013;8(5):573-585. doi:10.1177/1745691613498098

American Psychological Association. Biological psychology . 

Lonner WJ. Half a century of cross-cultural psychology: a grateful coda . Am Psychol . 2015;70(8):804-14. doi: 10.1037/a0039454

Cosmides L, Tooby, J. Evolutionary psychology: a new perspective on cognition and motivation . Annu Rev Psychol . 2013;64:201-229. doi:10.1146/annurev.psych.121208.131628

Waterman AS. The humanistic psychology-positive psychology divide: contrasts in philosophical foundations . Am Psychol . 2013;68(3):124-33. doi:10.1037/a0032168

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

OPINION article

Challenges and opportunities for human behavior research in the coronavirus disease (covid-19) pandemic.

• 1 Department of General Psychology, University of Padova, Padua, Italy
• 2 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

The COVID-19 pandemic is a serious public health crisis that is causing major worldwide disruption. So far, the most widely deployed interventions have been non-pharmacological (NPI), such as various forms of social distancing, pervasive use of personal protective equipment (PPE), such as facemasks, shields, or gloves, and hand washing and disinfection of fomites. These measures will very likely continue to be mandated in the medium or even long term until an effective treatment or vaccine is found ( Leung et al., 2020 ). Even beyond that time frame, many of these public health recommendations will have become part of individual lifestyles and hence continue to be observed. Moreover, it is implausible that the disruption caused by COVID-19 will dissipate soon. Analysis of transmission dynamics suggests that the disease could persist into 2025, with prolonged or intermittent social distancing in place until 2022 ( Kissler et al., 2020 ).

Human behavior research will be profoundly impacted beyond the stagnation resulting from the closure of laboratories during government-mandated lockdowns. In this viewpoint article, we argue that disruption provides an important opportunity for accelerating structural reforms already underway to reduce waste in planning, conducting, and reporting research ( Cristea and Naudet, 2019 ). We discuss three aspects relevant to human behavior research: (1) unavoidable, extensive changes in data collection and ensuing untoward consequences; (2) the possibility of shifting research priorities to aspects relevant to the pandemic; (3) recommendations to enhance adaptation to the disruption caused by the pandemic.

Data collection is very unlikely to return to the “old” normal for the foreseeable future. For example, neuroimaging studies usually involve placing participants in the confined space of a magnetic resonance imaging scanner. Studies measuring stress hormones, electroencephalography, or psychophysiology also involve close contact to collect saliva and blood samples or to place electrodes. Behavioral studies often involve interaction with persons who administer tasks or require that various surfaces and materials be touched. One immediate solution would be conducting “socially distant” experiments, for instance, by keeping a safe distance and making participants and research personnel wear PPE. Though data collection in this way would resemble pre-COVID times, it would come with a range of unintended consequences ( Table 1 ). First, it would significantly augment costs in terms of resources, training of personnel, and time spent preparing experiments. For laboratories or researchers with scarce resources, these costs could amount to a drastic reduction in the experiments performed, with an ensuing decrease in publication output, which might further affect the capacity to attract new funding and retain researchers. Secondly, even with the use of PPE, some participants might be reluctant or anxious to expose themselves to close and unnecessary physical interaction. Participants with particular vulnerabilities, like neuroticism, social anxiety, or obsessive-compulsive traits, might find the trade-off between risks, and gains unacceptable. Thirdly, some research topics (e.g., face processing, imitation, emotional expression, dyadic interaction) or study populations (e.g., autistic spectrum, social anxiety, obsessive-compulsive) would become difficult to study with the current experimental paradigms ( Table 1 ). New paradigms can be developed, but they will need to first be assessed for reliability and validated, which will undoubtedly take time. Finally, generalized use of PPE by participants and personnel could alter the “usual” experimental setting, introducing additional biases, similarly to the experimenter effect ( Rosenthal, 1976 ).

Table 1 . Possible consequences of non-pharmacological interventions for COVID-19 on human behavior research.

Data collection could also adapt by leveraging technology, such as running experiments remotely via available platforms, like for instance Amazon's Mechanical Turk (MTurk), where any task that programmable with standard browser technology can be used ( Crump et al., 2013 ). Templates of already-programmed and easily customizable experimental tasks, such as the Stroop or Balloon Analog Risk Task, are also available on platforms like Pavlovia. Ecological momentary assessment is another feasible option, since it was conceived from the beginning for remote use, with participants logging in to fill in scales or activity journals in a naturalistic environment ( Shiffman et al., 2008 ). Increasingly affordable wearables can be used for collecting physiological data ( Javelot et al., 2014 ). Web-based research was already expanding before the pandemic, and the quality of the data collected in this way is comparable with that of laboratory studies ( Germine et al., 2012 ). Still, there are lingering issues. For instance, for some MTurk experiments, disparities have been evidenced between laboratory and online data collection ( Crump et al., 2013 ). Further clarifications about quality, such as consistency or interpretability ( Abdolkhani et al., 2020 ), are also needed for data collected using wearables.

Beyond updating data collection practices, a significant portion of human behavior research might change course to focus on the effects of the pandemic. For example, the incidence of mental disorders or of negative effects on psychological and physical well-being, particularly across populations of interest (e.g., recovered patients, caregivers, and healthcare workers), are crucial areas of inquiry. Many researchers might feel hard-pressed to not miss out on studying this critical period and embark on hastily planned and conducted studies. Multiplication and fragmentation of efforts are likely, for instance, by conducting highly overlapping surveys in widely accessible and oversampled populations (e.g., university students). Moreover, rushed planning is bound to lead to taking shortcuts and cutting corners in study design and conduct, e.g., skipping pre-registration or even ethical committee approval or using not validated measurement tools, like ad hoc surveys. Surveys using non-probability and convenience samples, especially for social and mental health problems, frequently produce biased and misleading findings, particularly for estimates of prevalence ( Pierce et al., 2020 ). A significant portion of human behavior research that re-oriented itself to study the pandemic could result in to a heap of non-reproducible, unreliable, or overlapping findings.

Human behavior studies could also aim to inform the planning and enforcement of public health responses in the pandemic. Behavioral scientists might focus on finding and testing ways to increase adherence to NPIs or to lessen the negative effects of isolation, particularly in vulnerable groups, e.g., the elderly or the chronically ill and their caretakers. Studies could also attempt to elucidate factors that make individuals uncollaborative with recommendations from public health authorities. Though all of these topics are important, important caveats must be considered. Psychology and neuroscience have been affected by a crisis in reproducibility and credibility, with several established findings proving unreliable and even non-reproducible ( Button et al., 2013 ; Open Science Collaboration, 2015 ). It is crucial to ensure that only robust and reproducible results are applied or even proposed in the context of a serious public health crisis. For instance, the possible influence of psychological factors on susceptibility to infection and potential psychological interventions to address them could be interesting topics. However, the existing literature is marked by inconsistency, heterogeneity, reverse causality, or other biases ( Falagas et al., 2010 ). Even for robust and reproducible findings, translation is doubtful, particularly when these are based on convenience samples or on simplified and largely artificial experimental contexts. For example, the scarcity of medical resources (e.g., N-95 masks, drugs, or ventilators) in a pandemic with its unavoidable ethical conundrum about allocation principles and triage might appeal to moral reasoning researchers. Even assuming, implausibly, that most of the existent research in this area is robust, translation to dramatic real-life situations and highly specialized contexts, such as intensive care, would be difficult and error-prone. Translation might not even be useful, given that comprehensive ethical guidance and decision rules to support medical professionals already exist ( Emanuel et al., 2020 ).

The COVID-19 pandemic and the corresponding global public health response pose significant and lasting difficulties for human behavior research. In many contexts, such as laboratories with limited resources and uncertain funding, challenges will lead to a reduced research output, which might have further domino effects on securing funding and retaining researchers. As a remedy, modifying data collection practices is useful but insufficient. Conversely, adaptation might require the implementation of radical changes—producing less research but of higher quality and more utility ( Cristea and Naudet, 2019 ). To this purpose, we advocate for the acceleration and generalization of proposed structural reforms (i.e., “open science”) in how research is planned, conducted, and reported ( Munafò et al., 2017 ; Cristea and Naudet, 2019 ) and summarize six key recommendations.

First, a definitive move from atomized and fragmented experimental research to large-scale collaboration should be encouraged through incentives from funders and academic institutions alike. In the current status quo, interdisciplinary research has systematically lower odds of being funded ( Bromham et al., 2016 ). Conversely, funders could favor top-down funding on topics of prominent interest and encourage large consortia with international representativity and interdisciplinarity over bottom-up funding for a select number of excellent individual investigators. Second, particularly for research focused on the pandemic, relevant priorities need to be identified before conducting studies. This can be achieved through assessing the concrete needs of the populations targeted (e.g., healthcare workers, families of victims, individuals suffering from isolation, disabilities, pre-existing physical and mental health issues, and the economically vulnerable) and subsequently conducting systematic reviews so as to avoid fragmentation and overlap. To this purpose, journals could require that some reports of primary research also include rapid reviews ( Tricco et al., 2015 ), a simplified form of systematic reviews. For instance, The Lancet journals require a “Research in context” box, which needs to be based on a systematic search. Study formats like Registered Reports, in which a study is accepted in principle after peer review of its rationale and methods ( Hardwicke and Ioannidis, 2018 ), are uniquely suited for this change. Third, methodological rigor and reproducibility in design, conduct, analysis, and reporting should move to the forefront of the human behavior research agenda ( Cristea and Naudet, 2019 ). For example, preregistration of studies ( Nosek et al., 2019 ) in a public repository should be widely employed to support transparent reporting. Registered reports ( Hardwicke and Ioannidis, 2018 ) and study protocols are formats that ensure rigorous evaluation of the experimental design and statistical analysis plan before commencing data collection, thus making sure shortcuts and methodological shortcomings are eliminated. Fourth, data and code sharing, along with the use of publicly available datasets (e.g., 1000 Functional Connectomes Project, Human Connectome Project), should become the norm. These practices allow the use of already-collected data to be maximized, including in terms of assessing reproducibility, conducting re-analyses using different methods, and exploring new hypotheses on large collections of data ( Cristea and Naudet, 2019 ). Fifth, to reduce publication bias, submission of all unpublished studies, the so-called “file drawer,” should be encouraged and supported. Reporting findings in preprints can aid this desideratum, but stronger incentives are necessary to ensure that preprints also transparently and completely report conducted research. The Preprint Review at eLife ( Elife, 2020 ), in which the journal effectively takes into review manuscripts posted on the preprint server BioRxiv, is a promising initiative in this direction. Journals could also create study formats specifically designed for publishing studies that resulted in inconclusive findings, even when caused by procedural issues, e.g., unclear manipulation checks, insufficient stimulus presentation times, or other technical errors. This would both aid transparency and help other researchers better prepare their own experiments. Sixth, peer review of both articles and preprints should be regarded as on par with the production of new research. Platforms like Publons help track reviewing activity, which could be rewarded by funders and academic institutions involved in hiring, promotion, or tenure ( Moher et al., 2018 ). Researchers who manage to publish less during the pandemic could still be compensated for the onerous activity of peer review, to the benefit of the entire community.

Of course, individual researchers cannot implement such sweeping changes on their own, without decisive action from policymakers like funding bodies, academic institutions, and journals. For instance, decisions related to hiring, promotion, or tenure of academics could reward several of the behaviors described, such as complete and transparent publication regardless of the results, availability of data and code, or contributions to peer review ( Moher et al., 2018 ). Academic institutions and funders should acknowledge the slowdown of experimental research during the pandemic and hence accelerate the move toward more “responsible indicators” that would incentivize best publication practices over productivity and citations ( Moher et al., 2018 ). Funders could encourage submissions leveraging existing datasets or developing tools for data re-use, e.g., to track multiple uses of the same dataset. Journals could stimulate data sharing by assigning priority to manuscripts sharing or re-using data and code, like re-analyses, or individual participant data meta-analyses.

Author Contributions

CG and IC contributed equally to this manuscript in terms of its conceivement and preparation. All authors contributed to the article and approved the submitted version.

Conflict of Interest

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

Acknowledgments

This work was carried out within the scope of the project “use-inspired basic research”, for which the Department of General Psychology of the University of Padova has been recognized as “Dipartimento di eccellenza” by the Ministry of University and Research.

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Keywords: open science, data sharing, social distancing, preprint, preregistration, coronavirus disease, neuroimaging, experimental psychology

Citation: Gentili C and Cristea IA (2020) Challenges and Opportunities for Human Behavior Research in the Coronavirus Disease (COVID-19) Pandemic. Front. Psychol. 11:1786. doi: 10.3389/fpsyg.2020.01786

Received: 29 April 2020; Accepted: 29 June 2020; Published: 10 July 2020.

Reviewed by:

Copyright © 2020 Gentili and Cristea. 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: Claudio Gentili, c.gentili@unipd.it

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.

126 Human Behavior Essay Topic Ideas & Examples

🏆 best human behavior topic ideas & essay examples, ⭐ simple & easy human behavior essay titles, 👍 good essay topics on human behavior, 🔎 most interesting human behavior topics to write about, ❓ questions about human behavior.

• Human Behavior Effects on the Environment However, while some people are doing all they can to protect the environment, some are participating in activities that cause harm to the environment.
• Human Behavior and Psychology in “The Good Will Hunting” by Gus Van Sant The second important person with him is his best friend Chukie, who he tells that he would love to be a laborer for the rest of his life.
• The Influence of Nature and Nurture on Human Behavior This particular research challenged the views that were in support of nature as the sole determinant of human beings’ behavior and argued that nurture was a major contributing factor to ways in which human beings […]
• Effects of Computer Programming and Technology on Human Behavior Phones transitioned from the basic feature phones people used to own for the sole purpose of calling and texting, to smart phones that have amazing capabilities and have adapted the concepts of computers.
• Motivation and Human Behavior Internal motivation is the opposite, as it is not connected to the external conditions and is interlinked with the unique nature of the action and wants itself.
• Classical Conditioning as an Explanation of Human Behavior The main strategy used by advertisers is to associate their product and services with stimuli that evoke pleasurable feeling in general to the extent of trying to create a more specific association.
• The History Development of Psychology: The Understanding of Human Behavior The aim of the paper is to identify the reasons that have shaped and led to the development of the history of psychology.
• Sina’s Story: Multidimensional Approach to Understanding of Human Behavior An ideal case to analyze using multidimensional approach is the story of Sina, a woman who survived through the changing conditions of the time and the environment owing to her excellent personal characteristics.
• What Is Personality, and Is It Predictive Of Human Behavior? Personality, according to Harre & Lamb, is the entirety of feature and traits, as of manners or qualities that are particular per person.
• Internet Technology and Impact on Human Behavior It was the Internet that allowed the phenomenon of cyberbullying to emerge, the essence of which is the harassment of someone on the Internet by large groups of users.
• Non-Verbal Communication and Human Behavior It is also noteworthy to mention that people tend to avoid touching each other when maneuvering in the crowd. The presence of a friendly person also appears to make the other individual more prone to […]
• Sociology as a Way to Understanding Human Behavior and Society The examination of the individuals influenced by groups is the study of sociology whereas its main goal is to understand human behavior in the context of society and, after succeeding in this, trying to generalize […]
• Human Behavior: How Five General Perspectives Affect Marriage Social and cultural aspects also contribute to behavior of a person which is important in success of love marriage relationships. This is important to people in love as they can take time to observe and […]
• Empathy and Its Impact on Human Behavior In “The Baby in the Well” and “The Bad Things We Do Because of Empathy,” authors Paul Bloom and Fritz Breithaupt offer divergent perspectives on empathy and its impact on human behavior.
• The Role of Emotion in Understanding Human Behavior The situation is complicated by the findings in the evolutionary psychology field, which show that the ultimate aim of both emotions and cognition processes are very similar and are evolutionary-based.
• Human Behavior: Theoretical Approaches In certain regions of the world, various cultures, such as Islamic Shariah law in Pakistan, permit the relatives of a murder victim to commute the sentence of a killer in the event of an honor […]
• Socialization and the Life Course: Human Behavior and Sociology This is a rather hyperbolized statement; however, it may be seen as a reference to how people are integrated into society and how it may form them as individuals.
• Environmentalism and Human Behavior: A Literature Review In particular, Dietz, whose scholarly interest lies in the field of human ecology and environmental policy, traced a history of environmentalism in his article and emphasized the importance of integrating social science in environmental research.
• Robbins’ “Contemporary Human Behavior Theory”: Overview At the beginning of the 20th century, a new idea has emerged that contradicted the scientific method and denied the objectivity or reality.
• “Contemporary Human Behavior Theory” by Robbins In the United States, all the cultural studies are based on the values of the researchers rather than on the norms of studied culture.
• Implications of Theological and Psychological Reflections on Human Behavior The Bible talks about the works of the human flesh which are evident in the commission of sins and also warns about the dire consequences of not inheriting the kingdom of God.
• Literature: Relationships and Human Behavior The story of the narrator from “The Lone Ranger and Tonto Fistfight in Heaven” demonstrates the absence of one’s connection to his parents. This example adds to the role of relationships in one’s behavior and […]
• Gender as a Performance. Human Behavior Theory Thus, to be human is to accept the “unknowingness about the Other in the face of the Other that undoes us”. One such misconception is the innateness of gender and its immutability.
• Research With Animals Which Gives Information About Human Behavior However, to support the conclusions that parallels in human and animal conduct does exist, it is important to make a few assumptions about similarity between humans and animals.
• Influence of Heredity and Hormones on Human Behavior There are a lot of factors which influence the way human behavior develops, Some of this factors include hormones and heredity.
• Brain Injury: Cognitive Models of Human Behavior For motor functions, sight, and hearing, the left side of the brain controls the right side of the body, and the right side of the brain controls the left side of the body.
• Human Behavior in Social Systems and Environment The systems view was based on the ideas such as the relationship among the elements in the society like if the study was based on people then they had a study on how the people […]
• Conjunction Fallacies in Human Behavior Analysis But it may be that the conflict person is the other team member the opposite party to the conflict. As it may be seen of the hypothesis, the conjunction fallacies in human behavior appear because […]
• Human Behavior in Fire: Petersburg Hospital There is a large car park provided to take care of the staff and visitors’ needs both on the front and exterior location of the building.
• Technology Changing Human Behavior: Theory & Practice At the same time, it is important to remember that operant conditioning can be used to make the desired behavior a norm.
• Streamlining Human Behavior and Perception They aim to explain the mathematics behind coincidences and the influence of processes in the human brain on our perception of coincidences.
• Ethical Absolutism and Human Behavior This essay seeks to highlight Stance’s argument that absolutism has and still is the backbone that provides the standard used to measure human behavior.
• Disaster Reaction in Human Behavior And despite the differences in the origins of diverse disasters, they have the common features of abruptness, a serious threat to health and welfare of individuals and communities, interference with a regular mode of life, […]
• Romantic Relationship: Human Behavior Perspectives The cognitive perspective is related to the biological/evolutionally perspective in terms of underlining the role of nature-nurture interactions in explaining behavior; however, it is different from learning and sociocultural perspectives as the latter underscore the […]
• Social Influences on Human Behavior Failure to notify the police or other authorities in the vicinity contributed to excessive prolonging of the rape, psychological and physical torture of the victim.
• Observing Human Behavior in an Organization The meeting was about planning for a project to upgrade the information system in the organization, and the manager and the CEO of the organization was present along with 12 members of the team handling […]
• Human Behavior Prediction It is important to understand that individuals may be tempted to act in a particular manner following their free choices; however, they have to restrain themselves, therefore acting according to the expectations of the society.
• Human Behavior Change in the Course of a Lifetime This issue is important as the knowledge of the reasons of some kinds of people’s behavior provides individuals with the opportunity to reduce the adverse impacts and become more independent in the decision-making and actions.
• Social Issues of Human Behavior: Nature and Nurture On the other hand, the nurture view asserts that behaviors are developed and persist according to the upbringing and the environment the individual grows up in.
• Understanding Human Behavior and the Social Environment Besides, the impact that cancer has on the development of a person in this stage and the realization of goals in life is devastating.
• Human Behavior and the Best Principles to Follow In his words, the cause and effect of everything in the world are so entangled that differentiation between the two is almost impossible.
• Human Behavior during Evacuations According to Fahy and Proulx, “the phases of disaster response will vary significantly depending on the targeted individuals, the nature of structure, and the aspects of the situation”.
• Contemporary Mathematical Model of Human Behavior Under Some Environmental Constraints Such a situation was seen in the Kozma, Harter & Achunala study wherein their model of human performance was able to show both the inherent adaptability of human performance in light of increasingly difficult tasks […]
• Human Behavior Effect on the Results of Organization’s Projects An understanding of human behavior is important in the interaction of members of a team to a project and the outcome of a project in general.
• Climate Change Needs Human Behavior Change The thesis of this essay is that human behavior change, including in diet and food production, must be undertaken to minimize climate change, and resulting misery.
• Morality and Ethics: Religion Effect on Human Behavior The second objective is to articulate the effect of religions on the economy and the political establishments of a society. The existence of a lot of information on the impact of religion on society made […]
• Organization Culture and Human Behavior In order for a leader to ensure that the culture of an organization is embraced by all the stakeholders involved in project, the leader should make sure that all the team members share a common […]
• Organizational Behavior: Human Behavior at Work In Malka’s response tries to elaborate further on the private companies and the domains of health care that are involved as well as the consequences of the private companies.
• The Implications of Technology on Human Behavior As such it can be said that the use of technology creates cognitive and behavioral changes which in effect changes the way people perceive and interact behaviorally and socially due to the amount of time […]
• Particulars of Human Behavior As there is a limited and hard to get to amount of material objects, moral satisfactions and other acquisitions, people’s competition becomes more aggressive and in the end, violent.
• Human Behavior in Companies: When the Organizational Behavior Leaves Much to Be Desired The choices that the Lincoln electrics makes in its leadership strategies, however, also make it clear that the company managerial makes efficient use of the Theory Y, which claims that people have a “natural desire […]
• Full Moon Effect on Human Behavior From another perspective the full moon and the increase in violence are just a coincidence such that the moon happens to be present when people behave strangely but that’s not its intended purpose because the […]
• Dimensions of Human Behavior In this theory, an individual has a single identity, which is assumed by people of the same gender, and with similar roles as the individual in the society.
• The Study of Human Behavior and Stress Article four In the article, “The Effects of Stress on Mental Health” by Paul Hata, the mental effects that stress can manifest in a person are seen to be the major underpinning for the article.
• Animal Studies Resurgence and Its Effects on Human Behavior
• Abnormal Behavior and Human Behavior
• Culture Regulates Human Behavior and Identity
• Comparing and Evaluating the Ways in Which Literature Help to Understand Human Behavior
• Cell Phones and Its Effect on Human Behavior
• Cognitive Ability and Human Behavior in Experimental Ultimatum Games
• Electronic Music and Its Effect on Human Behavior
• Dorothy Parker Exposes the Darker Side of Human Behavior
• Deception and Its Effects on Human Behavior and Mental
• Biological Factors That Affect Human Behavior
• Describing the type of human behavior problems
• Applying Human Behavior Theory of Everyday Situations and Cases
• Behavioral Geography and Its Impact on Human Behavior
• Researching Challenges and Opportunities for Human Behavior in the Coronavirus Disease (COVID-19) Pandemic
• Choosing the Right Pond: Human Behavior and the Quest for Status
• Accounting for Human Behavior, Local Conditions and Organizational Constraints in Humanitarian Development Models
• Drugs, Society and Human Behavior by Ray and Ksir
• Analyzing Human Behavior Through Advertising
• Adolescent Behavior and Its Effects on Human Behavior
• Color and Its Effect on Human Behavior
• How Does Music Influence Sex and Human Behavior
• Hamlet and Shakespeare’s Perceptions of Human Behavior
• Frankenstein and RUR: Depiction Human Behavior
• Human Behavior and Sexual Desire
• Explaining How One Hormone Influences Human Behavior
• Ergonomics and Its Effect on Human Behavior
• Gorillas, Lemurs and Human Behavior
• Evolutionary Theory and Its Relation to Human Behavior
• How Has Film Influenced Lifestyles and Human Behavior in the 20th Century
• Historical Context Versus Human Behavior in “The Scarlet Letter”
• Human Behavior and the Effects of the Full Moon
• Gender Specificity and Human Behavior
• How Climate Change Influences Human Behavior
• How Stereotypes May Arise and Affect Human Behavior
• Human Behavior and Its Relations With Knowledge
• Ethnography About Human Behavior and Economics
• Eugenics and Its Impact on Human Behavior
• How Does Color Affect Human Behavior
• General Strain Theory and Its Effect on Human Behavior
• Exploring the Affect Society Has on the Shaping of Human Behavior
• What Are the Five Types of Human Behaviour?
• What Are Human Behavior and Examples?
• What Is the Importance of Human Behavior?
• What Is Good Human Behavior?
• What Are the Characteristics of Human Behaviour?
• How Does Human Behavior Develop?
• What Is Human Behaviour in Psychology?
• What Is Human Behavior in Sociology?
• What Affects Human Behaviour?
• How Does Media Affect Human Behaviour?
• How Does Climate Change Influences Human Behavior?
• How Does Authority Influence Human Behavior?
• How Does Color Affect Human Behavior?
• How Does Genetics Influence Human Behavior?
• How Does Music Influence Human Behavior?
• How Do Nature and Nurture Affect Human Behavior?
• How Does Oxytocin Affect Human Behavior?
• How Does Society Influence Individual Human Behavior?
• How Has Film Influenced Lifestyles and Human Behavior in the 20th Century?
• How Is Hardwired Human Behavior?
• How Does Human Behavior Change in Different Social Situations?
• How Human Behavior May Influence Health and Disease?
• How Psychodynamic Therapy Works and Its Manifestations on Human Behavior?
• How Do Psychologists Explain Human Behavior?
• How Has Psychology Changed Human Behavior?
• How Many Stereotypes Arise and Affect Human Behavior?
• How Does the Human Mind Operates and Controls Human Behavior?
• How Are Two Impulsivity Measures Used for Human Behavior?
• Why Can Sociologists Not Rely on Common Sense to Explain Human Behavior?
• Personal Values Ideas
• Organizational Behavior Research Topics
• Personality Psychology Research Topics
• Relationship Research Ideas
• Human Development Research Ideas
• Personal Identity Paper Topics
• Psychology Questions
• Research and Development Essay Topics
• Chicago (A-D)
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7 Famous Psychology Experiments

Many famous experiments studying human behavior have impacted our fundamental understanding of psychology. Though some could not be repeated today due to breaches in ethical boundaries, that does not diminish the significance of those psychological studies. Some of these important findings include a greater awareness of depression and its symptoms, how people learn behaviors through the process of association and how individuals conform to a group.

Below, we take a look at seven famous psychological experiments that greatly influenced the field of psychology and our understanding of human behavior.

The Little Albert Experiment, 1920

A John’s Hopkins University professor, Dr. John B. Watson, and a graduate student wanted to test a learning process called classical conditioning. Classical conditioning involves learning involuntary or automatic behaviors by association, and Dr. Watson thought it formed the bedrock of human psychology.

A nine-month-old toddler, dubbed “Albert B,” was volunteered for Dr. Watson and Rosalie Rayner ‘s experiment. Albert played with white furry objects, and at first, the toddler displayed joy and affection. Over time, as he played with the objects, Dr. Watson would make a loud noise behind the child’s head to frighten him. After numerous trials, Albert was conditioned to be afraid when he saw white furry objects.

The study proved that humans could be conditioned to enjoy or fear something, which many psychologists believe could explain why people have irrational fears and how they may have developed early in life. This is a great example of experimental study psychology.

Stanford Prison Experiment, 1971

Stanford professor Philip Zimbardo wanted to learn how individuals conformed to societal roles. He wondered, for example, whether the tense relationship between prison guards and inmates in jails had more to do with the personalities of each or the environment.

During Zimbardo’s experiment , 24 male college students were assigned to be either a prisoner or a guard. The prisoners were held in a makeshift prison inside the basement of Stanford’s psychology department. They went through a standard booking process designed to take away their individuality and make them feel anonymous. Guards were given eight-hour shifts and tasked to treat the prisoners just like they would in real life.

Zimbardo found rather quickly that both the guards and prisoners fully adapted to their roles; in fact, he had to shut down the experiment after six days because it became too dangerous. Zimbardo even admitted he began thinking of himself as a police superintendent rather than a psychologist. The study confirmed that people will conform to the social roles they’re expected to play, especially overly stereotyped ones such as prison guards.

“We realized how ordinary people could be readily transformed from the good Dr. Jekyll to the evil Mr. Hyde,” Zimbardo wrote.

The Asch Conformity Study, 1951

Solomon Asch, a Polish-American social psychologist, was determined to see whether an individual would conform to a group’s decision, even if the individual knew it was incorrect. Conformity is defined by the American Psychological Association as the adjustment of a person’s opinions or thoughts so that they fall closer in line with those of other people or the normative standards of a social group or situation.

In his experiment , Asch selected 50 male college students to participate in a “vision test.” Individuals would have to determine which line on a card was longer. However, the individuals at the center of the experiment did not know that the other people taking the test were actors following scripts, and at times selected the wrong answer on purpose. Asch found that, on average over 12 trials, nearly one-third of the naive participants conformed with the incorrect majority, and only 25 percent never conformed to the incorrect majority. In the control group that featured only the participants and no actors, less than one percent of participants ever chose the wrong answer.

Asch’s experiment showed that people will conform to groups to fit in (normative influence) because of the belief that the group was better informed than the individual. This explains why some people change behaviors or beliefs when in a new group or social setting, even when it goes against past behaviors or beliefs.

The Bobo Doll Experiment, 1961, 1963

Stanford University professor Albert Bandura wanted to put the social learning theory into action. Social learning theory suggests that people can acquire new behaviors “through direct experience or by observing the behavior of others.” Using a Bobo doll , which is a blow-up toy in the shape of a life-size bowling pin, Bandura and his team tested whether children witnessing acts of aggression would copy them.

Bandura and two colleagues selected 36 boys and 36 girls between the ages of 3 and 6 from the Stanford University nursery and split them into three groups of 24. One group watched adults behaving aggressively toward the Bobo doll. In some cases, the adult subjects hit the doll with a hammer or threw it in the air. Another group was shown an adult playing with the Bobo doll in a non-aggressive manner, and the last group was not shown a model at all, just the Bobo doll.

After each session, children were taken to a room with toys and studied to see how their play patterns changed. In a room with aggressive toys (a mallet, dart guns, and a Bobo doll) and non-aggressive toys (a tea set, crayons, and plastic farm animals), Bandura and his colleagues observed that children who watched the aggressive adults were more likely to imitate the aggressive responses.

Unexpectedly, Bandura found that female children acted more physically aggressive after watching a male subject and more verbally aggressive after watching a female subject. The results of the study highlight how children learn behaviors from observing others.

The Learned Helplessness Experiment, 1965

Martin Seligman wanted to research a different angle related to Dr. Watson’s study of classical conditioning. In studying conditioning with dogs, Seligman made an astute observation : the subjects, which had already been conditioned to expect a light electric shock if they heard a bell, would sometimes give up after another negative outcome, rather than searching for the positive outcome.

Under normal circumstances, animals will always try to get away from negative outcomes. When Seligman tested his experiment on animals who hadn’t been previously conditioned, the animals attempted to find a positive outcome. Oppositely, the dogs who had been already conditioned to expect a negative response assumed there would be another negative response waiting for them, even in a different situation.

The conditioned dogs’ behavior became known as learned helplessness, the idea that some subjects won’t try to get out of a negative situation because past experiences have forced them to believe they are helpless. The study’s findings shed light on depression and its symptoms in humans.

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The Milgram Experiment, 1963

In the wake of the horrific atrocities carried out by Nazi Germany during World War II, Stanley Milgram wanted to test the levels of obedience to authority. The Yale University professor wanted to study if people would obey commands, even when it conflicted with the person’s conscience.

Participants of the condensed study , 40 males between the ages of 20 and 50, were split into learners and teachers. Though it seemed random, actors were always chosen as the learners, and unsuspecting participants were always the teachers. A learner was strapped to a chair with electrodes in one room while the experimenter äóñ another actor äóñ and a teacher went into another.

The teacher and learner went over a list of word pairs that the learner was told to memorize. When the learner incorrectly paired a set of words together, the teacher would shock the learner. The teacher believed the shocks ranged from mild all the way to life-threatening. In reality, the learner, who intentionally made mistakes, was not being shocked.

As the voltage of the shocks increased and the teachers became aware of the believed pain caused by them, some refused to continue the experiment. After prodding by the experimenter, 65 percent resumed. From the study, Milgram devised the agency theory , which suggests that people allow others to direct their actions because they believe the authority figure is qualified and will accept responsibility for the outcomes. Milgram’s findings help explain how people can make decisions against their own conscience, such as when participating in a war or genocide.

The Halo Effect Experiment, 1977

University of Michigan professors Richard Nisbett and Timothy Wilson were interested in following up a study from 50 years earlier on a concept known as the halo effect . In the 1920s, American psychologist Edward Thorndike researched a phenomenon in the U.S. military that showed cognitive bias. This is an error in how we think that affects how we perceive people and make judgements and decisions based on those perceptions.

In 1977, Nisbett and Wilson tested the halo effect using 118 college students (62 males, 56 females). Students were divided into two groups and were asked to evaluate a male Belgian teacher who spoke English with a heavy accent. Participants were shown one of two videotaped interviews with the teacher on a television monitor. The first interview showed the teacher interacting cordially with students, and the second interview showed the teacher behaving inhospitably. The subjects were then asked to rate the teacher’s physical appearance, mannerisms, and accent on an eight-point scale from appealing to irritating.

Nisbett and Wilson found that on physical appearance alone, 70 percent of the subjects rated the teacher as appealing when he was being respectful and irritating when he was cold. When the teacher was rude, 80 percent of the subjects rated his accent as irritating, as compared to nearly 50 percent when he was being kind.

The updated study on the halo effect shows that cognitive bias isn’t exclusive to a military environment. Cognitive bias can get in the way of making the correct decision, whether it’s during a job interview or deciding whether to buy a product that’s been endorsed by a celebrity we admire.

How Experiments Have Impacted Psychology Today

Contemporary psychologists have built on the findings of these studies to better understand human behaviors, mental illnesses, and the link between the mind and body. For their contributions to psychology, Watson, Bandura, Nisbett and Zimbardo were all awarded Gold Medals for Life Achievement from the American Psychological Foundation. Become part of the next generation of influential psychologists with King University’s online bachelor’s in psychology . Take advantage of King University’s flexible online schedule and complete the major coursework of your degree in as little as 16 months. Plus, as a psychology major, King University will prepare you for graduate school with original research on student projects as you pursue your goal of being a psychologist.

3 Ways to Explain Human Behavior

Three core processes that explain why people do what they do..

Posted January 1, 2019

When you try to understand people and explain why they do what they do, what frame do you use? The most common, intuitive (and highly useful) approach is the “belief-desire” frame. That is, people in everyday situations use both beliefs and desires to explain why people do what they do. For example, if we were to observe “Jon” leaving the house to go to the movies, we explain that action by assuming that Jon believes that the movie theater is playing a certain movie and seeing it is something he desires. We can be flexible with this frame. For example, if Jon were a movie critic, then we might presume his belief-desire state for seeing the movie is something different than if Jon were a teenager going to see the movie on a date with his new girlfriend.

Although belief-desire frames are helpful for everyday living, we need a more sophisticated, scientifically grounded frame for deeper understanding. The field of psychology has generated a wide variety of different paradigms, but unfortunately, these paradigms speak different languages and tell us different things about how to understand people. Skinnerian behaviorists claim that we need to get rid of terms like beliefs and desires, and empirically observe the kinds of effects that the environment has on the frequency of emitted behaviors. Cognitive psychologists use the language of information processing to explain beliefs and desires. Freudian theorists claim that conscious beliefs and desires ultimately play a pretty small role in explaining why people do what they do, and that the real drivers of human behavior are subconscious forces.

I am developing a more unified language for human psychology and psychotherapy. It takes the key insights from the cognitivists, behaviorists, psychodynamic theorists, and other paradigms (e.g., evolutionary psychology , Russian activity theory) and combines them into a more scientifically coherent (and comprehensive) system. When we look at human behavior via the unified system, three key processes frame our understanding.

The first key process is investment . The Unified Framework characterizes human behavior in terms of work effort directed toward effecting change. Whether Jon is headed out because he wants to see the movie, wants to critique the movie for his job, or wants to be with his girlfriend, his going to the movie is a form of investment. As suggested by the term investment, work efforts expended (which involve calculations about time, calories, opportunity costs, risks, and so forth) are directed toward particular outcomes. The return might be found in the joy he gets from the movie itself, from the fact that he completes an assignment for his job, or a kiss from his girlfriend at the end of it. Framed as such, we can then ask: Where do our tendencies toward investments come from? We are evolved primates, so evolution has primed us to value certain states of affairs (e.g., safety, territory, food, sex , higher social status) over others. In addition, people differ in terms of temperaments and dispositions, much of which is strongly influenced by genetics . Extroverted people find stimulating social situations more rewarding than introverted people. And, of course, one’s learning history directly shapes the investment value system. If Jon loved the first two Star Wars movies, we are not surprised when we hear he has a strong desire to see the third.

The second key process is social influence . As Aristotle noted, we are an incredibly social animal, and one of the most important features of our environment is other people. And our action-investments rarely take place on an island, but they take place in the context of a social matrix. Social influence here refers to two things. First, it refers to the process by which one person’s actions impact the investment of another person. In the current example, it would refer to the way it came about that Jon was going to the movies (did he ask her, she ask him, was there any tension in the process, etc). Important social influence processes involve competition , cooperation , and whether exchanges move people closer (i.e., become more dependent) or make them further apart (become more independent).

The second meaning of influence is as a resource. When considered as a resource, it refers to the capacity to move other people in accordance with our interests. Here it refers to the levels of respect and social value other people show us, the extent to which they listen, care about our well-being, and are willing to sacrifice for us. So, if Jon is attracted to his girlfriend and she agrees to go to the movie with him, that is an indicator of social influence as a resource. The harder Jon had to work to get her to go results in raising questions about his social influence. If she breaks up with him, that is a powerful indication of a loss of social influence.

The third core process is justification . In the language of the Unified Framework, “justification” is a broad concept that refers to both the systematic structure and the legitimizing function of verbal communication (including writing). You can think of justification as anything that involves questions and answers which lead to claims about what is and what ought to be. For example, if Jon explains to his girlfriend, “I saw the first two movies in the series and thought they were great,” or says, “I am just happy to be with you, we can see whatever you want,” both are “justifications.” Likewise, if Jon (as a movie critic) gets a call and his editor asks if he has completed the write-up, there is a shared, if implicit, justification that Jon needs to do what his boss wants. Arguments, reasons for and against things, rationalizations, laws, and even scientific truth claims all are "justifications" in the unified language system. This blog is a justification for thinking about three core processes that explain why people do what they do.

If you want to achieve a better understanding of why people do what they do, consider starting with these three core processes. Human behavior is first and foremost a kind of “doing” or investing. Individuals do what they do because of either implicit or explicit cost-benefit analyses directed at certain outcomes. Then we can look to the social matrix to see the influence such investments might have on others or how the investment itself might be shaped by social influence factors. Finally, there is the explanatory system that people are using to make sense of the world around them and legitimize what they are doing and why. Keep in mind that even your analysis of the person's activity in terms of investment, influence, and justification is itself a kind of justification system. These processes line up quite well with common sense belief-desire psychology, and they are grounded in a scientifically unified view of human psychology.

• For more on investment, see here , here , and here
• For more on influence, see here , here , and here
• For more on justification, see here , here , and here

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Gregg Henriques, Ph.D. , is a professor of psychology at James Madison University.

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35 Human Behavior Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Psychologists, sociologists, and even anthropologists study patterns of human behaviors in order to unravel key insights into the human condition, cultural attitudes, cultural values , cultural norms , and their influence upon individuals and societies.

As the most advanced mammals on earth, we have a range of unique behaviors, generally oriented around or advanced social, technological, and cognitive abilities.

Below are examples of a range of human behaviors that make us unique.

Human Behavior Examples

Empathy is the human capacity to understand and share the feelings of another individual, mirroring their emotions as if we were experiencing them firsthand.

While some animals do demonstrate foundational elements of empathy, humans exhibit this behavioural trait at a level of complexity unparalleled in the animal kingdom.

Humans, with our intricate social constructs , have the ability to comprehensively interpret a diverse range of emotional states and respond accordingly.

Such intricate perception and attunement to the emotions of others forms a fundamental part of human communal interactions, positioning empathy as a quintessentially human trait.

2. Symbolic Thinking

Symbolic thinking represents the unique human ability to use symbols or images to depict something else.

This might sound straightforward, but it carries extensive implications . This behavior underpins the inception and evolution of language, culture, and art in human societies.

Our Homo Sapien ancestors did not only communicate through rudimentary sounds and gestures, but also demonstrated their capacity for symbolic thinking through pictorial representations and carved figurines.

This ability persists in the present day where humans employ symbolism in sophisticated forms of writing, illustrations, and virtual imagery.

3. Altruism

Altruism, the selfless act of placing the needs or wellbeing of others above one’s own, is a third uniquely human behavior.

There is an array of theories attempting to explain the evolution of human altruism, from developmental adaptation to sociocultural influences.

Besides mere kin selection, where related individuals help each other in order to improve their shared genetic success, humans also engage in gratuitous acts of goodwill towards entirely unrelated persons.

Distinct from the limited forms of cooperative behavior observed in other species, human altruism extends to widespread philanthropy, self-sacrificing heroism, and public service.

4. Creative Expression

Among the array of human behaviors, creative expression stands out as a remarkable trait.

The ability to conceive and bring forth novel ideas or objects not only for functional purposes, but also purely aesthetic or expressive ones, is a remarkable human speciality.

From painting and music to drama and literature, creative expression manifests in manifold ways across all cultures.

This expansive range and depth of creative manifestation separates humanity from other species.

5. Conceptualizing Time

The human behavior of conceptualizing time in abstract terms is significant. Humans are capable of discerning the past, present, and future, a trait unique to our species , to the best of our knowledge.

This ability to reflect upon the past and project into the future, along with understanding the abstract concept of ‘time,’ informs human decision-making processes.

More importantly, this concept of time provides a framework within which humans can plan and strategize, a trait that has significant implications for survival and success.

This behavior is striking in its complexity and in its relative absence in other species.

6. Vocal Language

Vocal language, the systematic and generative capability of assigning specific complex meanings to particular sounds, forming full sentences, distinguishes humans from other species .

Speech is the primary mode for transmitting information across generations and facilitating cooperation among human groups.

The use of vocal language allows the conveyance of complex, abstract ideas and emotions, a level of communication unmatched by non-human forms of interaction.

7. Cultural Transmission

The process of cultural transmission holds a prominent position among human behaviors.

Indeed, cultural transmission involves the intergenerational transfer of knowledge, beliefs, customs, skills, and behavioral norms in a community.

Unlike many animals that rely primarily on genetic programming to pass on survival tactics, humans teach and learn from one another in an ongoing cycle. From the crafting of tools to complex societal norms, each generation learns from the experience of its predecessors and contributes to the communal knowledge pool.

This continuous learning and teaching process enables the evolution of societies, eventually leading to the cultivation of the diverse and intricate cultures we see globally today.

8. Cooking Food

The act of cooking food distinguishes us from any other species .

Cooking has been an integral part of human behavior since the discovery of fire. It goes beyond simple food preparation, drastically altering the makeup of what we consume.

This has biological implications, allowing our bodies to obtain more energy from food materials and facilitating evolutionary changes, such as the growth of our brains. Additionally, cooking food has social implications, often serving as a centerpiece around which human gatherings and interactions occur.

9. Tools and Technology Use

Humans demonstrate a unique propensity for the use and creation of complex tools and technology.

This behavior involves not only using natural objects as tools but also modifying these objects and designing new ones to suit specific purposes. From crafting simple stone-age tools to developing advanced modern-day technologies, humans continuously innovate to enhance survival and improve the quality of life.

The complexity and sophistication of human tool use and technological development are unparalleled in the animal kingdom, making this a uniquely human behavior.

10. Self-Reflection

Self-reflection is a uniquely human behavior that sets us apart from other species.

Self-reflection is the practice of thinking about our own thoughts, emotions, decisions, and behaviors. It is a process that allows us to evaluate our experiences and learn from them, driving personal growth and development.

In essence, this behavior entails a higher level of cognition, enabling us to analyze ourselves and make changes based on our reflections. This process of introspection greatly contributes to human progress on an individual and societal level.

See More: Examples of Self-Reflection

11. Abstract Reasoning

Abstract reasoning forms a significant portion of the unique cognitive capabilities of humans.

Distinct from others in the animal kingdom, humans not only react to immediate, tangible situations but also display the remarkable capability to think abstractly.

By extracting meaning from complex situations, identifying underlying patterns, and making logical deductions, humans can contend with situations beyond sensory experience. This thought process underscores the development and application of mathematics, philosophy, and strategic planning.

12. Mourning

While grief is not exclusively human, our complex rituals around mourning distinguish us as a species.

Humans commemorate the lives of their deceased, honouring them with ritualistic practices that vary across cultures. These range from memorial services and funerals to distinct periods of mourning and the creation of monuments.

The act of mourning signifies humans’ potent awareness of mortality, capacity to remember, and the profound emotional bonds between individuals.

13. Trade and Barter

Trade and barter form an essential aspect of human societal interaction.

The practice of exchanging goods and services based on their perceived value is unique to human societies.

Unlike other species that might showcase primitive forms of resource exchange, humans have maintained complex trade systems, even from early civilization. This system has evolved over the centuries into global markets and economies, demonstrating the capacity of humans for strategic negotiation and cooperation on a large scale.

14. Law-Making

The creation and enforcement of laws are quintessentially human.

The formulation of codes of conduct governs individual and collective behavior. While numerous species have social rules, the human system of laws is unmatched in its complexity and sophistication.

From unwritten societal norms to formally enacted legislation that governs nations, this ability demonstrates the human desire for order, justice, and social stability.

15. Education

Education is an uncannily human pursuit.

The deliberate process of facilitating learning expands knowledge, skills, values, and attitudes. Learners absorb a planned curriculum via teaching, training, or research — a process more systematic and extensive than the rudimentary learning found in other species.

Beyond survival skills, the scope of human education spans arts, sciences, philosophy, history, physical dexterity, and social skills . The process of structured learning and intellectual growth underscores the foundations of human civilization and progress.

16. Music and Dance

Music and dance constitute a major aspect of human cultural and emotional expression.

These artistic forms are truly universal to human societies across the globe. They aren’t simply recreational activities but serve as means to express a broad spectrum of emotions, share stories, and reinforce group identity. Their interesting characteristic is that they require creativity, coordination, and a sense of rhythm and timing — all of which serve as testament to our cognitive abilities.

Moreover, music and dance can foster social bonding and unity during communal celebrations, explaining their central role in various cultural rituals and festivals.

17. Written Communication

The development and use of written communication distinguish humans significantly.

Humans not only communicate verbally but have developed a system by which language, ideas, and information can be represented in a visual and tangible form.

This ability has enriched human interaction, allowing societies to record their histories, disseminate knowledge, and structure complex social institutions .

It marks a significant departure from simpler forms of communication seen in non-human species, demonstrating the cognitive flexibility and creative innovativeness at the heart of human growth. Given its role in the success of our species, it’s almost impossible to overestimate the importance of written communication.

18. Medicine and Healthcare

The development of practices and principles related to medicine and healthcare is fundamentally a human endeavor.

Human societies have demonstrated an understanding of the mechanisms of health, injury, illness, and the body’s healing process.

With this understanding, they have embarked on developing treatments, therapies, and preventative measures to combat health problems, thus prioritizing population longevity and well-being. Our commitment to medicine separates us from other species, reflecting our cognitive complexity, empathy, and advanced problem-solving faculties.

This distinct behavior displays the transition from mere survival to a pursuit of improved quality and length of life, underlining the role of medicine in societal advancement.

19. Sports and Games

The creation and participation in highly organized sports and games underscore the depth of human creativity and social interaction.

Unlike other animals that engage in play as a form of survival training or social bonding, humans formalize play into structured games with complex rules and objectives.

This demonstrates the unique human capacities for strategic thinking , planning ahead, and operating as a team — skills that extend beyond the playfield.

Moreover, these sports and games serve as a conduit for societal bonding, group identification, and even international unity amid competition. The universal practice of sports and games is an affirmation of shared human culture, channeled through competition and cooperation.

20. Exploration

The drive for exploration and the pursuit of new knowledge is a trait that sits at the core of being human.

This desire to venture into unfamiliar territories, to study the uncharted, and to constantly push the boundaries of our knowledge sets us apart from other species.

Our history is replete with tales of explorers overcoming tremendous odds in the name of discovery, illuminating our collective consciousness .

Whether exploring geographical landscapes, outer space, or the realm of ideas, the trait of human exploration perpetuates our evolutionary imperative: adapt, learn, and survive. Ultimately, this venture into the unknown testifies to our relentless curiosity and sophistication as a species.

21. Metacognition

Metacognition, or thinking about thinking, underlines the level of cognitive sophistication that is unique to humans.

As humans, we have the capacity to analyze our thought processes, evaluate the efficiency of different thinking strategies , and alter our approach based on introspection. This level of self-awareness and self-regulation in the cognitive domain sharpens our learning efficiency and problem-solving skills.

Further, it fosters our ability for self-improvement, personal growth, and ethical decision-making. Metacognition demonstrates not just what we think, but also how we think, establishing it as a strictly human domain.

See More: Examples of Metacognition in Humans

22. Philanthropy

Philanthropy exemplifies our inherent capacity for empathy and compassion combined with our ability for advanced organization and collaboration.

By definition, philanthropy involves the use of resources to extend social goodwill and promote welfare on a large scale. This characteristic is unparalleled in the rest of the animal kingdom. This underscores our ability to sympathize with hardships beyond our immediate experiences and devise systematic approaches to alleviate those hardships.

The act of philanthropy underlines the height of ethical and moral developments within human societies, following a rationale that transcends basic survival instincts.

23. Spirituality and Religion

Spirituality and religion form an intricate part of the human behavioral repertoire for tens of thousands of years .

Humans across time and geographies have exhibited a propensity towards belief systems that explain the world around them, guiding moral frameworks, and providing a sense of purpose.

These systems encapsulate our ability for abstract thought, symbolic expression, and communal cohesion . Religion and spirituality offer a gateway into comprehending human existential concerns and our unique response to them.

These intricate systems, regardless of their truth value, offer meaningful narratives that shape our perceptions and behaviors, aptly reflecting the complexity of human cognition.

24. Sustainable Agriculture

The practice of sustainable agriculture is singularly distinctive of humans.

Taking charge of our food sources, humans transformed from simple gatherers to sophisticated farmers. This shift further catapulted us along the path of civilizational development, allowing population growth and societal stability.

We learned to cultivate crops, domesticate animals, and increasingly control our immediate environment.

While some animals show primitive forms of resource management, none approach the sophistication and scale of human sustainable agriculture. The conduct of agriculture highlights our capacity for long-term planning, understanding of ecological dynamics, and advanced problem-solving skills.

25. Civics and Politics

Civics and politics articulate the depths of human social organization.

Political structures reflect our ability to establish complex social organizations, consolidate communal values, and resolve conflicts.

These structures embark on regulating societal behaviors, distributing resources, and making collective decisions that affect entire communities or nations. Even in rudimentary forms, politics govern social interaction among human groups, highlighting our inherent social nature.

Crucially, it marks humans’ ability to construct abstract entities like power and authority and organize societies around such concepts.

Our political designs distinguish us sharply from other species, illustrating the social, cognitive, and communicative evolution of humankind.

26. Science and Technology

The development of science and technology sheds light on the intellectual prowess and innovative nature of humans.

This behavior involves systematically uncovering the salient principles governing our world and then, using this understanding, creating technological tools or methodologies.

With these, humans manipulate their environment and enhance their capabilities, from basic tools for hunting in the Stone Age to cutting-edge technology like artificial intelligence.

The development of science and technology emphasizes our cognitive capacity, problem-solving skills, and the human ability to adapt and evolve over time.

27. Collective Learning

The practice of collective learning marks a major milestone in human behavioral evolution.

In contrast to individual learning, collective learning involves the inter-generational transmission of knowledge and skillsets, fuelling progressive advancements in knowledge.

Our capacity to accumulate and build upon previous knowledge differentiates us from other species. This constant state of shared learning has fostered intricate societies and cultures, accelerating human advancement over centuries.

28. Storytelling

Storytelling is a distinctly human behavior which reflects our rich cognitive and linguistic abilities.

Humans have been telling stories since ancient times, and not simply for entertainment.

Storytelling acts as a means to pass on knowledge, values, and life lessons within and across generations. It demonstrates our ability to conceive complex, abstract thoughts, formulate them into stories, and effectively communicate these to others.

The stories often carry moral, cultural, or philosophical implications that further structure human societies.

29. Aesthetics and Beauty Appreciation

Appreciation of aesthetics and beauty forms an inherent part of human behaviors.

Unlike other species, humans not only recognize beauty but create and seek it. From artistic creations to the appreciation of natural beauty, humans have a distinct sense of aesthetics, often invoking emotional responses.

This conduct underscores our advanced cognitive capabilities and emotional depth.

The pursuit of beauty and the emotional fulfillment we derive from it underscore the rich emotional lives of humans.

30. Commerce and Economy

Commerce is a social activity specific to humans, born from our abilities to communicate, negotiate and exchange.

From prehistoric barter systems to modern digital economies, humans have developed complex structures to produce, distribute, and consume goods and services. These structures, or economies, support human survival, social interaction, and growth of civilizations.

Commerce involves planning, negotiations, and risk-taking, reflecting a depth of strategic thinking. It demonstrates our ability to value resources, understand demand and supply, and connect with other individuals for mutual benefit.

31. Long-Term Planning

Long-term planning stands as a testament to the forward-thinking capacity of humans.

Visible in our daily lives, right from scheduling our tasks to drafting career paths or establishing retirement plans, humans have the cognitive ability to forecast, prepare, and plan for the future.

We envisage future events and work towards tasks or goals that may not yield immediate benefits. This behavior underscores our unique temporal consciousness, strategic thinking, and the ability to delay immediate gratification for future benefits.

32. Mapping and Geography

The creation and utilization of geographical maps are unique to humans.

This practice represents a complex behavior where we consider spatial information, scale it down, and represent it symbolically.

Maps depict physical landscapes and aid navigation, strategic planning, and territorial understanding. This unique behavior exhibits our abstract thinking skills, comprehension of physical space, and the desire to move beyond the immediate environment.

33. Body Art and Modification

Body art and modification, including tattoos and piercings, are distinctively human behaviors.

Such modifications are not purely aesthetic, but often deeply symbolic and representative of personal or cultural identity . They attest to our self-awareness , need for self-expression, and social communication.

Body art and modification contribute to our individual uniqueness while also enabling a sense of communal unity, especially within particular cultural groups.

34. Conservation and Environmental Awareness

The concerted effort towards conservation and environmental stewardship is a trait that is undeniably human.

With comprehension of our impact on the environment, humans exhibit extraordinary capabilities in working towards the preservation of nature.

The act of conservation encompasses different practices such as creating natural reserves, implementing sustainable practices, and constructing legislative frameworks to limit harmful activities. This behavior represents our capacity for foresight, empathy towards all life forms, and our understanding of ecological interconnectedness.

35. Architecture and Infrastructure Development

The creation of complex architecture and infrastructure is purely a human endeavor.

From homes hewn out of caves to towering skyscrapers, humans engineer and construct structures for shelter, utility, and a symbol of societal progress. These constructions reflect our ability to manipulate the environment using tools and technology, our understanding of materials and their properties, and our innate need for safety and community.

The development of complex structures and infrastructures substantiates human ingenuity and the drive to create functional as well as beautiful spaces for inhabitation and use.

I made the decision to present examples of uniquely human behaviors in this article in order to demonstrate aspects of ourselves that make us special. But, I could very well have presented behaviors based on a range of types (such as overt behavior , covert behavior , ethical behavior , unethical behavior , learned behavior , prosocial behavior , and collective behavior ).

To explore in more depth, I recommend starting out with my types of behaviors article , which will demonstrate the ‘lay of the land’ for cognitive-behavioral studies, and can help you to spread out to other sub-categories that I’ve explored elsewhere on this site.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 19 Top Cognitive Psychology Theories (Explained)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 119 Bloom’s Taxonomy Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ All 6 Levels of Understanding (on Bloom’s Taxonomy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Self-Actualization Examples (Maslow's Hierarchy)

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• Connecting with community through human-centered, public health lens

Rachel Varisco, MPH ’20

• Health Behavior and Health Education

June 20, 2024

All along, the master plan had called for Rachel Varisco, MPH ’20, to one day don the white lab coat, drape a stethoscope over her shoulders and recite the Hippocratic Oath.

“I wanted to be a doctor,” the Pittsburgh native said. “Like a lot of people who intend to pursue careers in healthcare, I always thought I was going to end up as a doctor.”

But two years into her undergraduate studies in Ann Arbor, Varisco took a detour that changed her life, and the countless lives she has impacted in a variety of roles outside of the traditional MD job description.

“I didn’t know that public health was a thing until my junior year at Michigan when I decided pre-med wasn’t the best route for me,” she said, “but I knew I liked helping people.”

Varisco said she started giving serious thought to other avenues of serving people and caring for their overall health after a summer trip to Peru between her freshman and sophomore years at Michigan.

I didn’t know that public health was a thing until my junior year at Michigan when I decided pre-med wasn’t the best route for me, but I knew I liked helping people.”

“I got into a program that called for us to work in hospitals, but once I got to Peru and started working with kids, that was when I realized that the work I enjoyed the most was not in the hospital—it was becoming part of the community,” she said.

Varisco spoke some Spanish at the time, but she found that winning people’s trust and being able to help them did not necessarily require complex exchanges in conversation.

“In different ways, with the kids, language was not that essential,” she said. “A smile and kindness worked wonders. And with the adults, as long as you were kind to them and they could see that you were trying and you showed them that you cared, it made a world of difference.” 

After receiving a bachelor’s degree in Psychology and Spanish from the University of Michigan College of Literature, Science, and the Arts in 2016, Varisco went on to earn a Master of Public Health degree in Health Behavior and Health Education from Michigan Public Health . She currently works as a project evaluation manager/community engagement associate at the University of California-Irvine, where she is called on to fill many roles. 

Her primary responsibility is assessing whether it is possible to make systems-level change efforts to support healthcare programs in Orange County with the goal of making them available to everyone, no matter their economic or citizenship status.

“Orange County is very diverse, so I work a lot in the community and use a very community-oriented lens to evaluate these programs,” she said. “It is essential to have the community involved and make sure the community has a voice in what is done and how it is incorporated every day.”

She worked as a health and nutrition coordinator for Pacific Clinics’ Head Start/Early Head Start program before taking on her current role. Varisco was a research assistant in the Child Health and Development Lab while at the University of Michigan, working on creating a reflective practice curriculum for staff who went out to work on lead prevention efforts to help combat staff burnout. It was a program designed to support the mental health of the staff and address the toll that constantly seeing people in need would take on these individuals.  

Trust is so important. It’s the cornerstone of everything we do in healthcare. You might be an expert on the science involved in public health, but that means nothing unless you get the buy-in of the community or individual you are serving by earning their trust.”

During her time on the Ann Arbor campus, Varisco also assisted with Motherly Intercessions , where she worked with the children of incarcerated mothers, providing social and emotional support along with tutoring. She also interacted with the mothers while they were serving time in jail, holding parenting classes to prepare them for a better relationship with their children upon being released.

Whether the focus is on mental, physical, emotional or behavioral health, Varisco said the foundation is the same.

“Trust is so important,” she said. “It’s the cornerstone of everything we do in healthcare. You might be an expert on the science involved in public health, but that means nothing unless you get the buy-in of the community or individual you are serving by earning their trust.”

Varisco said that throughout her time at Michigan Public Health, she obtained the skills she uses “to be humble, approach people with an open heart and mind, and gain their trust.”

“That way, you can truly help them,” she said. “Without that, the science is obsolete.”

Varisco said that the time she spent in the classroom of Barbara Israel , professor of Health Behavior and Health Education, and working in Israel’s Detroit Community-Academic Urban Research Center , has had a significant impact on her career.

“The knowledge she has to share with her students about approaching a community with humility—I use that every single day in my work,” Varisco said. “I am committed to fighting against social and health disparities. That is a part of me, and so much of who I am was honed at Michigan.”

I am consistently learning to be more aware of structural biases that contribute to social inequities and racism. And wherever my future career takes me, I am excited to engage in a lifelong commitment of growth, self-reflection and community collaboration.”

She also cited Ken Resnicow , Irwin Rosenstock Professor of Health Behavior and Health Education, and his Health Communications and Motivational Interviewing classes as being especially valuable.

“He helped give me the skills and confidence to humbly and appropriately connect with the community through a human-centered and public health lens,” she said.

Varisco said she is grateful that her education and career track took that undergraduate detour that has allowed her to explore and experience public health in an impactful and rewarding manner. 

“I am consistently learning to be more aware of structural biases that contribute to social inequities and racism,” she said. “And wherever my future career takes me, I am excited to engage in a lifelong commitment of growth, self-reflection and community collaboration.”

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Traffic engineers build roads that invite crashes because they rely on outdated research and faulty data

Professor of Civil Engineering, University of Colorado Denver

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Wes Marshall receives funding from the US Department of Transportation's University Transportation Centers program and various state transportation departments.

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“Can you name the truck with four-wheel drive, smells like a steak, and seats 35?”

Back in 1998, “The Simpsons” joked about the Canyonero, an SUV so big that they were obviously kidding. At that time, it was preposterous to think anyone would drive something that was “12 yards long, two lanes wide, 65 tons of American Pride.”

In 2024, that joke isn’t far from reality.

And our reality is one where more pedestrians and bicyclists are getting killed on U.S. streets than at any time in the past 45 years – over 1,000 bicyclists and 7,500 pedestrians in 2022 alone.

Vehicle size is a big part of this problem. A recent paper by urban economist Justin Tyndall found that increasing the front-end height of a vehicle by roughly 4 inches (10 centimeters) increases the chance of a pedestrian fatality by 22% . The risk increases by 31% for female pedestrians or those over 65 years, and by 81% for children.

It’s hard to argue with physics, so there is a certain logic in blaming cars for rising traffic deaths. In fact, if a bicyclist is hit by a pickup truck instead of a car, Tyndall suggests that they are 291% more likely to die.

Yet automakers have long asserted that if everyone simply followed the rules of the road, nobody would die. Vehicle size is irrelevant to that assertion.

My discipline, traffic engineering , acts similarly. We underestimate our role in perpetuating bad outcomes, as well as the role that better engineering can play in designing safer communities and streets.

Millions of road deaths

How bad are the bad outcomes? The U.S. has been tracking car-related road deaths since 1899. As a country, we hit the threshold of 1 million cumulative deaths in 1953, 2 million in 1975 and 3 million in 1998. While the past several years of data have not yet been released, I estimate that the U.S. topped 4 million total road deaths sometime in the spring of 2024.

How many of those are pedestrians and bicyclists? Analysts didn’t do a great job of separating out the pedestrian and cyclist deaths in the early years , but based on later trends, my estimate is that some 930,000 pedestrians and bicyclists have been killed by automobiles in the U.S.

How many of those deaths do we blame on big cars or bad streets? The answer is, very few.

As I show in my new book, “ Killed by a Traffic Engineer: Shattering the Delusion that Science Underlies our Transportation System ,” the National Highway Traffic Safety Administration calls road user error the “ critical reason” behind 94% of crashes, injuries and deaths .

Crash data backs that up.

Police investigate crashes and inevitably look to see which road users, including drivers, pedestrians and cyclists, are most at fault. It’s easy to do because in almost any crash, road user error appears to be the obvious problem.

This approach helps insurance companies figure out who needs to pay. It also helps automakers and traffic engineers rationalize away all these deaths. Everyone – except the families and friends of these 4 million victims – goes to sleep at night feeling good that bad-behaving road users just need more education or better enforcement.

But road user error only scratches the surface of the problem.

Who creates dangerous streets?

When traffic engineers build an overly wide street that looks more like a freeway , and a speeding driver in a Canyonero crashes, subsequent crash data blames the driver for speeding.

When traffic engineers provide lousy crosswalks separated by long distances , and someone jaywalks and gets hit by that speeding Canyonero driver, one or both of these road users will be blamed in the official crash report.

And when automakers build gargantuan vehicles that can easily go double the speed limit and fill them with distracting touchscreens , crash data will still blame the road users for almost anything bad that happens.

These are the sorts of systemic conditions that lead to many so-called road user errors. Look just below the surface, though, and it becomes clear that many human errors represent the typical, rational behaviors of typical, rational road users given the transportation system and vehicle options we put in front of them.

Look more deeply, and you can start to see how our underlying crash data gives everyone a pass but the road users themselves. Everyone wants a data-driven approach to road safety, but today’s standard view of crash data lets automakers, insurance companies and policymakers who shape vehicle safety standards off the hook for embiggening these ever-larger cars and light-duty trucks.

It also absolves traffic engineers, planners and policymakers of blame for creating a transportation system where for most Americans, the only rational choice for getting around is a car .

Understanding road behavior

Automakers want to sell cars and make money. And if bigger SUVs seem safer to potential customers, while also being much more profitable , it’s easy to see how interactions between road users and car companies – making seemingly rational decisions – have devolved into an SUV arms race.

Even though these same vehicles are less safe for pedestrians, bicyclists and those in opposing vehicles , the current data-driven approach to road safety misses that part of the story.

This can’t all be fixed at once. But by pursuing business as usual, automakers and traffic engineers will continue wasting money on victim-blaming campaigns or billboards placed high over a road telling drivers to pay attention to the road .

A better starting point would be remaking the U.S.’s allegedly data-driven approach to road safety by reinventing our understanding of the crash data that informs it all.

The key is starting to ask why. Why did these road users act as they did? Why didn’t they follow the rules that were laid out for them? Bad road user behavior shouldn’t be excused, but a bit of digging below the surface of crash data unearths a completely different story.

Figuring out which road user is most at fault may be useful for law enforcement and insurance companies, but it doesn’t give transportation engineers, planners, policymakers or automakers much insight into what they can do better. Even worse, it has kept them from realizing that they might be doing anything wrong.

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• Published: 19 June 2024

Detecting hallucinations in large language models using semantic entropy

• Sebastian Farquhar   ORCID: orcid.org/0000-0002-9185-6415 1   na1 ,
• Jannik Kossen 1   na1 ,
• Lorenz Kuhn 1   na1 &
• Yarin Gal   ORCID: orcid.org/0000-0002-2733-2078 1  

Nature volume  630 ,  pages 625–630 ( 2024 ) Cite this article

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Large language model (LLM) systems, such as ChatGPT 1 or Gemini 2 , can show impressive reasoning and question-answering capabilities but often ‘hallucinate’ false outputs and unsubstantiated answers 3 , 4 . Answering unreliably or without the necessary information prevents adoption in diverse fields, with problems including fabrication of legal precedents 5 or untrue facts in news articles 6 and even posing a risk to human life in medical domains such as radiology 7 . Encouraging truthfulness through supervision or reinforcement has been only partially successful 8 . Researchers need a general method for detecting hallucinations in LLMs that works even with new and unseen questions to which humans might not know the answer. Here we develop new methods grounded in statistics, proposing entropy-based uncertainty estimators for LLMs to detect a subset of hallucinations—confabulations—which are arbitrary and incorrect generations. Our method addresses the fact that one idea can be expressed in many ways by computing uncertainty at the level of meaning rather than specific sequences of words. Our method works across datasets and tasks without a priori knowledge of the task, requires no task-specific data and robustly generalizes to new tasks not seen before. By detecting when a prompt is likely to produce a confabulation, our method helps users understand when they must take extra care with LLMs and opens up new possibilities for using LLMs that are otherwise prevented by their unreliability.

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Testing theory of mind in large language models and humans

Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT

ThoughtSource: A central hub for large language model reasoning data

‘Hallucinations’ are a critical problem 9 for natural language generation systems using large language models (LLMs), such as ChatGPT 1 or Gemini 2 , because users cannot trust that any given output is correct.

Hallucinations are often defined as LLMs generating “content that is nonsensical or unfaithful to the provided source content” 9 , 10 , 11 but they have come to include a vast array of failures of faithfulness and factuality. We focus on a subset of hallucinations which we call ‘confabulations’ 12 for which LLMs fluently make claims that are both wrong and arbitrary—by which we mean that the answer is sensitive to irrelevant details such as random seed. For example, when asked a medical question “What is the target of Sotorasib?” an LLM confabulates by sometimes answering KRASG12 ‘C’ (correct) and other times KRASG12 ‘D’ (incorrect) despite identical instructions. We distinguish this from cases in which a similar ‘symptom’ is caused by the following different mechanisms: when LLMs are consistently wrong as a result of being trained on erroneous data such as common misconceptions 13 ; when the LLM ‘lies’ in pursuit of a reward 14 ; or systematic failures of reasoning or generalization. We believe that combining these distinct mechanisms in the broad category hallucination is unhelpful. Our method makes progress on a portion of the problem of providing scalable oversight 15 by detecting confabulations that people might otherwise find plausible. However, it does not guarantee factuality because it does not help when LLM outputs are systematically bad. Nevertheless, we significantly improve question-answering accuracy for state-of-the-art LLMs, revealing that confabulations are a great source of error at present.

We show how to detect confabulations by developing a quantitative measure of when an input is likely to cause an LLM to generate arbitrary and ungrounded answers. Detecting confabulations allows systems built on LLMs to avoid answering questions likely to cause confabulations, to make users aware of the unreliability of answers to a question or to supplement the LLM with more grounded search or retrieval. This is essential for the critical emerging field of free-form generation in which naive approaches, suited to closed vocabulary and multiple choice, fail. Past work on uncertainty for LLMs has focused on simpler settings, such as classifiers 16 , 17 and regressors 18 , 19 , whereas the most exciting applications of LLMs relate to free-form generations.

The term hallucination in the context of machine learning originally comes from filling in ungrounded details, either as a deliberate strategy 20 or as a reliability problem 4 . The appropriateness of the metaphor has been questioned as promoting undue anthropomorphism 21 . Although we agree that metaphor must be used carefully with LLMs 22 , the widespread adoption of the term hallucination reflects the fact that it points to an important phenomenon. This work represents a step towards making that phenomenon more precise.

To detect confabulations, we use probabilistic tools to define and then measure the ‘semantic’ entropy of the generations of an LLM—an entropy that is computed over meanings of sentences. High entropy corresponds to high uncertainty 23 , 24 , 25 —so semantic entropy is one way to estimate semantic uncertainties. Semantic uncertainty, the broader category of measures we introduce, could be operationalized with other measures of uncertainty, such as mutual information, instead. Entropy in free-form generation is normally hard to measure because answers might mean the same thing (be semantically equivalent) despite being expressed differently (being syntactically or lexically distinct). This causes naive estimates of entropy or other lexical variation scores 26 to be misleadingly high when the same correct answer might be written in many ways without changing its meaning.

By contrast, our semantic entropy moves towards estimating the entropy of the distribution of meanings of free-form answers to questions, insofar as that is possible, rather than the distribution over the ‘tokens’ (words or word-pieces) which LLMs natively represent. This can be seen as a kind of semantic consistency check 27 for random seed variation. An overview of our approach is provided in Fig. 1 and a worked example in Supplementary Table 1 .

a , Naive entropy-based uncertainty measures variation in the exact answers, treating ‘Paris’, ‘It’s Paris’ and ‘France’s capital Paris’ as different. But this is unsuitable for language tasks for which sometimes different answers mean the same things. Our semantic entropy clusters answers which share meanings before computing the entropy. A low semantic entropy shows that the LLM is confident about the meaning. b , Semantic entropy can also detect confabulations in longer passages. We automatically decompose a long generated answer into factoids. For each factoid, an LLM generates questions to which that factoid might have been the answer. The original LLM then samples  M possible answers to these questions. Finally, we compute the semantic entropy over the answers to each specific question, including the original factoid. Confabulations are indicated by high average semantic entropy for questions associated with that factoid. Here, semantic entropy classifies Fact 1 as probably not a confabulation because generations often mean the same thing, despite very different wordings, which a naive entropy would have missed.

Intuitively, our method works by sampling several possible answers to each question and clustering them algorithmically into answers that have similar meanings, which we determine on the basis of whether answers in the same cluster entail each other bidirectionally 28 . That is, if sentence A entails that sentence B is true and vice versa, then we consider them to be in the same semantic cluster. We measure entailment using both general-purpose LLMs and natural language inference (NLI) tools developed specifically for detecting entailment for which we show direct evaluations in Supplementary Tables 2 and 3 and Supplementary Fig. 1 . Textual entailment has previously been shown to correlate with faithfulness 10 in the context of factual consistency 29 as well as being used to measure factuality in abstractive summarization 30 , especially when applied at the right granularity 31 .

Semantic entropy detects confabulations in free-form text generation across a range of language models and domains, without previous domain knowledge. Our evaluations cover question answering in trivia knowledge (TriviaQA 32 ), general knowledge (SQuAD 1.1; ref. 33 ), life sciences (BioASQ 34 ) and open-domain natural questions (NQ-Open 35 ) derived from actual queries to Google Search 36 . In addition, semantic entropy detects confabulations in mathematical word problems (SVAMP 37 ) and in a biography-generation dataset, FactualBio, accompanying this paper.

Our results for TriviaQA, SQuAD, BioASQ, NQ-Open and SVAMP are all evaluated context-free and involve sentence-length answers (96 ± 70 characters, mean ± s.d.) and use LLaMA 2 Chat (7B, 13B and 70B parameters) 38 , Falcon Instruct (7B and 40B) 39 and Mistral Instruct (7B) 40 . In the Supplementary Information , we further consider short-phrase-length answers. Results for FactualBio (442 ± 122 characters) use GPT-4 (ref. 1 ). At the time of writing, GPT-4 (ref. 1 ) did not expose output probabilities 41 or hidden states, although it does now. As a result, we propose a discrete approximation of our estimator for semantic entropy which allows us to run experiments without access to output probabilities, which we use for all GPT-4 results in this paper and which performs similarly well.

Our confabulation detection with semantic entropy is more robust to user inputs from previously unseen domains than methods which aim to ‘learn’ how to detect confabulations from a set of example demonstrations. Our method is unsupervised, meaning that we do not need labelled examples of confabulations. By contrast, supervised methods detect confabulations by learning patterns behind examples of confabulations, assuming that future questions preserve these patterns. But this assumption is often untrue in new situations or with confabulations that human overseers are unable to identify (compare Fig. 17 of ref. 24 ). As a strong supervised baseline, we compare to an embedding regression method inspired by ref. 24 which trains a logistic regression classifier to predict whether the model correctly answered a question on the basis of the final ‘embedding’ (hidden state) of the LLM. We also use the P (True) method 24 which looks at the probability with which an LLM predicts that the next token is ‘True’ when few-shot prompted to compare a main answer with ‘brainstormed’ alternatives.

Confabulations contribute substantially to incorrect answers given by language models. We show that semantic entropy can be used to predict many incorrect model answers and to improve question-answering accuracy by refusing to answer those questions the model is uncertain about. Corresponding to these two uses, we evaluate two main metrics. First, the widely used area under the receiver operating characteristic (AUROC) curve for the binary event that a given answer is incorrect. This measure captures both precision and recall and ranges from 0 to 1, with 1 representing a perfect classifier and 0.5 representing an un-informative classifier. We also show a new measure, the area under the ‘rejection accuracy’ curve (AURAC). This studies the case in which the confabulation detection score is used to refuse to answer the questions judged most likely to cause confabulations. Rejection accuracy is the accuracy of the answers of the model on the remaining questions and the area under this curve is a summary statistic over many thresholds (representative threshold accuracies are provided in Supplementary Material ). The AURAC captures the accuracy improvement which users would experience if semantic entropy was used to filter out questions causing the highest entropy.

Detecting confabulations in QA and math

In Fig. 2 , we show that both semantic entropy and its discrete approximation outperform our best baselines for sentence-length generations. These results are averaged across datasets and provide the actual scores on the held-out evaluation dataset. We report the raw average score across held-out evaluation datasets without standard error because the distributional characteristics are more a property of the models and datasets selected than the method. Consistency of relative results across different datasets is a stronger indicator of variation in this case.

Semantic entropy outperforms leading baselines and naive entropy. AUROC (scored on the y -axes) measures how well methods predict LLM mistakes, which correlate with confabulations. AURAC (likewise scored on the y -axes) measures the performance improvement of a system that refuses to answer questions which are judged likely to cause confabulations. Results are an average over five datasets, with individual metrics provided in the Supplementary Information .

Semantic entropy greatly outperforms the naive estimation of uncertainty using entropy: computing the entropy of the length-normalized joint probability of the token sequences. Naive entropy estimation ignores the fact that token probabilities also express the uncertainty of the model over phrasings that do not change the meaning of an output.

Our methods also outperform the supervised embedding regression method both in- and out-of-distribution. In pale-yellow bars we show that embedding regression performance deteriorates when its training data do not match the deployment distribution—which mirrors the common real-world case in which there is a distribution shift between training and deployment 42 —the plotted value is the average metric for embedding regression trained on one of the four ‘off-distribution’ datasets for that evaluation. This is critical because reliable uncertainty is most important when the data distribution shifts. Semantic entropy also outperforms P (True) which is supervised ‘in-context’; that is, it is adapted to the deployment task with a few training examples provided in the LLM prompt itself. The discrete variant of semantic entropy performs similarly to our standard estimator, despite not requiring exact output probabilities.

Averaged across the 30 combinations of tasks and models we study, semantic entropy achieves the best AUROC value of 0.790 whereas naive entropy (0.691), P (True) (0.698) and the embedding regression baseline (0.687) lag behind it. Semantic entropy performs well consistently, with stable performance (between 0.78 and 0.81 AUROC) across the different model families (LLaMA, Falcon and Mistral) and scales (from 7B to 70B parameters) which we study (we report summary statistics for each dataset and model as before). Although semantic entropy outperforms the baselines across all model sizes, P (True) seems to improve with model size, suggesting that it might become more competitive for very capable honest models in settings that the model understands well (which are, however, not the most important cases to have good uncertainty). We use ten generations to compute entropy, selected using analysis in Supplementary Fig. 2 . Further results for short-phrase generations are described in Supplementary Figs. 7 – 10 .

The results in Fig. 2 offer a lower bound on the effectiveness of semantic entropy at detecting confabulations. These evaluations determine whether semantic entropy and baseline methods can detect when the answers of the model are incorrect (which we validate against human correctness evaluations in Supplementary Table 4 ). In addition to errors from confabulations (arbitrary incorrectness), this also includes other types of mistakes for which semantic entropy is not suited, such as consistent errors learned from the training data. The fact that methods such as embedding regression are able to spot other kinds of errors, not just confabulations, but still are outperformed by semantic entropy, suggests that confabulations are a principal category of errors for actual generations.

Examples of questions and answers from TriviaQA, SQuAD and BioASQ, for LLaMA 2 Chat 70B, are shown in Table 1 . These illustrate how only semantic entropy detects when the meaning is constant but the form varies (the first row of the table) whereas semantic entropy and naive entropy both correctly predict the presence of confabulations when the form and meaning vary together (second row) and predict the absence of confabulations when the form and meaning are both constant across several resampled generations (third row). In the final row, we give an example in which semantic entropy is erroneously high as a result of overly sensitive semantic clustering relative to the reference answer. Our clustering method distinguishes the answers which provide a precise date from those which only provide a year. For some contexts that would have been correct but in this context the distinction between the specific day and the year is probably irrelevant. This highlights the importance of context and judgement in clustering, especially in subtle cases, as well as the shortcomings of evaluating against fixed reference answers which do not capture the open-ended flexibility of conversational deployments of LLMs.

Detecting confabulations in biographies

Semantic entropy is most natural for sentences that express a single proposition but the idea of semantic equivalence is trickier to apply to longer passages which express many propositions which might only agree partially 43 . Nevertheless, we can use semantic entropy to detect confabulations in longer generations, such as entire paragraphs of text. To show this, we develop a dataset of biographical generations from GPT-4 (v.0613) for 21 individuals notable enough to have their own Wikipedia page but without extensive online biographies. From each biography generated by GPT-4, we automatically extract propositional factual claims about the individual (150 factual claims in total), which we manually label as true or false.

Applying semantic entropy to this problem is challenging. Naively, one might simply regenerate each sentence (conditioned on the text so far) and then compute semantic entropy over these regenerations. However, the resampled sentences often target different aspects of the biography: for example, one time describing family and the next time profession. This is analogous to the original problem semantic entropy was designed to resolve: the model is uncertain about the right ordering of facts, not about the facts themselves. To address this, we break down the entire paragraph into factual claims and reconstruct questions which might have been answered by those claims. Only then do we apply semantic entropy (Fig. 1 ) by generating three new answers to each question (selected with analysis in Supplementary Figs. 3 and 4 ) and computing the semantic entropy over those generations plus the original factual claim. We aggregate these by averaging the semantic entropy over all the questions to get an uncertainty score for each proposition, which we use to detect confabulations. Unaggregated results are shown in Supplementary Figs. 5 and 6 .

As GPT-4 did not allow access to the probability of the generation at the time of writing, we use a discrete variant of semantic entropy which makes the further approximation that we can infer a discrete empirical distribution over semantic meaning clusters from only the generations ( Methods ). This allows us to compute semantic entropy using only the black-box outputs of an LLM. However, we were unable to compute the naive entropy baseline, the standard semantic entropy estimator or the embedding regression baseline for GPT-4 without output probabilities and embeddings.

In Fig. 3 we show that the discrete variant of semantic entropy effectively detects confabulations on this dataset. Its AUROC and AURAC are higher than either a simple ‘self-check’ baseline—which just asks the LLM whether the factoid is likely to be true—or a variant of P (True) which has been adapted to work for the paragraph-length setting. Discrete semantic entropy has better rejection accuracy performance until 20% of the questions have been rejected at which point P (True) has a narrow edge. This indicates that the questions predicted to cause confabulations are indeed more likely to be wrong.

The discrete variant of our semantic entropy estimator outperforms baselines both when measured by AUROC and AURAC metrics (scored on the y -axis). The AUROC and AURAC are substantially higher than for both baselines. At above 80% of questions being answered, semantic entropy has the highest accuracy. Only when the top 20% of answers judged most likely to be confabulations are rejected does the answer accuracy on the remainder for the P (True) baseline exceed semantic entropy.

Our probabilistic approach, accounting for semantic equivalence, detects an important class of hallucinations: those that are caused by a lack of LLM knowledge. These are a substantial portion of the failures at present and will continue even as models grow in capabilities because situations and cases that humans cannot reliably supervise will persist. Confabulations are a particularly noteworthy failure mode for question answering but appear in other domains too. Semantic entropy needs no previous domain knowledge and we expect that algorithmic adaptations to other problems will allow similar advances in, for example, abstractive summarization. In addition, extensions to alternative input variations such as rephrasing or counterfactual scenarios would allow a similar method to act as a form of cross-examination 44 for scalable oversight through debate 45 .

The success of semantic entropy at detecting errors suggests that LLMs are even better at “knowing what they don’t know” than was argued by ref. 24 —they just don’t know they know what they don’t know. Our method explicitly does not directly address situations in which LLMs are confidently wrong because they have been trained with objectives that systematically produce dangerous behaviour, cause systematic reasoning errors or are systematically misleading the user. We believe that these represent different underlying mechanisms—despite similar ‘symptoms’—and need to be handled separately.

One exciting aspect of our approach is the way it makes use of classical probabilistic machine learning methods and adapts them to the unique properties of modern LLMs and free-form language generation. We hope to inspire a fruitful exchange of well-studied methods and emerging new problems by highlighting the importance of meaning when addressing language-based machine learning problems.

Semantic entropy as a strategy for overcoming confabulation builds on probabilistic tools for uncertainty estimation. It can be applied directly to any LLM or similar foundation model without requiring any modifications to the architecture. Our ‘discrete’ variant of semantic uncertainty can be applied even when the predicted probabilities for the generations are not available, for example, because access to the internals of the model is limited.

In this section we introduce background on probabilistic methods and uncertainty in machine learning, discuss how it applies to language models and then discuss our contribution, semantic entropy, in detail.

Uncertainty and machine learning

We aim to detect confabulations in LLMs, using the principle that the model will be uncertain about generations for which its output is going to be arbitrary.

One measure of uncertainty is the predictive entropy of the output distribution, which measures the information one has about the output given the input 25 . The predictive entropy (PE) for an input sentence x is the conditional entropy ( H ) of the output random variable Y with realization y given x ,

A low predictive entropy indicates an output distribution which is heavily concentrated whereas a high predictive entropy indicates that many possible outputs are similarly likely.

Aleatoric and epistemic uncertainty

We do not distinguish between aleatoric and epistemic uncertainty in our analysis. Researchers sometimes separate aleatoric uncertainty (uncertainty in the underlying data distribution) from epistemic uncertainty (caused by having only limited information) 46 . Further advances in uncertainty estimation which separate these kinds of uncertainty would enhance the potential for our semantic uncertainty approach by allowing extensions beyond entropy.

Joint probabilities of sequences of tokens

Generative LLMs produce strings of text by selecting tokens in sequence. Each token is a wordpiece that often represents three or four characters (though especially common sequences and important words such as numbers typically get their own token). To compute entropies, we need access to the probabilities the LLM assigns to the generated sequence of tokens. The probability of the entire sequence, s , conditioned on the context, x , is the product of the conditional probabilities of new tokens given past tokens, whose resulting log-probability is \(\log P({\bf{s}}| {\boldsymbol{x}})={\sum }_{i}\log P({s}_{i}| {{\bf{s}}}_{ < i},{\boldsymbol{x}})\) , where s i is the i th output token and s < i denotes the set of previous tokens.

Length normalization

When comparing the log-probabilities of generated sequences, we use ‘length normalization’, that is, we use an arithmetic mean log-probability, \(\frac{1}{N}{\sum }_{i}^{N}\log P({s}_{i}| {{\bf{s}}}_{ < i},{\boldsymbol{x}})\) , instead of the sum. In expectation, longer sequences have lower joint likelihoods because of the conditional independence of the token probabilities 47 . The joint likelihood of a sequence of length N shrinks exponentially in N . Its negative log-probability therefore grows linearly in N , so longer sentences tend to contribute more to entropy. We therefore interpret length-normalizing the log-probabilities when estimating the entropy as asserting that the expected uncertainty of generations is independent of sentence length. Length normalization has some empirical success 48 , including in our own preliminary experiments, but little theoretical justification in the literature.

Principles of semantic uncertainty

If we naively calculate the predictive entropy directly from the probabilities of the generated sequence of tokens, we conflate the uncertainty of the model over the meaning of its answer with the uncertainty over the exact tokens used to express that meaning. For example, even if the model is confident in the meaning of a generation, there are still usually many different ways for phrasing that generation without changing its meaning. For the purposes of detecting confabulations, the uncertainty of the LLM over meanings is more important than the uncertainty over the exact tokens used to express those meanings.

Our semantic uncertainty method therefore seeks to estimate only the uncertainty the LLM has over the meaning of its generation, not the choice of words. To do this, we introduce an algorithm that clusters model generations by meaning and subsequently calculates semantic uncertainty. At a high level this involves three steps:

Generation: sample output sequences of tokens from the predictive distribution of a LLM given a context x .

Clustering: cluster sequences by their meaning using our clustering algorithm based on bidirectional entailment.

Entropy estimation: estimate semantic entropy by summing probabilities of sequences that share a meaning following equation ( 2 ) and compute their entropy.

Generating a set of answers from the model

Given some context x as input to the LLM, we sample M sequences, { s (1) , …,  s ( M ) } and record their token probabilities, { P ( s (1) ∣ x ), …,  P ( s ( M ) ∣ x )}. We sample all our generations from a single model, varying only the random seed used for sampling from the token probabilities. We do not observe the method to be particularly sensitive to details of the sampling scheme. In our implementation, we sample at temperature 1 using nucleus sampling ( P  = 0.9) (ref. 49 ) and top- K sampling ( K  = 50) (ref. 50 ). We also sample a single generation at low temperature (0.1) as an estimate of the ‘best generation’ of the model to the context, which we use to assess the accuracy of the model. (A lower sampling temperature increases the probability of sampling the most likely tokens).

Clustering by semantic equivalence

To estimate semantic entropy we need to cluster generated outputs from the model into groups of outputs that mean the same thing as each other.

This can be described using ‘semantic equivalence’ which is the relation that holds between two sentences when they mean the same thing. We can formalize semantic equivalence mathematically. Let the space of tokens in a language be \({\mathcal{T}}\) . The space of all possible sequences of tokens of length N is then \({{\mathcal{S}}}_{N}\equiv {{\mathcal{T}}}^{N}\) . Note that N can be made arbitrarily large to accommodate whatever size of sentence one can imagine and one of the tokens can be a ‘padding’ token which occurs with certainty for each token after the end-of-sequence token. For some sentence \({\bf{s}}\in {{\mathcal{S}}}_{N}\) , composed of a sequence of tokens, \({s}_{i}\in {\mathcal{T}}\) , there is an associated meaning. Theories of meaning are contested 51 . However, for specific models and deployment contexts many considerations can be set aside. Care should be taken comparing very different models and contexts.

Let us introduce a semantic equivalence relation, E (  ⋅  ,  ⋅  ), which holds for any two sentences that mean the same thing—we will operationalize this presently. Recall that an equivalence relation is any reflexive, symmetric and transitive relation and that any equivalence relation on a set corresponds to a set of equivalence classes. Each semantic equivalence class captures outputs that can be considered to express the same meaning. That is, for the space of semantic equivalence classes \({\mathcal{C}}\) the sentences in the set \(c\in {\mathcal{C}}\) can be regarded in many settings as expressing a similar meaning such that \(\forall {\bf{s}},{{\bf{s}}}^{{\prime} }\in c:E({\bf{s}},{{\bf{s}}}^{{\prime} })\) . So we can build up these classes of semantically equivalent sentences by checking if new sentences share a meaning with any sentences we have already clustered and, if so, adding them into that class.

We operationalize E (  ⋅  ,  ⋅  ) using the idea of bidirectional entailment, which has a long history in linguistics 52 and natural language processing 28 , 53 , 54 . A sequence, s , means the same thing as a second sequence, s ′, only if the sequences entail (that is, logically imply) each other. For example, ‘The capital of France is Paris’ entails ‘Paris is the capital of France’ and vice versa because they mean the same thing. (See later for a discussion of soft equivalence and cases in which bidirectional entailment does not guarantee equivalent meanings).

Importantly, we require that the sequences mean the same thing with respect to the context—key meaning is sometimes contained in the context. For example, ‘Paris’ does not entail ‘The capital of France is Paris’ because ‘Paris’ is not a declarative sentence without context. But in the context of the question ‘What is the capital of France?’, the one-word answer does entail the longer answer.

Detecting entailment has been the object of study of a great deal of research in NLI 55 . We rely on language models to predict entailment, such as DeBERTa-Large-MNLI 56 , which has been trained to predict entailment, or general-purpose LLMs such as GPT-3.5 (ref. 57 ), which can predict entailment given suitable prompts.

We then cluster sentences according to whether they bidirectionally entail each other using the algorithm presented in Extended Data Fig. 1 . Note that, to check if a sequence should be added to an existing cluster, it is sufficient to check if the sequence bidirectionally entails any of the existing sequences in that cluster (we arbitrarily pick the first one), given the transitivity of semantic equivalence. If a sequence does not share meaning with any existing cluster, we assign it its own cluster.

Computing the semantic entropy

Having determined the classes of generated sequences that mean the same thing, we can estimate the likelihood that a sequence generated by the LLM belongs to a given class by computing the sum of the probabilities of all the possible sequences of tokens which can be considered to express the same meaning as

Formally, this treats the output as a random variable whose event-space is the space of all possible meaning-classes, C , a sub- σ -algebra of the standard event-space S . We can then estimate the semantic entropy (SE) as the entropy over the meaning-distribution,

There is a complication which prevents direct computation: we do not have access to every possible meaning-class c . Instead, we can only sample c from the sequence-generating distribution induced by the model. To handle this, we estimate the expectation in equation ( 3 ) using a Rao–Blackwellized Monte Carlo integration over the semantic equivalence classes C ,

where \(P({C}_{i}| {\boldsymbol{x}})=\frac{P({c}_{i}| {\boldsymbol{x}})}{{\sum }_{c}P(c| {\boldsymbol{x}})}\) estimates a categorical distribution over the cluster meanings, that is, ∑ i P ( C i ∣ x ) = 1. Without this normalization step cluster ‘probabilities’ could exceed one because of length normalization, resulting in degeneracies. Equation ( 5 ) is the estimator giving our main method that we refer to as semantic entropy throughout the text.

For scenarios in which the sequence probabilities are not available, we propose a variant of semantic entropy which we call ‘discrete’ semantic entropy. Discrete semantic entropy approximates P ( C i ∣ x ) directly from the number of generations in each cluster, disregarding the token probabilities. That is, we approximate P ( C i ∣ x ) as \({\sum }_{1}^{M}\frac{{I}_{c={C}_{i}}}{M}\) , the proportion of all the sampled answers which belong to that cluster. Effectively, this just assumes that each output that was actually generated was equally probable—estimating the underlying distribution as the categorical empirical distribution. In the limit of M the estimator converges to equation ( 5 ) by the law of large numbers. We find that discrete semantic entropy results in similar performance empirically.

We provide a worked example of the computation of semantic entropy in Supplementary Note  1 .

Semantic entropy is designed to detect confabulations, that is, model outputs with arbitrary meaning. In our experiments, we use semantic uncertainty to predict model accuracy, demonstrating that confabulations make up a notable fraction of model mistakes. We further show that semantic uncertainty can be used to improve model accuracy by refusing to answer questions when semantic uncertainty is high. Last, semantic uncertainty can be used to give users a way to know when model generations are probably unreliable.

We use the datasets BioASQ 34 , SQuAD 33 , TriviaQA 32 , SVAMP 37 and NQ-Open 35 . BioASQ is a life-sciences question-answering dataset based on the annual challenge of the same name. The specific dataset we use is based on the QA dataset from Task B of the 2023 BioASQ challenge (11B). SQuAD is a reading comprehension dataset whose context passages are drawn from Wikipedia and for which the answers to questions can be found in these passages. We use SQuAD 1.1 which excludes the unanswerable questions added in v.2.0 that are deliberately constructed to induce mistakes so they do not in practice cause confabulations to occur. TriviaQA is a trivia question-answering dataset. SVAMP is a word-problem maths dataset containing elementary-school mathematical reasoning tasks. NQ-Open is a dataset of realistic questions aggregated from Google Search which have been chosen to be answerable without reference to a source text. For each dataset, we use 400 train examples and 400 test examples randomly sampled from the original larger dataset. Note that only some of the methods require training, for example semantic entropy does not use the training data. If the datasets themselves are already split into train and test (or validation) samples, we sample our examples from within the corresponding split.

All these datasets are free-form, rather than multiple choice, because this better captures the opportunities created by LLMs to produce free-form sentences as answers. We refer to this default scenario as our ‘sentence-length’ experiments. In Supplementary Note  7 , we also present results for confabulation detection in a ‘short-phrase’ scenario, in which we constrain model answers on these datasets to be as concise as possible.

To make the problems more difficult and induce confabulations, we do not provide the context passages for any of the datasets. When the context passages are provided, the accuracy rate is too high for these datasets for the latest generations of models to meaningfully study confabulations.

For sentence-length generations we use: Falcon 39 Instruct (7B and 40B), LLaMA 2 Chat 38 (7B, 13B and 70B) and Mistral 40 Instruct (7B).

In addition to reporting results for semantic entropy, discrete semantic entropy and naive entropy, we consider two strong baselines.

Embedding regression is a supervised baseline inspired by the P (IK) method 24 . In that paper, the authors fine-tune their proprietary LLM on a dataset of questions to predict whether the model would have been correct. This requires access to a dataset of ground-truth answers to the questions. Rather than fine-tuning the entire LLM in this way, we simply take the final hidden units and train a logistic regression classifier to make the same prediction. By contrast to their method, this is much simpler because it does not require fine-tuning the entire language model, as well as being more reproducible because the solution to the logistic regression optimization problem is not as seed-dependent as the fine-tuning procedure. As expected, this supervised approach performs well in-distribution but fails when the distribution of questions is different from that on which the classifier is trained.

The second baseline we consider is the P (True) method 24 , in which the model first samples M answers (identically to our semantic entropy approach) and then is prompted with the list of all answers generated followed by the highest probability answer and a question whether this answer is “(a) True” or “(b) False”. The confidence score is then taken to be the probability with which the LLM responds with ‘a’ to the multiple-choice question. The performance of this method is boosted with a few-shot prompt, in which up to 20 examples from the training set are randomly chosen, filled in as above, but then provided with the actual ground truth of whether the proposed answer was true or false. In this way, the method can be considered as supervised ‘in-context’ because it makes use of some ground-truth training labels but can be used without retraining the model. Because of context-size constraints, this method cannot fit a full 20 few-shot examples in the context when input questions are long or large numbers of generations are used. As a result, we sometimes have to reduce the number of few-shot examples to suit the context size and we note this in the  Supplementary Material .

Entailment estimator

Any NLI classification system could be used for our bidirectional entailment clustering algorithm. We consider two different kinds of entailment detector.

One option is to use an instruction-tuned LLM such as LLaMA 2, GPT-3.5 (Turbo 1106) or GPT-4 to predict entailment between generations. We use the following prompt:

We are evaluating answers to the question {question} Here are two possible answers: Possible Answer 1: {text1} Possible Answer 2: {text2} Does Possible Answer 1 semantically entail Possible Answer 2? Respond with entailment, contradiction, or neutral.

Alternatively, we consider using a language model trained for entailment prediction, specifically the DeBERTa-large model 56 fine-tuned on the NLI dataset MNLI 58 . This builds on past work towards paraphrase identification based on embedding similarity 59 , 60 and BERT-style models 61 , 62 . We template more simply, checking if DeBERTa predicts entailment between the concatenation of the question and one answer and the concatenation of the question and another answer. Note that DeBERTa-large is a relatively lightweight model with only 1.5B parameters which is much less powerful than most of the LLMs under study.

In Supplementary Note 2 , we carefully evaluate the benefits and drawbacks of these methods for entailment prediction. We settle on using GPT-3.5 with the above prompt, as its entailment predictions agree well with human raters and lead to good confabulation detection performance.

In Supplementary Note  3 , we provide a discussion of the computational cost and choosing the number of generations for reliable clustering.

Prompting templates

We use a simple generation template for all sentence-length answer datasets:

Answer the following question in a single brief but complete sentence. Question: {question} Answer:

Metrics and accuracy measurements

We use three main metrics to evaluate our method: AUROC, rejection accuracy and AURAC. Each of these is grounded in an automated factuality estimation measurement relative to the reference answers provided by the datasets that we use.

AUROC, rejection accuracy and AURAC

First, we use the AUROC curve, which measures the reliability of a classifier accounting for both precision and recall. The AUROC can be interpreted as the probability that a randomly chosen correct answer has been assigned a higher confidence score than a randomly chosen incorrect answer. For a perfect classifier, this is 1.

Second, we compute the ‘rejection accuracy at X %’, which is the question-answering accuracy of the model on the most-confident X % of the inputs as identified by the respective uncertainty method. If an uncertainty method works well, predictions on the confident subset should be more accurate than predictions on the excluded subset and the rejection accuracy should increase as we reject more inputs.

To summarize this statistic we compute the AURAC—the total area enclosed by the accuracies at all cut-off percentages X %. This should increase towards 1 as given uncertainty method becomes more accurate and better at detecting likely-inaccurate responses but it is more sensitive to the overall accuracy of the model than the AUROC metric.

In Supplementary Note  5 , we provide the unaggregated rejection accuracies for sentence-length generations.

Assessing accuracy

For the short-phrase-length generation setting presented in Supplementary Note  7 , we simply assess the accuracy of the generations by checking if the F1 score of the commonly used SQuAD metric exceeds 0.5. There are limitations to such simple scoring rules 63 but this method is widely used in practice and its error is comparatively small on these standard datasets.

For our default scenario, the longer sentence-length generations, this measure fails, as the overlap between the short reference answer and our long model answer is invariably too small. For sentence-length generations, we therefore automatically determine whether an answer to the question is correct or incorrect by using GPT-4 to compare the given answer to the reference answer. We use the template:

We are assessing the quality of answers to the following question: {question} The expected answer is: {reference answer} The proposed answer is: {predicted answer} Within the context of the question, does the proposed answer mean the same as the expected answer? Respond only with yes or no.

We make a small modification for datasets with several reference answers: line two becomes “The following are expected answers to this question:” and the final line asks “does the proposed answer mean the same as any of the expected answers?”.

In Supplementary Note 6 , we check the quality of our automated ground-truth evaluations against human judgement by hand. We find that GPT-4 gives the best results for determining model accuracy and thus use it in all our sentence-length experiments.

In this section we describe the application of semantic entropy to confabulation detection in longer model generations, specifically paragraph-length biographies.

We introduce a biography-generation dataset—FactualBio—available alongside this paper. FactualBio is a collection of biographies of individuals who are notable enough to have Wikipedia pages but not notable enough to have large amounts of detailed coverage, generated by GPT-4 (v.0613). To generate the dataset, we randomly sampled 21 individuals from the WikiBio dataset 64 . For each biography, we generated a list of factual claims contained in each biography using GPT-4, with 150 total factual claims (the total number is only coincidentally a round number). For each of these factual claims, we manually determined whether the claim was correct or incorrect. Out of 150 claims, 45 were incorrect. As before, we apply confabulation detection to detect incorrect model predictions, even though there may be model errors which are not confabulations.

Prompting and generation

Given a paragraph-length piece of LLM-generated text, we apply the following sequence of steps:

Automatically decompose the paragraph into specific factual claims using an LLM (not necessarily the same as the original).

For each factual claim, use an LLM to automatically construct Q questions which might have produced that claim.

For each question, prompt the original LLM to generate M answers.

For each question, compute the semantic entropy of the answers, including the original factual claim.

Average the semantic entropies over the questions to arrive at a score for the original factual claim.

We pursue this slightly indirect way of generating answers because we find that simply resampling each sentence creates variation unrelated to the uncertainty of the model about the factual claim, such as differences in paragraph structure.

We decompose the paragraph into factual claims using the following prompt:

Please list the specific factual propositions included in the answer above. Be complete and do not leave any factual claims out. Provide each claim as a separate sentence in a separate bullet point.

We found that we agreed with the decompositions in all cases in the dataset.

We then generate six questions for each of the facts from the decomposition. We generate these questions by prompting the model twice with the following:

Following this text: {text so far} You see the sentence: {proposition} Generate a list of three questions, that might have generated the sentence in the context of the preceding original text, as well as their answers. Please do not use specific facts that appear in the follow-up sentence when formulating the question. Make the questions and answers diverse. Avoid yes-no questions. The answers should not be a full sentence and as short as possible, e.g. only a name, place, or thing. Use the format “1. {question} – {answer}”.

These questions are not necessarily well-targeted and the difficulty of this step is the main source of errors in the procedure. We generate three questions with each prompt, as this encourages diversity of the questions, each question targeting a different aspect of the fact. However, we observed that the generated questions will sometimes miss obvious aspects of the fact. Executing the above prompt twice (for a total of six questions) can improve coverage. We also ask for brief answers because the current version of GPT-4 tends to give long, convoluted and highly hedged answers unless explicitly told not to.

Then, for each question, we generate three new answers using the following prompt:

We are writing an answer to the question “{user question}”. So far we have written: {text so far} The next sentence should be the answer to the following question: {question} Please answer this question. Do not answer in a full sentence. Answer with as few words as possible, e.g. only a name, place, or thing.

We then compute the semantic entropy over these answers plus the original factual claim. Including the original fact ensures that the estimator remains grounded in the original claim and helps detect situations in which the question has been interpreted completely differently from the original context. We make a small modification to handle the fact that GPT-4 generations often include refusals to answer questions. These refusals were not something we commonly observe in our experiments with LLaMA 2, Falcon or Mistral models. If more than half of the answers include one of the strings ‘not available’, ‘not provided’, ‘unknown’ or ‘unclear’ then we treat the semantic uncertainty as maximal.

We then average the semantic entropies for each question corresponding to the factual claim to get an entropy for this factual claim.

Despite the extra assumptions and complexity, we find that this method greatly outperforms the baselines.

To compute semantic entailment between the original claim and regenerated answers, we rely on the DeBERTa entailment prediction model as we find empirically that DeBERTa predictions result in higher train-set AUROC than other methods. Because DeBERTa has slightly lower recall than GPT-3.5/4, we use a modified set-up for which we say the answers mean the same as each other if at least one of them entails the other and neither is seen to contradict the other—a kind of ‘non-defeating’ bidirectional entailment check rather than true bidirectional entailment. The good performance of DeBERTa in this scenario is not surprising as both factual claims and regenerated answers are relatively short. We refer to Supplementary Notes 2 and 3 for ablations and experiments regarding our choice of entailment estimator for paragraph-length generations.

We implement two baselines. First, we implement a variant of the P (True) method, which is adapted to the new setting. For each factoid, we generate a question with answers in the same way as for semantic entropy. We then use the following prompt:

Question: {question} Here are some brainstormed ideas: {list of regenerated answers} Possible answer: {original answer} Is the possible answer true? Respond with “yes” or “no”.

As we cannot access the probabilities GPT-4 assigns to predicting ‘yes’ and ‘no’ as the next token, we approximate this using Monte Carlo samples. Concretely, we execute the above prompt ten times (at temperature 1) and then take the fraction of answers which was ‘yes’ as our unbiased Monte Carlo estimate of the token probability GPT-4 assigns to ‘yes’.

As a second, simpler, baseline we check if the model thinks the answer is true. We simply ask:

Following this text: {text so far} You see this statement: {proposition} Is it likely that the statement is true? Respond with ‘yes’ or ‘no’.

It is interesting that this method ought to perform very well if we think that the model has good ‘self-knowledge’ (that is, if “models mostly know what they don’t know” 24 ) but in fact semantic entropy is much better at detecting confabulations.

Data availability

The data used for the short-phrase and sentence-length generations are publicly available and the released code details how to access it. We release a public version of the FactualBio dataset as part of the code base for reproducing the paragraph-length experiments.

Code availability

We release all code used to produce the main experiments. The code for short-phrase and sentence-length experiments can be found at github.com/jlko/semantic_uncertainty and https://doi.org/10.5281/zenodo.10964366 (ref. 65 ). The code for paragraph-length experiments can be found at github.com/jlko/long_hallucinations and https://doi.org/10.5281/zenodo.10964366 (ref. 65 ).

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Acknowledgements

We thank G. Irving, K. Perlin, J. Richens, L. Rimell and M. Turpin for their comments or discussion related to this work. We thank K. Handa for his help with the human evaluation of our automated accuracy assessment. We thank F. Bickford Smith and L. Melo for their code review. Y.G. is supported by a Turing AI Fellowship funded by the UK government’s Office for AI, through UK Research and Innovation (grant reference EP/V030302/1), and delivered by the Alan Turing Institute.

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These authors contributed equally: Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn

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OATML, Department of Computer Science, University of Oxford, Oxford, UK

Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn & Yarin Gal

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S.F. led the work from conception to completion and proposed using bidirectional entailment to cluster generations as a way of computing entropy in LLMs. He wrote the main text, most of the Methods and Supplementary Information and prepared most of the figures. J.K. improved the mathematical formalization of semantic entropy; led the extension of semantic entropy to sentence- and paragraph-length generations; wrote the code for, and carried out, all the experiments and evaluations; wrote much of the Methods and Supplementary Information and prepared drafts of many figures; and gave critical feedback on the main text. L.K. developed the initial mathematical formalization of semantic entropy; wrote code for, and carried out, the initial experiments around semantic entropy and its variants which demonstrated the promise of the idea and helped narrow down possible research avenues to explore; and gave critical feedback on the main text. Y.G. ideated the project, proposing the idea to differentiate semantic and syntactic diversity as a tool for detecting hallucinations, provided high-level guidance on the research and gave critical feedback on the main text; he runs the research laboratory in which the work was carried out.

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Correspondence to Sebastian Farquhar .

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S.F. is currently employed by Google DeepMind and L.K. by OpenAI. For both, this paper was written under their University of Oxford affiliation. The remaining authors declare no competing interests.

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Nature thanks Mirella Lapata and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended data fig. 1 algorithm outline for bidirectional entailment clustering..

Given a set of outputs in response to a context, the bidirectional entailment answer returns a set of sets of outputs which have been classified as sharing a meaning.

Supplementary information

Supplementary information.

Supplementary Notes 1–7, Figs. 1–10, Tables 1–4 and references. Includes, worked example for semantic entropy calculation, discussion of limitations and computational cost of entailment clustering, ablation of entailment prediction and clustering methods, discussion of automated accuracy assessment, unaggregated results for sentence-length generations and further results for short-phrase generations.

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Farquhar, S., Kossen, J., Kuhn, L. et al. Detecting hallucinations in large language models using semantic entropy. Nature 630 , 625–630 (2024). https://doi.org/10.1038/s41586-024-07421-0

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Received : 17 July 2023

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Published : 19 June 2024

Issue Date : 20 June 2024

DOI : https://doi.org/10.1038/s41586-024-07421-0

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