research human behavior examples

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

35 Human Behavior Research Topics & Questions

Useful information: Does a research paper need a thesis ?

• 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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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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• 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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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.

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• 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.

LEARN ABOUT: Theoretical Research

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80 fascinating psychology research questions for your next project

Last updated

15 February 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Short on time? Get an AI generated summary of this article instead

Psychology research is essential for furthering our understanding of human behavior and improving the diagnosis and treatment of psychological conditions.

When psychologists know more about how different social and cultural factors influence how humans act, think, and feel, they can recommend improvements to practices in areas such as education, sport, healthcare, and law enforcement.

Below, you will find 80 research question examples across 16 branches of psychology. First, though, let’s look at some tips to help you select a suitable research topic.

• How to choose a good psychology research topic

Psychology has many branches that break down further into topics. Choosing a topic for your psychology research paper can be daunting because there are so many to choose from. It’s an important choice, as the topic you select will open up a range of questions to explore.

The tips below can help you find a psychology research topic that suits your skills and interests.

Tip #1: Select a topic that interests you

Passion and interest should fuel every research project. A topic that fascinates you will most likely interest others as well. Think about the questions you and others might have and decide on the issues that matter most. Draw on your own interests, but also keep your research topical and relevant to others.

Don’t limit yourself to a topic that you already know about. Instead, choose one that will make you want to know more and dig deeper. This will keep you motivated and excited about your research.

Tip #2: Choose a topic with a manageable scope

If your topic is too broad, you can get overwhelmed by the amount of information available and have trouble maintaining focus. On the other hand, you may find it difficult to find enough information if you choose a topic that is too narrow.

To determine if the topic is too broad or too narrow, start researching as early as possible. If you find there’s an overwhelming amount of research material, you’ll probably need to narrow the topic down. For example, instead of researching the general population, it might be easier to focus on a specific age group. Ask yourself what area of the general topic interests you most and focus on that.

If your scope is too narrow, try to generalize or focus on a larger related topic. Expand your search criteria or select additional databases for information. Consider if the topic is too new to have much information published on it as well.

Tip #3: Select a topic that will produce useful and relevant insights

Doing some preliminary research will reveal any existing research on the topic. If there is existing research, will you be able to produce new insights? You might need to focus on a different area or see if the existing research has limitations that you can overcome.

Bear in mind that finding new information from which to draw fresh insights may be impossible if your topic has been over-researched.

You’ll also need to consider whether your topic is relevant to current trends and needs. For example, researching psychology topics related to social media use may be highly relevant today.

• 80 psychology research topics and questions

Psychology is a broad subject with many branches and potential areas of study. Here are some of them:

Developmental

Personality

Experimental

Organizational

Educational

Neuropsychology

Controversial topics

Below we offer some suggestions on research topics and questions that can get you started. Keep in mind that these are not all-inclusive but should be personalized to fit the theme of your paper.

Social psychology research topics and questions

Social psychology has roots as far back as the 18th century. In simple terms, it’s the study of how behavior is influenced by the presence and behavior of others. It is the science of finding out who we are, who we think we are, and how our perceptions affect ourselves and others. It looks at personalities, relationships, and group behavior.

Here are some potential research questions and paper titles for this topic:

How does social media use impact perceptions of body image in male adolescents?

2. Is childhood bullying a risk factor for social anxiety in adults?

Is homophobia in individuals caused by genetic or environmental factors?

What is the most important psychological predictor of a person’s willingness to donate to charity?

Does a person’s height impact how other people perceive them? If so, how?

Cognitive psychology research questions

Cognitive psychology is the branch that focuses on the interactions of thinking, emotion, creativity, and problem-solving. It also explores the reasons humans think the way they do.

This topic involves exploring how people think by measuring intelligence, thoughts, and cognition. 

Here are some research question ideas:

6. Is there a link between chronic stress and memory function?

7. Can certain kinds of music trigger memories in people with memory loss?

8. Do remote meetings impact the efficacy of team decision-making?

9. Do word games and puzzles slow cognitive decline in adults over the age of 80?

10. Does watching television impact a child’s reading ability?

Developmental psychology research questions

Developmental psychology is the study of how humans grow and change over their lifespan. It usually focuses on the social, emotional, and physical development of babies and children, though it can apply to people of all ages. Developmental psychology is important for understanding how we learn, mature, and adapt to changes.

Here are some questions that might inspire your research:

11. Does grief accelerate the aging process?

12. How do parent–child attachment patterns influence the development of emotion regulation in teenagers?

13. Does bilingualism affect cognitive decline in adults over the age of 70?

14. How does the transition to adulthood impact decision-making abilities

15. How does early exposure to music impact mental health and well-being in school-aged children?

Personality psychology research questions

Personality psychology studies personalities, how they develop, their structures, and the processes that define them. It looks at intelligence, disposition, moral beliefs, thoughts, and reactions.

The goal of this branch of psychology is to scientifically interpret the way personality patterns manifest into an individual’s behaviors. Here are some example research questions:

16. Nature vs. nurture: Which impacts personality development the most?

17. The role of genetics on personality: Does an adopted child take on their biological parents’ personality traits?

18. How do personality traits influence leadership styles and effectiveness in organizational settings?

19. Is there a relationship between an individual’s personality and mental health?

20. Can a chronic illness affect your personality?

Abnormal psychology research questions

As the name suggests, abnormal psychology is a branch that focuses on abnormal behavior and psychopathology (the scientific study of mental illness or disorders).

Abnormal behavior can be challenging to define. Who decides what is “normal”? As such, psychologists in this area focus on the level of distress that certain behaviors may cause, although this typically involves studying mental health conditions such as depression, obsessive-compulsive disorder (OCD), and phobias.

Here are some questions to consider:

21. How does technology impact the development of social anxiety disorder?

22. What are the factors behind the rising incidence of eating disorders in adolescents?

23. Are mindfulness-based interventions effective in the treatment of PTSD?

24. Is there a connection between depression and gambling addiction?

25. Can physical trauma cause psychopathy?

Clinical psychology research questions

Clinical psychology deals with assessing and treating mental illness or abnormal or psychiatric behaviors. It differs from abnormal psychology in that it focuses more on treatments and clinical aspects, while abnormal psychology is more behavioral focused.

This is a specialty area that provides care and treatment for complex mental health conditions. This can include treatment, not only for individuals but for couples, families, and other groups. Clinical psychology also supports communities, conducts research, and offers training to promote mental health. This category is very broad, so there are lots of topics to explore.

Below are some example research questions to consider:

26. Do criminals require more specific therapies or interventions?

27. How effective are selective serotonin reuptake inhibitors in treating mental health disorders?

28. Are there any disadvantages to humanistic therapy?

29. Can group therapy be more beneficial than one-on-one therapy sessions?

30. What are the factors to consider when selecting the right treatment plan for patients with anxiety?

Experimental psychology research questions

Experimental psychology deals with studies that can prove or disprove a hypothesis. Psychologists in this field use scientific methods to collect data on basic psychological processes such as memory, cognition, and learning. They use this data to test the whys and hows of behavior and how outside factors influence its creation.

Areas of interest in this branch relate to perception, memory, emotion, and sensation. The below are example questions that could inspire your own research:

31. Do male or female parents/carers have a more calming influence on children?

32. Will your preference for a genre of music increase the more you listen to it?

33. What are the psychological effects of posting on social media vs. not posting?

34. How is productivity affected by social connection?

35. Is cheating contagious?

Organizational psychology research questions

Organizational psychology studies human behavior in the workplace. It is most frequently used to evaluate an employee, group, or a company’s organizational dynamics. Researchers aim to isolate issues and identify solutions.

This area of study can be beneficial to both employees and employers since the goal is to improve the overall work environment and experience. Researchers apply psychological principles and findings to recommend improvements in performance, communication, job satisfaction, and safety. 

Some potential research questions include the following:

36. How do different leadership styles affect employee morale?

37. Do longer lunch breaks boost employee productivity?

38. Is gender an antecedent to workplace stress?

39. What is the most effective way to promote work–life balance among employees?

40. How do different organizational structures impact the effectiveness of communication, decision-making, and productivity?

Forensic psychology research questions

Some questions to consider exploring in this branch of psychology are:

41. How does incarceration affect mental health?

42. Is childhood trauma a driver for criminal behavior during adulthood?

43. Are people with mental health conditions more likely to be victims of crimes?

44. What are the drivers of false memories, and how do they impact the justice system?

45. Is the media responsible for copycat crimes?

Educational psychology research questions

Educational psychology studies children in an educational setting. It covers topics like teaching methods, aptitude assessment, self-motivation, technology, and parental involvement.

Research in this field of psychology is vital for understanding and optimizing learning processes. It informs educators about cognitive development, learning styles, and effective teaching strategies.

Here are some example research questions:

46. Are different teaching styles more beneficial for children at different times of the day?

47. Can listening to classical music regularly increase a student’s test scores?

48. Is there a connection between sugar consumption and knowledge retention in students?

49. Does sleep duration and quality impact academic performance?

50. Does daily meditation at school influence students’ academic performance and mental health?

Sports psychology research question examples

Sport psychology aims to optimize physical performance and well-being in athletes by using cognitive and behavioral practices and interventions. Some methods include counseling, training, and clinical interventions.

Research in this area is important because it can improve team and individual performance, resilience, motivation, confidence, and overall well-being

Here are some research question ideas for you to consider:

51. How can a famous coach affect a team’s performance?

52. How can athletes control negative emotions in violent or high-contact sports?

53. How does using social media impact an athlete’s performance and well-being?

54. Can psychological interventions help with injury rehabilitation?

55. How can mindfulness practices boost sports performance?

Cultural psychology research question examples

The premise of this branch of psychology is that mind and culture are inseparable. In other words, people are shaped by their cultures, and their cultures are shaped by them. This can be a complex interaction.

Cultural psychology is vital as it explores how cultural context shapes individuals’ thoughts, behaviors, and perceptions. It provides insights into diverse perspectives, promoting cross-cultural understanding and reducing biases.

Here are some ideas that you might consider researching:

56. Are there cultural differences in how people perceive and deal with pain?

57. Are different cultures at increased risk of developing mental health conditions?

58. Are there cultural differences in coping strategies for stress?

59. Do our different cultures shape our personalities?

60. How does multi-generational culture influence family values and structure?

Health psychology research question examples

Health psychology is a crucial field of study. Understanding how psychological factors influence health behaviors, adherence to medical treatments, and overall wellness enables health experts to develop effective interventions and preventive measures, ultimately improving health outcomes.

Health psychology also aids in managing stress, promoting healthy behaviors, and optimizing mental health, fostering a holistic approach to well-being.

Here are five ideas to inspire research in this field:

61. How can health psychology interventions improve lifestyle behaviors to prevent cardiovascular diseases?

62. What role do social norms play in vaping among adolescents?

63. What role do personality traits play in the development and management of chronic pain conditions?

64. How do cultural beliefs and attitudes influence health-seeking behaviors in diverse populations?

65. What are the psychological factors influencing the adherence to preventive health behaviors, such as vaccination and regular screenings?

Neuropsychology research paper question examples

Neuropsychology research explores how a person’s cognition and behavior are related to their brain and nervous system. Researchers aim to advance the diagnosis and treatment of behavioral and cognitive effects of neurological disorders.

Researchers may work with children facing learning or developmental challenges, or with adults with declining cognitive abilities. They may also focus on injuries or illnesses of the brain, such as traumatic brain injuries, to determine the effect on cognitive and behavioral functions.

Neuropsychology informs diagnosis and treatment strategies for conditions such as dementia, traumatic brain injuries, and psychiatric disorders. Understanding the neural basis of behavior enhances our ability to optimize cognitive functioning, rehabilitate people with brain injuries, and improve patient care.

Here are some example research questions to consider:

66. How do neurotransmitter imbalances in specific brain regions contribute to mood disorders such as depression?

67. How can a traumatic brain injury affect memory?

68. What neural processes underlie attention deficits in people with ADHD?

69. Do medications affect the brain differently after a traumatic brain injury?

70. What are the behavioral effects of prolonged brain swelling?

Psychology of religion research question examples

The psychology of religion is a field that studies the interplay between belief systems, spirituality, and mental well-being. It explores the application of the psychological methods and interpretive frameworks of religious traditions and how they relate to both religious and non-religious people.

Psychology of religion research contributes to a holistic understanding of human experiences. It fosters cultural competence and guides therapeutic approaches that respect diverse spiritual beliefs.

Here are some example research questions in this field:

71. What impact does a religious upbringing have on a child’s self-esteem?

72. How do religious beliefs shape decision-making and perceptions of morality?

73. What is the impact of religious indoctrination?

74. Is there correlation between religious and mindfulness practices?

75. How does religious affiliation impact attitudes towards mental health treatment and help-seeking behaviors?

Controversial topics in psychology research question examples

Some psychology topics don’t fit into any of the subcategories above, but they may still be worthwhile topics to consider. These topics are the ones that spark interest, conversation, debate, and disagreement. They are often inspired by current issues and assess the validity of older research.

Consider some of these research question examples:

76. How does the rise in on-screen violence impact behavior in adolescents.

77. Should access to social media platforms be restricted in children under the age of 12 to improve mental health?

78. Are prescription mental health medications over-prescribed in older adults? If so, what are the effects of this?

79. Cognitive biases in AI: what are the implications for decision-making?

80. What are the psychological and ethical implications of using virtual reality in exposure therapy for treating trauma-related conditions?

• Inspiration for your next psychology research project

You can choose from a diverse range of research questions that intersect and overlap across various specialties.

From cognitive psychology to clinical studies, each inquiry contributes to a deeper understanding of the human mind and behavior. Importantly, the relevance of these questions transcends individual disciplines, as many findings offer insights applicable across multiple areas of study.

As health trends evolve and societal needs shift, new topics emerge, fueling continual exploration and discovery. Diving into this ever-changing and expanding area of study enables you to navigate the complexities of the human experience and pave the way for innovative solutions to the challenges of tomorrow.

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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-2/ 15 Green Flags in a Relationship
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Signs you're Burnt Out, Not Lazy
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Toxic Things Parents Say to their Children
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Red Flags Early in a Relationship

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What is Human Behavior: A Deep Dive into Our Actions and Reactions

Often, I find myself intrigued by the complexities of human nature. Human behavior , a subject as vast as it is fascinating, has long been a topic that captivates my interest. It’s this intricate web of emotions, actions, and reactions that sets us apart from other species on our planet.

Now you might ask, “What exactly is human behavior?” Well, at its core, it’s how we act and interact with others and the world around us. It encompasses everything from our thoughts and feelings to our physical actions. From a simple smile to complex decision-making processes – all these are facets of human behavior.

There’s no denying that understanding human behavior can be quite challenging. After all, we’re not just talking about individual actions here but also societal norms and influences. Yet it’s this complexity that makes studying human behavior both essential and intriguing in equal measure.

Understanding Human Behavior: An Overview

Delving into the realm of human behavior, I’m amazed by its complexity and diversity. Simply put, human behavior refers to the array of every physical action and observable emotion associated with individuals. From conscious decisions like choosing what clothes to wear, to subconscious impulses like reaching for a snack when you’re bored – it’s all part of our complex behavioral patterns.

What fascinates me most about human behavior is how it’s influenced by a blend of both nature and nurture. On one hand, we’ve got genetic predispositions that shape aspects of our behavior. For instance, studies have shown that traits such as introversion or extraversion can be inherited! Here’s an interesting statistic:

On the other hand, there’s no denying the powerful role environment plays in molding our behaviors. Factors such as culture, upbringing, peer influence – they all leave indelible imprints on us.

Here are some key environmental factors affecting human behavior:

• Cultural norms
• Early childhood experiences
• Peer influences
• Socioeconomic status

But let’s not forget about individual differences either! Each person has unique life experiences resulting in different perceptions and responses to similar situations. That’s why even identical twins raised under same conditions might exhibit distinct behavioral patterns.

Now you’d think understanding human behavior would be as easy as piecing together these factors but it’s not quite so black-and-white. The human mind isn’t a static entity; it evolves over time through learning and experience. It’s an intricate maze where biological instincts intertwine with learned behaviors creating a fascinating tapestry called ‘Human Behavior’.

So when we talk about understanding human behavior, we’re really talking about understanding this dynamic interplay between biology and environment, between instinct and learning – each shaping us in their own unique ways. It’s a fascinating journey, one that I’m excited to continue exploring with you as we dive deeper into our subsequent sections.

The Role of Genetics in Human Behavior

I’ve always found it fascinating how much our genes can influence who we are. From the color of our eyes to our susceptibility to certain diseases, genetics play a huge role in human behavior.

Let’s take an example – identical twins. They share 100% of their genetic material, yet they’re not carbon copies of each other when it comes to personality or behavior. Studies suggest that about 50% of the variation in human behavior can be attributed to genetic factors. So while they might have the same hair color or similar facial features, one twin could be outgoing and adventurous while the other is introverted and cautious.

Here are some key points on how genetics influence human behavior:

• Genetic Predispositions : Some individuals are genetically predisposed towards certain behaviors. For instance, research indicates that there might be a “novelty-seeking” gene which makes people more inclined towards risky activities.
• Mental Health Disorders : Conditions like schizophrenia or bipolar disorder have been linked to specific genetic variants.
• Personality Traits : Traits such as extraversion or conscientiousness have been associated with particular genes.

But it’s important not to oversimplify these connections. Our behaviors aren’t dictated by our genes alone – environment also plays a critical role. Known as gene-environment interaction, this concept holds that your genes may make you more susceptible to certain influences, but your environment determines whether these potentialities will manifest.

For example, if you’ve got a genetic predisposition for alcoholism but grow up in an environment where alcohol isn’t prevalent or easily accessible, chances are you won’t develop an alcohol addiction.

So yes, while genetics do play a significant role in human behavior, it’s not the whole story. Understanding our genetic makeup can give us insights into why we behave as we do but remember, our environment and experiences are equally important players in shaping who we are.

Environmental Influences on Behavioral Patterns

The environment plays a significant role in shaping our behavior. It’s not just our genes that determine how we act, but also our surroundings. Both physical and social environments have profound effects on our behaviors , actions, and decisions.

Physical environments can directly impact human behaviors. For example, studies show that people living in urban areas are more likely to suffer from stress-related disorders than those residing in rural settings. This is due largely to factors such as noise pollution, overcrowding, and high crime rates prevalent in city life.

Our social environment too has its sway over us. We’re influenced heavily by the attitudes, behaviors, and beliefs of the people around us – this is referred to as ‘social influence’. The classic experiment by Solomon Asch on conformity provides an intriguing example here. In his study, individuals tended to agree with incorrect answers given by a group merely to fit in.

In addition to these broad categories:

• Cultural norms and societal expectations shape behavior significantly.
• Educational institutions mold student behavior through rules and discipline.
• Workplaces set standards for professional conduct influencing employee behavior.

It’s crucial then for us to acknowledge these environmental influences when studying human behavior. Understanding them not only helps interpret why people behave the way they do but also aids in creating healthier environments conducive for positive behavioral patterns.

Psychological Perspectives on Human Actions

Diving into the realm of human behavior, it’s essential to understand the psychological perspectives that shape our actions. These perspectives provide a framework for understanding why we act the way we do.

One major perspective is the cognitive approach. It’s rooted in the belief that our behavior is driven by internal mental processes. Think about when you’re solving a tough math problem – your mind runs through various equations and strategies before landing on an answer. This internal thought process directly influences your external behavior.

Another significant viewpoint is the biological perspective, which argues that our genetics and physiological makeup govern our actions. Ever noticed how certain traits run in families? Maybe you’ve inherited your father’s short temper or your mother’s meticulous nature. That’s biology at work!

Behaviorists, on the other hand, argue it’s all about learning from experience. They suggest that we act based on what we’ve previously learned from similar situations – essentially, past experiences shape current behaviors.

Then there’s the psychodynamic perspective, largely influenced by Sigmund Freud’s theories, which asserts that unconscious drives and instincts influence our actions more than anything else.

Let me also mention humanistic psychology – this perspective focuses on personal growth and self-fulfillment as key drivers of behavior.

Clearly, there are many ways to approach human behavior:

• The cognitive perspective looks at mental processes.
• The biological perspective focuses on genetic and physiological factors.
• Behaviorism emphasizes learning from experience.
• Psychodynamics looks at unconscious drives.
• Humanistic psychology prioritizes personal growth and self-fulfillment.

In reality, each of these perspectives offers valuable insights into why we behave as we do – they’re all pieces of a complex puzzle!

Cultural Factors Shaping Human Conduct

Culture plays a pivotal role in shaping our behaviors. It’s the invisible hand that sculpts our actions, reactions, and interactions. Every society has unique norms and values that serve as a guide for acceptable conduct. These cultural standards are absorbed from an early age, shaping how we perceive the world and behave within it.

Take communication styles as an example. In some cultures, directness is valued while other societies might prefer more indirect ways of expression. For instance, people in Germany are known for their straightforwardness – they say what they mean without beating around the bush. On the other hand, Japanese culture tends to value harmony and avoiding confrontation which often leads to more subtle methods of conveying messages.

Even our eating habits can be influenced by cultural factors! Some societies view food purely as sustenance while others consider meals a time for socializing and bonding with family or friends.

Rituals and traditions also have a strong impact on behavior patterns across different societies. In India, touching elders’ feet is seen as a sign of respect – it’s ingrained in their societal norms. Conversely, such an act would seem quite outlandish in Western cultures where personal space is highly valued.

• Germany : Direct communication
• Japan : Indirect communication
• India : Shows respect through physical gestures

Lastly, let’s not forget about cultural attitudes towards gender roles. They greatly influence how men and women behave within their communities: from attire choices to career aspirations. For example:

• In Saudi Arabia: Women were only recently allowed to drive vehicles.
• In Norway: Emphasis on gender equality results in more balanced participation across various sectors including politics and business.

Understanding these cultural factors gives us valuable insights into the diverse tapestry of human behavior. It’s fascinating to see how deeply our surroundings influence us, molding our actions in ways we often don’t even notice.

Human Behavior in Social Contexts

Diving headfirst into the world of human behavior, it’s critical to understand the role social contexts play. They’re not just backdrops; these contexts shape our actions and responses in profound ways.

Let’s take a look at conformity, for example. It’s one human behavior that prominently manifests itself in group settings. Conformity is when we adjust our behaviors or attitudes to align with a particular group norm. Remember high school? We’ve all experienced peer pressure at some point, haven’t we?

Social loafing provides another interesting angle on how our behavior changes in social situations. This concept refers to individuals exerting less effort when working within a group compared to working alone. Now that brings back memories of those dreaded group projects, doesn’t it?

Of course, I can’t forget about altruism – an act of helping others without expecting anything in return. Research shows many factors like empathy and moral reasoning influence altruistic behaviors.

But remember this: social context isn’t always about groups! Even one-on-one interactions significantly impact human behavior. For instance, attraction plays a huge role in how we behave around potential mates or friends.

• Attraction towards potential mates often leads us to display positive traits and hide negative ones.
• Similarly, interaction with friends frequently involves sharing interests and experiences.

So you see folks, human behavior is as much influenced by its environment as by internal factors like personality or emotions. Our actions are constantly shaped by the people around us – whether we’re part of a crowd or engaged in an intimate conversation.

Changing Behaviors: The Impact of Education and Therapy

It’s fascinating to observe how education and therapy can dramatically shift human behaviors. I’m sure you’ve heard the saying, “Knowledge is power.” Well, it turns out that this age-old adage has a solid basis in reality. When people learn new information or gain insights into their behavior patterns, they’re often empowered to make positive changes.

Take smoking cessation for instance. It’s been shown that educational interventions can significantly increase the likelihood of successful quit attempts. A study conducted by the American Lung Association found that smokers who received educational materials were 1.5 times more likely to stop smoking compared with those who didn’t receive any additional resources.

Another example is cognitive-behavioral therapy (CBT), a type of psychotherapy that helps individuals understand and change thought patterns leading to harmful actions or emotions. Research shows CBT can effectively reduce symptoms of various mental health conditions including anxiety and depression.

• According to a review published in the Journal of Clinical Psychiatry, about half of individuals with varying types of anxiety disorders remained symptom-free after undergoing CBT.
• Similarly, in treating depression, data suggests that up to two-thirds of patients show significant improvement after engaging in CBT.

These examples illustrate just how powerful education and therapy can be when it comes to altering human behaviors.

However, it’s important not to overlook the role societal influences play in shaping our actions and attitudes as well – from family dynamics and cultural norms right down to economic factors. Undoubtedly, changing human behavior is complex but through education and therapeutic interventions we’re able hone-in on individual factors aiding us on our journey towards understanding—and ultimately influencing—human behavior better.

Conclusion: Decoding the Complexity of Human Behavior

Deciphering human behavior isn’t a walk in the park. Not only does it take years of study, but it also requires an understanding that people and their actions are multi-layered, complicated, and ever-changing.

To wrap things up, let’s acknowledge that we’ve barely scratched the surface when it comes to fully comprehending human behavior. However, I believe this article has given you a solid starting point. We’ve delved into various aspects such as biological influences, cultural impacts and psychological factors.

• Our genes don’t solely define us; our behaviors are shaped by both nature and nurture.
• The society we live in greatly affects our actions and choices.
• Our mental state plays a critical role in how we behave.

As we continue to unravel the intricate web of human conduct, one thing remains clear – there’s no one-size-fits-all explanation for why we do what we do. It’s this complexity that makes studying human behavior so fascinating yet challenging at the same time.

In conclusion, I’d say that while understanding every facet of human behavior might seem like an uphill battle now, with ongoing research and advancements in neuroscience coupled with psychology, I’m confident that we’re on track towards gaining deeper insights into the intricacies of mankind.

After all, unlocking these secrets not only benefits me or you but humanity as a whole. With knowledge comes power – power to change patterns for better decision making, fostering healthier relationships and creating societies where everyone feels understood and valued.

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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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• Published: 20 March 2023

A manifesto for applying behavioural science

• Michael Hallsworth   ORCID: orcid.org/0000-0002-7868-4727 1  

Nature Human Behaviour volume  7 ,  pages 310–322 ( 2023 ) Cite this article

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• Human behaviour

Recent years have seen a rapid increase in the use of behavioural science to address the priorities of public and private sector actors. There is now a vibrant ecosystem of practitioners, teams and academics building on each other’s findings across the globe. Their focus on robust evaluation means we know that this work has had an impact on important issues such as antimicrobial resistance, educational attainment and climate change. However, several critiques have also emerged; taken together, they suggest that applied behavioural science needs to evolve further over its next decade. This manifesto for the future of applied behavioural science looks at the challenges facing the field and sets out ten proposals to address them. Meeting these challenges will mean that behavioural science is better equipped to help to build policies, products and services on stronger empirical foundations—and thereby address the world’s crucial challenges.

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There has been “a remarkable increase in behavioural studies and interventions in public policy on a global scale” over the past 15 years 1 . This growth has been built on developments taking place over many preceding decades. One was the increasing empirical evidence of the importance of non-conscious drivers of behaviour. While psychologists have studied these drivers since at least as far back as the work of William James and Wilhelm Wundt in the nineteenth century, they received renewed attention from the research agenda that showed how “heuristics and biases” influence judgement and decision-making 2 . These and other studies led many psychologists to converge on dual-process theories of behaviour that proposed that rapid, intuitive and non-conscious cognitive processes sit alongside deliberative, reflective and self-aware ones 3 .

These theories challenged explanations that foregrounded the role of conscious attitudes, motivations and intentions in determining actions 4 . One result was the creation of the field of behavioural economics, which developed new explanations for why observed behaviour diverged from existing economic models 5 . For example, the concept of “mental accounting” showed how people assign money to certain purposes and—contrary to standard economic theory—are reluctant to repurpose those sums, even when they might benefit from doing so 6 .

Behavioural economics may represent only one strand of applied behavioural science, but it has attracted substantial attention. By the mid-2000s, these advances had an increasingly receptive audience among some governments and policymakers 7 . The publication of the book Nudge in 2008 responded to this demand by using the evidence mentioned earlier to create practical policy solutions (Box 1 ) 8 . Then, in 2010, the UK government set up its Behavioural Insights Team 9 . The creation of the Behavioural Insights Team is notable because it became “a paradigmatic example for the translation of behavioural insights into public policy” that acted as “a blueprint for the establishment of similar units elsewhere” 10 , 11 , 12 . Similar initiatives were adopted by many public sector bodies at the local, national and supra-national levels and by private companies large and small 1 , 11 , 13 , 14 . The Organisation for Economic Development and Cooperation has labelled this creation of more than 200 dedicated public entities a “paradigm shift” 15 that shows that applied behavioural science has “taken root in many ways across many countries around the world and across a wide range of sectors and policy areas” 16 .

This history is necessarily selective; it does not attempt to cover the full range of work in the behavioural sciences. Rather, my focus is on the main ways that approaches often grouped under the term ‘behavioural insights’ have been applied to practical issues in the public and private sectors over the past 15 years 17 (see Box 1 for definitions of these and other terms). These approaches have been adopted in both developed and developing economies, and their precise forms of implementation have varied from context to context 18 . However, a crucial point to emphasize is that they have gone far beyond the self-imposed limits of nudges, even if that label is still used (often unhelpfully) as a blanket term. Instead, a broader agenda has emerged that explores how behavioural science can be integrated into core public and private sector activities such as regulation, taxation, strategy and operations. This broader agenda is reflected in the creation of research programmes on “behavioural public policy” 19 or “behavioural public administration” 20 .

Proponents of these approaches can point to improved outcomes in many areas, including health 21 , education 22 , sustainability 23 and criminal justice 24 . Yet criticisms have emerged alongside these successes. For example, there is an ongoing debate about how publication bias may have inflated the published effect sizes of nudge interventions 25 , 26 . Other criticisms target the goals, assumptions and techniques associated with recent applications of behavioural science (Box 2 ).

This Perspective attempts to respond to these criticisms by setting out an agenda to ensure that applied behavioural science can fulfil its potential in the coming decades. It does so by offering ten proposals, as summarized in Table 1 . These proposals fall into three categories: scope (the range and scale of issues to which behavioural science is applied), methods (the techniques and resources that behavioural science deploys) and values (the principles, ideals and standards of conduct that behavioural scientists adopt). These proposals are the product of a non-systematic review of relevant literature and my experience of applying behavioural science. They are not an attempt to represent expert consensus; they aim to provoke debate as well as agreement.

Figure 1 shows how each proposal aims to address one or more of the criticisms set out in Box 2 . Figure 1 also indicates how responsibilities for implementing the proposals are allocated among four major groups in the behavioural science ecosystem: practitioners (individuals or teams who apply behavioural science findings in practical settings), the clients who commission these practitioners (for example, public or private sector organizations), academics working in the behavioural sciences (including disciplines such as anthropology, economics and sociology) and funders who support the work of these academics. These groups constitute the ‘we’ referred to in the rest of the paper, which summarizes a full-length, in-depth report available at www.bi.team .

The left side shows common criticisms made of the behavioural insights approach. The middle column presents ten proposals to improve the way behavioural science is applied. These proposals are organized into three categories (scope, methods and values), which are represented by red, blue and yellow, respectively. The arrows from the criticisms to the proposals show which of the latter attempt to address the former. The matrix on the right shows the four main groups involved with implementing the proposals: practitioners, clients, academics and funders. The dots in each column indicate that the relevant group will need to make a substantive contribution to achieving the goals of the proposal in the corresponding row.

Box 1 Glossary of main terms

Behavioural science . In its broadest sense, a discipline that uses scientific methods to generate and test theories that explain and predict the behaviour of individuals, groups and populations. This piece focuses particularly on the implications of dual-process theories of behaviour. Behavioural science is different from ‘the behavioural sciences’, which refers to a broader group of any scientific disciplines that study behaviour.

Behavioural insights . The application of findings from behavioural science to analyse and address practical issues in real-world settings, usually coupled with a rigorous evaluation of the effects of any interventions. In the current piece, this term is used interchangeably with ‘applied behavioural science’.

Behavioural economics . The application of findings from behavioural science to the field of economics to create explanations for economic behaviour that often diverge from the principles of neoclassical economic theory.

Nudge . The design of choices so that non-conscious cognitive processes lead individuals to select the option that leaves them better off, as judged by themselves. Nudges do not involve coercion or any substantial change to economic incentives, leaving people with a meaningful ability to choose a different option from the one that the choice architect intends.

Box 2 Criticisms of the behavioural insights approach

Limited impact . The approach has focused on more tractable and easy-to-measure changes at the expense of bigger impacts; it has just been tinkering around the edges of fundamental problems 29 , 50 , 172 .

Failure to reach scale . The approach promotes a model of experimentation followed by scaling, but it has not paid enough attention to how successful scaling happens—and the fact that it often does not happen 18 .

Mechanistic thinking . The approach has promoted a simple, linear and mechanistic approach to understanding behaviour that ignores second-order effects and spillovers (and employs evaluation methods that assume a move from A to B against a static background) 29 , 62 , 173 .

Flawed evidence base . The replication crisis has challenged the evidence base underpinning the behavioural insights approach, adding to existing concerns such as the duration of its interventions’ effects 79 , 174 .

Lack of precision . The approach lacks the ability to construct precise interventions and establish what works for whom, and when. Instead, it relies either on overgeneral frameworks or on disconnected lists of biases 80 , 92 , 94 .

Overconfidence . The approach can encourage overconfidence and overextrapolation from its evidence base, particularly when testing is not an option 175 .

Control paradigm . The approach is elitist and pays insufficient attention to people’s own goals and strategies; it uses concepts such as irrationality to justify attempts to control the behaviour of individuals, since they lack the means to do so themselves 176 , 177 .

Neglect of the social context . The approach has a limited, overly cognitive and individualistic view of behaviour that neglects the reality that humans are embedded in established societies and practices 125 , 178 , 179 .

Ethical concerns . The behavioural insights approach will face more ethics, transparency and privacy conundrums as it attempts more ambitious and innovative work 143 , 145 , 154 .

Homogeneity of participants and perspectives . The range of participants in behavioural science research has been narrow and unrepresentative 164 ; homogeneity in the locations and personal characteristics of behavioural scientists influences their viewpoints, practices and theories 124 , 166 .

Use behavioural science as a lens

The early phase of the behavioural insights movement was marked by scepticism about whether effects obtained in laboratories would translate to real-world settings 27 . In response, practitioners developed standard approaches that could demonstrate a clear causal link between an intervention and an outcome 28 . In practice, these approaches directed attention towards how the design of specific aspects of a policy, product or service influences discrete behaviours by actors who are considered mostly in isolation 29 .

These standard approaches are strong and have produced valuable results in many contexts around the world 20 , 30 . However, in the aggregate, they have also fostered a perspective centred on the metaphor of behavioural science as a specialist tool. This view mostly limits behavioural science to the role of fixing concrete aspects of predetermined interventions rather than aiding the consideration of broader policy goals 31 .

Over time, this view has created a self-reinforcing perception that only certain kinds of tasks are suitable for behavioural scientists 29 . Opportunities, skills and ambitions have been constricted as a result; a rebalancing is needed. Behavioural science also has much to say about pressing societal issues such as discrimination, pollution and economic mobility and the structures that produce them 32 , 33 . These ambitions have always been present in the behavioural insights movement 34 , but the factors just outlined acted against their being realized more fully 35 .

The first step towards achieving these ambitions is to replace the dominant metaphor of behavioural science as a tool. Instead, behavioural science should be understood as a lens that can be applied to any public or private issue. This change offers several advantages:

A lens metaphor shows that behavioural science can enhance the use of standard policy options (for example, revealing new ways of structuring taxes) rather than just acting as an alternative to them.

A lens metaphor conveys that the uses of behavioural science are not limited to creating new interventions. A behavioural science lens can, for example, help to reassess existing actions and understand how they may have unintended effects. It emphasizes the behavioural diagnosis of a situation or issue rather than pushing too soon to define a precise target outcome and intervention 31 .

Specifying that this lens can be applied to any action conveys the error of separating ‘behavioural’ and ‘non-behavioural’ issues: most of the goals of private and public action depend on certain behaviours happening (or not). Behavioural science should therefore be integrated into an organization’s core activities rather than acting as an optional specialist tool 36 .

It may seem odd to start with a change of metaphor, but the primary problem here is one of perception. Behavioural science itself shows us the power of framing: the metaphors we use shape the way we behave and therefore can be agents of change 37 . Metaphors are particularly important in this case because the task of broadening the use of behavioural science requires making a compelling case to decision makers 38 . The metaphor of behavioural science as a tool has established credibility and acceptance in a defined area; expanding beyond that area is the task for the next decade.

Build behavioural science into organizations

The second proposal is to broaden the scope of how behavioural science is used in organizations. Given that many dedicated behavioural science teams exist worldwide, it is understandable that much attention has been paid to the question of how they should be set up successfully. However, this focus has diverted attention from considering how to use behavioural science to shape organizations themselves 39 . We need to talk less about how to set up a dedicated behavioural science team and more about how behavioural science can be integrated into an organization’s standard processes. For example, as well as trying to ensure that a departmental budget includes provisions for behavioural science, why not use behavioural science to improve the way this budget is created (for example, are managers anchored to outdated spending assumptions) 40 ?

The overriding message here is for greater focus on the organizational changes that indirectly apply or support behavioural science principles, rather than just thinking through how the direct and overt use of behavioural science can be promoted in an organization. One advantage to this approach is that it can help organizations to address problems with scaling interventions 36 . If some of the barriers to scaling concern cognitive biases in organizations, these changes could minimize the effect of such biases 41 . Rather than starting with a behavioural science project and then trying to scale it, we could start by looking at operations at scale and understanding how they can be influenced.

It is useful to understand how this approach maps onto existing debates about how to set up a behavioural function in organizations. Doing so reveals six main scenarios, as shown in Table 2 . In the ‘baseline’ scenario, there is limited awareness of behavioural science in the organization, and its principles are not incorporated into processes. In the ‘nudged organization’, behavioural science awareness is still low, but its principles have been used to redesign processes to create better outcomes for staff or service users. In ‘proactive consultancy’, leaders may have set up a dedicated behavioural team without grafting it onto the organization’s standard processes. This lack of institutional grounding puts the team in a less resilient position, meaning that it must always search for new work. In ‘call for the experts’, an organization has concentrated behavioural expertise, but there are also prompts and resources that allow this expertise to be integrated into business as usual. Expertise is not widespread, but access to it is. Processes stimulate demand for behavioural expertise that the central team can fulfil. In ‘behavioural entrepreneurs’, there is behavioural science capacity distributed throughout the organization, through either direct capacity building or recruitment. The problem is that organizational processes do not support these individual pockets of knowledge. Finally, a ‘behaviourally enabled organization’ is one where there is knowledge of behavioural science diffused throughout the organization, which also has processes that reflect this knowledge and support its deployment.

Most discussions make it seem like the meaningful choice is between the different columns in Table 2 —how to organize dedicated behavioural science resources. Instead, the more important move is from the top row to the bottom row: moving from projects to processes, from commissions to culture. A useful way of thinking about this task is about building or upgrading the “choice infrastructure” of the organization 42 . In other words, we should place greater focus on the institutional conditions and connections that support the direct and indirect ways that behavioural science can infuse organizations.

Working out how best to build the choice infrastructure in organizations should be a major priority for applied behavioural science. Already we can see that some features will be crucial: reducing the costs of experimentation, creating a system that can learn from its actions, and developing new and better ways of using behavioural science principles to analyse the behavioural effects of organizational processes, rules, incentives, metrics and guidelines 36 .

See the system

Many important policy challenges emerge from complex adaptive systems, where change often does not happen in a linear or easily predictable way, and where coherent behaviour can emerge from interactions without top-down direction 43 . There are many examples of such systems in human societies, including cities, markets and political movements 44 . These systems can create “wicked problems”—such as the COVID-19 pandemic—where ideas of success are contested, changes are nonlinear and difficult to model, and policies have unintended consequences 45 .

This reality challenges the dominant behavioural science approach, which usually assumes stability over time, keeps a tight focus on predefined target behaviours and predicts linear effects on the basis of a predetermined theory of change 46 . The result, some argue, is a failure to understand how actors are acting and reacting in a complex system that leads policymakers to conclude they are being irrational—and then actually disrupt the system in misguided attempts to correct perceived biases or inefficiencies 47 , 48 , 49 .

These criticisms may overstate the case, but they point to a way forward. Behavioural science can be improved by using aspects of complexity thinking to offer new, credible and practical ways of addressing major policy issues. The first step is to reject crude distinctions of ‘upstream’ versus ‘downstream’ or the ‘individual frame’ versus the ‘system frame’ 50 . Instead, complex adaptive systems show that higher-level features of a system can actually emerge from the lower-level interactions of actors participating in the system 44 . When they become the governing features of the system, they then shape the lower-level behaviour until some other aspect emerges, and the fluctuations continue. An example might be the way that new coronavirus variants emerged in particular settings and then went on to change the course of the whole pandemic, requiring new overall strategic responses.

In other words, we are dealing with “cross-scale behaviours” 49 . For example, norms, rules, practices and culture itself can emerge from aggregated social interactions; these features then shape cognition and behavioural patterns in turn 51 . Recognizing cross-scale behaviours means that behavioural science could:

Identify “leverage points” where a specific shift in behaviour will produce wider system effects 52 . One option is to identify when and where tipping points are likely to occur in a system and then either nudge them to occur or not, depending on the policy goal 53 . For example, if even a subset of consumers decides to switch to a healthier version of a food product, this can have broader effects on a population’s health through the way the food system responds by restocking and product reformulation 54 .

Model the collective implications of individuals using simple heuristics to navigate a system. For example, new models show how small changes to simple heuristics that guide savings (in this case, how quickly households copy the savings behaviours of neighbours) can lead to the sudden emergence of inequalities in wealth 55 .

Find targeted changes to features of a system that create the conditions for wide-ranging shifts in behaviour to occur. For example, a core driver of social media behaviours is the ease with which information can be shared 46 . Even minor changes to this parameter can drive widespread changes—some have argued that such a change is what created the conditions leading to the Arab Spring, for example 56 .

This approach also suggests that a broader change in perspective is needed. We need to realize the flaws in launching interventions in isolation and then moving on when a narrowly defined goal has been achieved. Instead, we need to see the longer-term impact on a system of a collection of different policies with varying goals 57 . The best approach may be “system stewardship”, which focuses on creating the conditions for behaviours and indirectly steering adaptation towards overall goals 58 .

Of course, not every problem will involve a complex adaptive system; for simple issues, standard approaches to applying behavioural science work well. Behavioural scientists should therefore develop the skills to recognize the type of system that they are facing (see the system) and then choose their approach accordingly. These skills can be developed through agent-based simulations 59 , immersive technologies 60 or just basic checklists 61 .

Put randomized controlled trials in their place

Randomized controlled trials (RCTs) have been a core part of applied behavioural science, and they work well in relatively simple and stable contexts. But they can fare worse in complex adaptive systems, whose many shifting connections can make it difficult to keep a control group isolated and where a narrow focus on predetermined outcomes may neglect others that are important but difficult to predict 43 , 62 .

We can strengthen RCTs to deal better with complexity. We can try to gain a better understanding of the system interactions and anticipate how they may play out, perhaps through “dark logic” exercises that try to trace potential harms rather than just benefits 63 . For example, we might anticipate that sending parents text messages encouraging them to talk to their children about the school science curriculum may achieve this outcome at the expense of other school-supporting behaviours—as turned out to be the case 64 . Engaging the people who will implement and participate in an intervention will be a key part of this effort.

Another option is to set up RCTs to measure diffusion and contagion in networks, either by creating separate online environments or by randomizing real-world clusters, such as separate villages 65 , 66 . Finally, we can build feedback and adaptation into the design of the RCT and the intervention, allowing adjustments to changing conditions 67 , 68 . Options include using two-stage trial protocols 69 , evolutionary RCTs 70 , sequential multiple assignment randomized trials 71 and ‘bandit’ algorithms that identify high-performing interventions and allocate more people to them 72 .

Behavioural science can also be used to enhance alternative ways of measuring impacts—in particular, agent-based modelling, which tries to simulate the interactions between the different actors in a system 73 . The agents in these models are mostly assumed to be operating on rational choice principles 74 , 75 . There is therefore an opportunity to build in more evidence about the drivers of behaviour—for example, habits and social comparisons 49 .

Replication, variation and adaptation

The ‘replication crisis’ of the past decade has seen intense debate and concern about the reliability of behavioural science findings. Poor research practices were a major cause of the replication crisis; the good news is that many have improved as a result 76 , 77 . Now there are sharper incentives to preregister analysis plans, greater expectations that data and code will be freely shared, and wider acceptance of post-publication review of findings 78 .

Behavioural scientists need to secure and build on these advances to move towards a future where appropriately scoped meta-analyses of high-quality studies (including deliberate replications) are used to identify the most reliable interventions, develop an accurate sense of the likely size of their effects and avoid the weaker options. We have a responsibility to discard ideas if solid evidence now shows that they are shaky, and to offer a realistic view of what behavioural science can accomplish 18 .

That responsibility also requires us to have a hard conversation about heterogeneity in results: the complexity of human behaviour creates so much statistical noise that it is often hard to detect consistent signals and patterns 79 . The main drivers of heterogeneity are that contexts influence results and that the effect of an intervention may vary greatly between groups within a population 80 , 81 . For example, choices of how to set up experiments vary greatly between studies and researchers, in ways that often go unnoticed 82 . A recent study ran an experiment to measure the impact of these contextual factors. Participants were randomly allocated to studies designed by different research teams to test the same hypothesis. For four of the five research questions, studies actually produced effects in opposing directions. These “radically dispersed” results indicate that “idiosyncratic choices in stimulus design have a very large effect on observed results” 83 . These factors complicate the idea of replication itself: a ‘failed’ replication may not show that a finding was false but rather show how it exists under some conditions and not others 84 .

These challenges mean that applied behavioural scientists need to set a much higher bar for claiming that an effect holds true across many unspecified settings 85 . There is a growing sense that interventions should be talked about as hypotheses that were true in one place and that may need adapting to be true elsewhere 18 , 86 .

Narrative changes need to be complemented by specific proposals. The first concerns data collection: behavioural scientists should expand studies to include (and thus examine) a wider range of contexts and participants and gather richer data about them. To date, only a small minority of behavioural studies have provided enough information to see how effects vary 87 . Moreover, the gaps in data coverage may result from and create systemic issues in society: certain groups may be excluded or may have their data recorded differently from others 88 . Coordinated multi-site studies will be needed to collect enough data to explore heterogeneity systematically; crowdsourced studies offer particular promise for testing context and methods 83 . Realistically, this work is going to require a major investment in research infrastructure to set up standing panels of participants, coordinate between institutions, and reduce barriers to data collection and transfer 80 . These efforts cannot be limited to just a few countries.

Behavioural scientists also need to get better at judging how strongly an intervention’s results were linked to its context and therefore how much adaptation it needs 81 . We should use and modify frameworks from implementation science to develop such judgement 89 . Finally, we need to codify and cultivate the practical skills that successfully adapt interventions to new contexts; expertise in behavioural science should not be seen as simply knowing about concepts and findings in the abstract. It is therefore particularly valuable to learn from practitioners how they adapted specific interventions to new contexts. These accounts are starting to emerge, but they are still rare 18 , since researchers are incentivized to claim universality for their results rather than report and value contextual details 82 .

Beyond lists of biases

The heterogeneity in behavioural science findings also means that our underlying theories need to improve: we are lacking good explanations for why findings vary so much 84 . This need for better theories can be seen as part of a wider ‘theory crisis’ in psychology, which has thrown up two big concerns for behavioural science 90 , 91 .

The first stems from the fact that theories of behaviour often try to explain phenomena that are complex and wide-ranging 92 . If you are trying to show how emotion and cognition interact (for example), this involves many causes and interactions. Trying to cover this variability can produce descriptions of relationships and definitions of constructs that are abstract and imprecise 85 . The result is theories that are vague and weak, since they can be used to generate many different hypotheses—some of which may actually contradict each other 90 . That makes theories hard to disprove, and so weak theories stumble on, unimproved 93 .

The other concern is that theories can make specific predictions, but they are disconnected from each other—and from a deeper, general framework that can provide broader explanations (such as evolutionary theory) 94 . The main way this issue affects behavioural science is through heuristics and biases. Examples of individual biases are accessible, popular and how many people first encounter behavioural science. These ideas are incredibly useful, but they have often been presented as lists of standalone curiosities in a way that is incoherent, reductive and deadening. Presenting lists of biases does not help us to distinguish or organize them 95 , 96 , 97 . Such lists can also create overconfident thinking that targeting a specific bias (in isolation) will achieve a certain outcome 98 .

Perhaps most importantly, focusing on lists of biases distracts us from answering core underlying questions. When does one or another bias apply? Which are widely applicable, and which are highly specific? How does culture or life experience affect whether a bias influences behaviour or not 99 , 100 ? These are highly practical questions when one is faced with tasks such as taking an intervention to new places.

The concern for behavioural science is that it uses both these high-level frameworks (such as dual-process theories) and jumbled collections of heuristics and biases, with little in the middle to draw both levels together 94 . Recent years have seen valuable advances in connecting and systematizing theories 101 , 102 . At the same time, there are various ongoing attempts to create strong theories: “coherent and useful conceptual frameworks into which existing knowledge can be integrated” 93 (see also refs. 91 , 103 , 104 ). Naturally, such work should continue, but I think that applied behavioural science will benefit particularly from theories that are practical. By this I mean:

They fill the gap between day-to-day working hypotheses and comprehensive and systematic attempts to find universal underlying explanations.

They are based on data rather than being derived from pure theorizing 105 .

They can generate testable hypotheses, so they can be disproved 106 .

They specify the conditions under which a prediction applies or does not 85 .

They are geared towards realistic adaptation by practitioners and offer “actionable steps toward solving a problem that currently exists in a particular context in the real world” 107 .

Resource rationality may be a good example of a practical theory. It starts from the basis that people make rational use of their limited cognitive resources 108 . Given that there is a cost to thinking, people will look for solutions that balance choice quality with effort. Resource rationality can offer a “unifying framework for a wide range of successful models of seemingly unrelated phenomena and cognitive biases” that can be used to build models for how people act 108 .

A recent study has shown how these models not only can predict how people will respond to different kinds of nudges in certain contexts but also can be integrated with machine learning to create an automated method for constructing “optimal nudges” 109 . Such an approach could reveal new kinds of nudges and make creating them much more efficient. More reliable ways of developing personalized nudges are also possible. These are all highly practical benefits coming from applying a particular theory.

Predict and adjust

Hindsight bias is what happens when we feel ‘I knew it all along’, even if we did not 110 . When the results of an experiment come in, hindsight bias may mean that behavioural scientists are more likely to think that they had predicted them or quickly find ways of explaining why they occurred. Hindsight bias is a big problem because it breeds overconfidence, impedes learning, dissuades innovation and prevents us from understanding what is truly unexpected 111 , 112 .

In response, behavioural scientists should establish a standard practice of predicting the results of experiments and then receiving feedback on how their predictions performed. Hindsight bias can flourish if we do not systematically capture expectations or priors about what the results of a study will be 113 . Making predictions provides regular, clear feedback of the kind that is more likely to trigger surprise and reassessment rather than hindsight bias 114 . Establishing the average expert prediction—which may be different from the null hypothesis in an experiment—clearly reveals when results challenge the consensus 115 .

There are existing practices to build on here, such as the practice of preregistering hypotheses and trial protocols and the use of a Bayesian approach to make priors explicit. Indeed, more and more studies are explicitly integrating predictions 116 , 117 . However, barriers lie in the way of further progress. People may not welcome the ensuing challenge to their self-image, predicting may seem like one thing too many on the to-do list, and the benefits lie in the future. Some responses to these challenges are to make predicting easy by incorporating it into standard processes; minimize threats to predictors’ self-image (for example, by making and feeding back predictions anonymously) 118 ; give concrete prompts for learning and reflection, to disrupt the move from surprise to hindsight bias 119 ; and build learning from prediction within and between institutions.

Be humble, explore and enable

This proposal is made up of three connected ideas. First, behavioural scientists need to become more aware of the limits of their knowledge and to avoid fitting behaviours into pre-existing ideas around biases or irrationality. Second, they should broaden the exploratory work they conduct, in terms of gaining new types of qualitative data and recognizing how experiences vary by group and geography. Finally, they should develop new approaches to enable people to apply behavioural science themselves—and adopt new criteria for judging when these approaches are appropriate.

Humility is important because behavioural scientists (like other experts) may overconfidently rely on decontextualized principles that do not match the real-world setting for a behaviour 29 . Deeper inquiry can reveal reasonable explanations for what seem to be behavioural biases 120 . In response, those applying behavioural science should avoid using the term ‘irrationality’, which can limit attempts to understand actions in context; acknowledge that diagnoses of behaviour are provisional and incomplete (epistemic humility) 121 ; and design processes and institutions to counteract overconfidence 122 .

How do we conduct these deeper inquiries? Three areas demand particular focus in the future. First, pay greater attention to people’s goals and strategies and their own interpretations of their beliefs, feelings and behaviours 123 . Second, reach a wider range of experiences, including marginalized voices and communities, understanding how structural inequalities can lead to expectations and experiences varying greatly by group and geography 124 . Third, recognize how apparently universal cognitive processes are shaped by specific contexts, thereby unlocking new ways for behavioural science to engage with values and culture 125 , 126 . For example, one influential view of culture is that it influences action “not by providing the ultimate values toward which action is oriented but by shaping a repertoire or ‘toolkit’ of habits, skills, and styles” 127 . There are similarities here to the heuristics-and-biases toolkit perspective on behaviour: behavioural scientists could start explaining how and when certain parts of the toolkit become more or less salient.

More can and should be done to broaden ownership of behavioural science approaches. Many (but far from all) behavioural science applications have been top-down, with a choice architect enabling certain outcomes 8 , 128 . One route is to enable people to become more involved in designing interventions that affect them—and “nudge plus” 129 , “self-nudges” 130 and “boosts” 131 have been proposed as ways of doing this. Reliable criteria are needed to decide when enabling approaches may be appropriate, including whether the opportunity to use an enabling approach exists; ability and motivation; preferences; learning and setup costs; equity impacts; and effectiveness, recognizing that evidence on this point is still emerging 132 , 133 .

But these new approaches should not be seen simplistically as enabling alternatives to disempowering nudges 134 . Instead, we need to consider how far the person performing the behaviour is involved in shaping the initiative itself, as well as the level and nature of any capacity created by the intervention. People may be heavily engaged in selecting and developing a nudge intervention that nonetheless does not trigger any reflection or build any skills 135 . Alternatively, a policymaker may have paternalistically assumed that people want to build up their capacity to perform an action, when in fact they do not. This is the real choice to be made.

A final piece missing from current thinking is that enabling people can lead to a major decentring of the use of behavioural science. If more people are enabled to use behavioural science, they may decide to introduce interventions that influence others 136 . Rather than just creating self-nudges through altering their immediate environments, they may decide that wider system changes are needed instead. A range of people could be enabled to create nudges that generate positive societal change (with no central actors involved). This points towards a future where policy or product designers act less like (choice) architects and more like facilitators, brokers and partnership builders 137 .

Data science for equity

Recent years have seen growing interest in using new data science techniques to reliably analyse the heterogeneity of large datasets 138 , 139 . Machine learning is claimed to offer more sophisticated, reliable and data-driven ways of detecting meaningful patterns in datasets 140 , 141 . For example, a machine learning approach has been shown to be more effective than conventional segmentation approaches at analysing patterns of US household energy usage to reduce peak consumption 142 .

A popular idea is to use such techniques to better understand what works best for certain groups and thereby tailor an offering to them 143 . Scaling an intervention stops being about a uniform roll-out and instead becomes about presenting recipients with the aspects that are most effective for them 144 .

This vision is often presented as straightforward and obviously desirable, but it runs almost immediately into ethical quandaries and value judgements. People are unlikely to know what data have been used to target them and how; the specificity of the data involved may make manipulation more likely, since it may exploit sensitive personal vulnerabilities; and expectations of universality and non-discrimination in public services may be violated 143 , 145 .

Closely related to manipulation concerns is the fear that data science will open up new opportunities to exploit, rather than to help, the vulnerable 146 . One aspect is algorithmic bias. Models using data that reflect historical patterns of discrimination can produce results that reinforce these outcomes 147 . Since disadvantaged groups are more likely to be subject to the decisions of algorithms, there is a particular risk that inequalities will be perpetuated—although some studies argue that algorithms are actually less likely to be biased than human judgement 148 , 149 .

There is also emerging evidence that people often object to personalization. While they support some personalized services, they consistently oppose advertising that is customized on the basis of sensitive information—and they are generally against the collection of the information that personalization relies on 150 . To navigate this landscape, behavioural scientists need to examine four factors:

Who does the personalization target, and using what criteria? Many places have laws or norms to ensure equal treatment based on personal characteristics. When does personalization violate those principles?

How is the intervention constructed? To what extent do the recipients have awareness of the personalization, choice over whether it occurs, control over its level or nature, and the opportunity to give feedback on it 151 ?

When is it directed? Is it at a time when the participant is vulnerable? Would they probably regret it later, if they had time to reflect?

Why is personalization happening? Does it aim to exploit and harm or to support and protect, recognizing that those terms are often contested?

Taking these factors into account, I propose that the main opportunity is for data science to identify the ways in which an intervention or situation appears to increase inequalities, and reduce them 152 . For example, groups that are particularly likely to miss a filing requirement could be offered pre-emptive help. Algorithms can be used to better explain the causes of increased knee pain experienced in disadvantaged communities, thereby giving physicians better information to act on 153 .

I call this idea data science for equity. It addresses the ‘why’ factor by using data science to support, not exploit. ‘Data science for equity’ may seem like a platitude, but it is a very real choice: the combination of behavioural and data science is powerful and has been used to create harm in the past. Moreover, it needs to be complemented by attempts to increase agency (the ‘how’ factors), as in a recent study that showed how boosts can be used to help people to detect micro-targeting of advertising 154 , and studies that obtain more data on which uses of personalization people find acceptable.

No “view from nowhere”

The final proposal is one of the most wide-ranging, challenging and important. For the philosopher Thomas Nagel, the “view from nowhere” was an objective stance that allowed us to “transcend our particular viewpoint” 155 . Taking such a stance may not be possible for behavioural scientists. We bring certain assumptions and ways of seeing to what we do; we are always situated in, embedded in and entangled with ideas and situations 124 . We cannot assume that there is some set-aside position from which to observe the behaviour of others; no objective observation deck outside society exists 156 .

Behavioural scientists are defined by having knowledge, skills and education; many of them can use these resources to shape public and private actions. They are therefore in a privileged position, but they may not see the extent to which they hold elite positions that stop them from understanding people who think differently (for example, those who are sceptical of education) 157 . The danger is that elites place their group values and preferences on others, while thinking that they are adopting a view from nowhere 158 , 159 . This does not mean that they can never act or opine, but rather that they need to carefully understand their own positionality and those of others before doing so.

There have been repeated concerns that the field is still highly homogeneous in other ways as well. Gender, race, physical abilities, sexuality and geography also influence the viewpoints, practices and theories of behavioural scientists 160 , 161 . Only a quarter of the behavioural insights teams catalogued in a 2020 survey were based in the Global South 162 . An over-reliance on using English in cognitive science has led to the impact of language on thought being underestimated 163 . The past decade has shown how behaviours can vary greatly from culture to culture, even as psychology has tended to generalize from relatively small and unrepresentative samples 164 . Behavioural science studies often present data from Western, educated, industrialized, rich and democratic samples as more generalizable to humans as a whole 165 . So, rather than claiming that science is value-free, we need to find realistic ways of acknowledging and improving this reality 166 .

A starting point is for behavioural scientists to cultivate self-scrutiny by querying how their identities and experiences contribute to their stance on a topic. Hypothesis generation could particularly benefit from this exercise, since arguably it is closely informed by the researcher’s personal priorities and preferences 167 . Behavioural scientists could be actively reflecting on interventions in progress, including what factors are contributing to power dynamics 168 . Self-scrutiny may not be enough. We should also find more ways for people to judge researchers and decide whether they want to participate in research—going beyond consent forms. If they do participate, there are many opportunities to combine behavioural science with co-design 128 .

Finally, we should take actions to increase diversity (of several kinds) among behavioural scientists, teams, collaborations and institutions. Doing this requires addressing barriers such as the lack of professional networks connecting the Global North and Global South, and the time needed to build understanding of the tactics required to write successful grant applications from funders 169 . In many countries, much more could be done to increase the ethnic and racial diversity of the behavioural science field—for example, through support for starting and completing PhDs or through reducing the substantial racial gaps present in much public funding of research 170 , 171 .

Applied behavioural science has seen rapid growth and meaningful achievements over the past decade. Although the popularity of nudging provided its initial impetus, an ambition soon formed to apply a broader range of techniques to a wider range of goals. However, a set of credible critiques have emerged as levels of activity have grown. As Fig. 1 indicates, there are proposals that can address these critiques (and progress is already being made on some of them). When considered together, these proposals present a coherent vision for the scope, methods and values of applied behavioural science.

This vision is not limited to technical enhancements for the field; it also covers questions of epistemology, identity, politics and praxis. A common theme throughout the ten proposals is the need for self-reflective practice that is aware of how its knowledge and approaches have originated and how they are situated. In other words, a main priority for behavioural scientists is to recognize the various ways that their own behaviour is being shaped by structural, institutional, environmental and cognitive factors.

Realizing these proposals will require sustained work and experiencing the discomfort of disrupting what may have become familiar and comfortable practices. That is a particular problem because incentives for change are often weak or absent. Improving applied behavioural science has some characteristics of a social dilemma: the benefits are diffused across the field as a whole, while the costs fall on any individual party who chooses to act (or act first). Practitioners are often in competition. Academics often want to establish a distinctive research agenda. Commissioners are often rewarded for risk aversion. Impaired coordination is particularly problematic because coordination forms the basis for several necessary actions, such as the multi-site studies to measure heterogeneity.

Solving these problems will be hard. Funders need to find mechanisms that adequately reward coordination and collaboration by recognizing the true costs involved. Practitioners need to perceive the competitive advantages of adopting new practices and be able to communicate them to clients. Clients themselves need to have a realistic sense of what can be achieved but still be motivated to commit resources. Stepping back, the starting point for addressing these barriers needs to be a change in the narrative about what the field does and could do—a new set of ambitions to aim for. This manifesto aims to help to shape such a narrative.

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Acknowledgements

I thank L. Tublin for her editorial support. I also thank S. Banerjee, E. Berkman, A. Buttenheim, F. Callaway, J. Collins, J. Doctor, A. Gyani, D. Halpern, P. John, T. Marteau, M. Muthukrishna, D. Perera, D. Perrott, K. Ruggeri, R. Schmidt, D. Soman, H. Strassheim, C. Sunstein and members of the Behavioural Insights Team for their feedback on previous drafts.

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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.

• Culture in Human Behavior Essay The act of changing a culture can only be minimal because of the complexities of the study complexity Culture, serving as a categorical idea of people, is a school of thought that has anthropologists all […]
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• 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 […]
• 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.
• 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.
• 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 […]
• 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 […]
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 […]
• 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.
• 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.
• 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 […]
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 […]
• 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 […]
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 […]
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Understanding Human Behavior – A Physiological Approach

Bryn Farnsworth

Table of Contents

As fantastically (and fanatically) self-aware organisms, we humans tend to ascribe great importance to our intellectual processes: We’re rational and reasoning creatures, we assert, capable of stepping back and assessing our own behavior through an analytical lens.

Like any other biological entity, however, we’re interacting with and responding to our environment in myriad ways well beyond the realm of our conscious perception. We usually take these nonconscious, autonomic aspects of our being for granted, but naturally, they’re fundamental to both our appreciation of the world around us and, critically, our day-to-day survival.

We don’t need to compel ourselves to shiver when the mercury drops; our hand recoils at the lick of the flame or the bite of the dog. Thankfully, we don’t have to think our way through the mechanics of walking in order to pull it off – start trying to, and you’re liable to beeline for the pavement.

The conscious and the nonconscious , the voluntary and the involuntary: When it comes to Homo sapiens , these processes aren’t either-or propositions. They’re thoroughly intertwined, influencing and echoing one another. In short, human beings (breaking news) are complicated systems, and the study of human behavior is a complex task. Parsing out behavioral and emotional nuances requires zoomed-in looks at the tempos and intensities of all kinds of physical and psychological networks – and a holistic, big-picture perspective of how those networks interface with one another.

What influences human behavior?

Human behavior is an intricate tapestry woven from diverse threads of influences, each contributing to the complex patterns of how we act and interact. One of the most fundamental influences is our biological makeup. Genetics play a significant role in shaping our dispositions and behaviors, impacting everything from our temperament to our susceptibility to certain conditions. The brain’s neurochemistry, especially the levels and functions of neurotransmitters like serotonin and dopamine, also significantly affects our mood, decision-making, and behavior.

Psychological factors are another key influence. Our individual personalities, shaped by a combination of innate traits and life experiences, dictate a large part of our behavior. Cognitive processes like perception, reasoning, and memory, along with emotional responses, guide how we interpret and react to the world around us. The impact of early life experiences, particularly during critical developmental stages, can have lasting effects on our behavior and personality.

Equally important is the role of the environment. This encompasses the social and cultural context in which we are raised and live. Our family, peers, education, and broader cultural norms and values significantly influence our behavior. These social factors shape our beliefs, attitudes, and behaviors, often subconsciously.

In essence, human behavior is a complex, dynamic interplay of biology, psychology, and environmental factors. Each aspect plays a critical role in shaping who we are and how we behave, making human behavior a rich field of study with endless nuances and variations.

Types of Human Behavior

Human behavior, in its multifaceted nature, exhibits a spectrum of types, each uniquely influencing how individuals perceive and interact with the world. These behavioral types, shaped by a combination of personal experiences, cultural background, and psychological makeup, play a crucial role in defining an individual’s approach to life and relationships.

Optimistic behavior is characterized by a general expectation of positive outcomes. Optimists typically focus on the brighter side of situations, maintaining a hopeful and confident attitude towards future events. This type of behavior often leads to higher levels of resilience in the face of adversity, as optimists tend to view challenges as temporary and surmountable. Their positive outlook can enhance mental and physical well-being, fostering stronger social relationships and a greater sense of life satisfaction.

Pessimistic

In contrast, pessimistic behavior is marked by a tendency to expect unfavorable outcomes. Pessimists often focus on the potential negatives in a situation and may anticipate failure or disappointment. This outlook can sometimes lead to a more cautious approach to life, potentially reducing the likelihood of taking risks that may result in failure. However, chronic pessimism may also contribute to a decrease in overall life satisfaction and can be linked to higher levels of stress and anxiety.

Trusting behavior involves a willingness to rely on the actions and intentions of others, often expecting honesty and integrity. Individuals exhibiting this type of behavior are typically open to forming new relationships and collaborations, assuming that others are generally good and reliable. This trust can foster strong social bonds and effective teamwork, but it also carries the risk of disappointment or exploitation if the trust is misplaced.

Envious behavior is characterized by a desire for others’ traits, status, abilities, or rewards, often accompanied by feelings of discontent or ill will towards those who possess them. Envy can motivate individuals to improve their own status or abilities, acting as a catalyst for personal growth and achievement. However, if left unchecked, it can lead to negative emotions like bitterness and resentment, which can harm interpersonal relationships and personal well-being.

Physiology and Human Behavior

Researchers interested in how humans respond to stimuli, therefore – whether it’s an Internet ad or an interpersonal encounter – can enhance their investigations by employing biosensors that document psychophysiological patterns. 

Self-assessment  / self-reporting remains a powerful and useful tool for understanding the how and the why of human behavior but has some major limitations.

People aren’t always entirely honest when describing how something makes them feel – not necessarily because they’re trying to be duplicitous or crafty, but because they may feel pressured by the formal self-critical exercise to give what they think is the “right” answer (or the least embarrassing one).

Furthermore, it’s often exceedingly difficult to explain in coherent sentences our response to a piece of information, or our mood at a given moment. We may not know exactly why we favor one product over another, or why we’re feeling generally joyful or generally depressed (there are many techniques for honing a survey’s efficacy – you can learn more in one of our  blog posts ).

Meanwhile, physiological data – such as the rate of our heartbeat , the degree of our perspiration, and the direction and rhythm of our eye movements can shed light on behavioral phenomena our conscious minds may deny, distort, or completely fail to register.

The academic and commercial applications of the psychophysiological studies considering such data are virtually limitless, relevant to fields as diverse as neuroscience, psychotherapy, marketing, and design. 

Examples of Biosensor Research to Understand Human Behaviour

What’s remarkable about such studies are the incredibly fine-scale insights into the human emotion that can be gleaned from the minute nonconscious or involuntary phenomena.

Consider galvanic skin response or GSR. This is a measure of electrodermal activity: the relative conductance of our skin from perspiration. Sweating is an utterly autonomic operation that, in addition to its role in thermoregulation, is a reaction to arousal, from general excitement to flat-out terror. By measuring sweat production via skin conductance, GSR can reveal evidence for a stimulated, agitated state of being that’s beyond a person’s deliberate control – including arousal too subtle to manifest on the self-aware spectrum.

Electrocardiography (ECG) registers the electrical signature of a heartbeat, revealing intricacies of their rate and variability that, like GSR, can demonstrate physiological, emotional, or psychological arousal.

Then there’s electroencephalography (EEG) , which tracks brainwaves via scalp-affixed electrodes that measure the electrical pulses produced by mass neuron firings. An EEG readout indicates the moment-by-moment “geography” of brain activity – which cortex is excited when, basically – as well as the brain’s overall state at a given time.

Eye tracking , meanwhile, quantifies when and where a subject’s gaze lingers, the rhythm of reading, and other optical minutiae, while facial expression analysis looks up-close at the configuration of the face’s musculature for clues to a person’s emotions.

The information outputted by a single kind of biosensor can be intriguing and useful, but only to a point. For instance, GSR and ECG readings can suggest the condition of arousal, but not its valence, or emotional character. In other words, sweaty palms or a ramped-up heartbeat doesn’t reveal whether we’re dealing with a love-at-first-sight (i.e., a positive stimulus) sort of situation or a figure-looming-out-of-the-shadows (i.e., a negative stimulus) deal.

Integrate those electrodermal and cardiac data with EEG, facial expression analysis, eye tracking, and other analyses, and you’ve got a much more multifaceted picture. That’s what iMotions is all about.

Expanding Horizons

As we noted earlier, psychophysiological investigations have wide-ranging utility – whether it’s a company trying to gauge the appeal of a new product design to a prospective shopper, or it’s a therapist treating a patient with post-traumatic stress disorder.

As research into human behavior continues to expand – in concert with improvements in the technology and methodology for implementing that research – it goes without saying that its applications will as well.

FAQ: Understanding Human Behavior

Why is it important to understand human behavior.

Understanding human behavior is crucial for a variety of reasons, both on a personal and societal level. At a personal level, it helps in better self-awareness and self-improvement. By understanding the reasons behind our own actions, we can work towards personal growth and effective decision-making. In interpersonal relationships, understanding behavior aids in communication and empathy, allowing us to build stronger, more meaningful connections with others.

On a broader scale, comprehending human behavior is essential for professionals in numerous fields, such as psychology, sociology, education, marketing, and management. For instance, in psychology and counseling, understanding behavior is key to addressing mental health issues and providing effective therapy. In education, it helps in developing teaching methods that cater to different learning styles. In the realm of business, knowledge of consumer behavior is vital for effective marketing strategies.

What is the psychology theory of human behavior?

The psychology theory of human behavior refers to various frameworks and models used to explain why humans behave the way they do. One of the most influential theories is the Behavioral Theory , which suggests that behavior is learned from the environment through conditioning. This theory is divided into two types: Classical Conditioning, where behavior is associated with a new stimulus, and Operant Conditioning, where behavior is shaped by rewards or punishments.

Another significant theory is the Cognitive Theory , which focuses on internal states, such as motivation, problem-solving, decision-making, and beliefs, to understand behavior. This theory posits that cognitive processes are central to understanding human behavior.

Additionally, the Humanistic Theory , pioneered by psychologists like Carl Rogers and Abraham Maslow, emphasizes individual choice and self-actualization as the primary drivers of behavior. It suggests that humans are inherently good and strive for self-improvement and personal growth.

Understanding human behavior is complex and requires considering various perspectives and theories. Each theory offers unique insights and helps in comprehending the diverse and intricate nature of human actions and interactions.

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A Workflow for Building Computationally Rational Models of Human Behavior

• Open access
• Published: 15 August 2024

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• Suyog Chandramouli 1 ,
• Danqing Shi 1 ,
• Aini Putkonen 1 ,
• Sebastiaan De Peuter 1 ,
• Shanshan Zhang 2 ,
• Jussi Jokinen 3 ,
• Andrew Howes 4 &
• Antti Oulasvirta 1  

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Computational rationality explains human behavior as arising due to the maximization of expected utility under the constraints imposed by the environment and limited cognitive resources. This simple assumption, when instantiated via partially observable Markov decision processes (POMDPs), gives rise to a powerful approach for modeling human adaptive behavior, within which a variety of internal models of cognition can be embedded. In particular, such an instantiation enables the use of methods from reinforcement learning (RL) to approximate the optimal policy solution to the sequential decision-making problems posed to the cognitive system in any given setting; this stands in contrast to requiring ad hoc hand-crafted rules for capturing adaptive behavior in more traditional cognitive architectures. However, despite their successes and promise for modeling human adaptive behavior across everyday tasks, computationally rational models that use RL are not easy to build. Being a hybrid of theoretical cognitive models and machine learning (ML) necessitates that model building take into account appropriate practices from both cognitive science and ML. The design of psychological assumptions and machine learning decisions concerning reward specification, policy optimization, parameter inference, and model selection are all tangled processes rife with pitfalls that can hinder the development of valid and effective models. Drawing from a decade of work on this approach, a workflow is outlined for tackling this challenge and is accompanied by a detailed discussion of the pros and cons at key decision points.

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Introduction

Computational models have played a central role in the field of cognitive science (McClelland, 2009 ; Shiffrin, 2010 ; Lake et al., 2017 ; Kriegeskorte & Douglas, 2018 ; Lieder & Griffiths, 2020 ). Models shed light on human behavior by precisely describing mechanisms that link hypothesized cognitive processes with environments and behaviors. Good models not only help develop theoretical understanding, but also produce useful predictions of future behavior, which can in turn be used in applied settings such as interactive AI. However, building such models, especially in scenarios that extend from controlled experiments in the laboratory to real-world behavior, has turned out to be challenging. One obstacle involves difficulties in capturing the extensive adaptivity that characterizes human behavior in the real world (Oulasvirta et al., 2022 ). To be applied practically, models need to account for how behavior changes as a function of beliefs, capabilities, goals, and the environment, which is, however, further complicated by the continual learning, adaptation, and exploration of individuals in their environment (Howes et al., 2023 ). Successfully tackling this challenge would advance efforts towards building computing systems that better understand people. For safer AI that collaborates better with people, we need models with control- and explanation-amenable causal mechanisms.

Computational rationality is a theory that has recently emerged as a candidate to address this challenge (Howes et al., 2009 ; Lewis et al., 2014 ; Gershman et al., 2015 ; Lieder & Griffiths, 2020 ; Oulasvirta et al., 2022 ; Howes et al., 2023 ). Footnote 1 It draws together ideas from cognitive sciences and machine learning to predict adaptive behavior in artificial and biological agents. While the principle of rationality describes the ideal solution of a problem posed to a (unbounded) rational agent, computational rationality recognizes that biological agents also have computational limitations and provides a framework for describing agent behavior as a result of trying to make good decisions under computational constraints. Given our focus on human cognition in this article, the core assumption made by computational rationality is that our choices and behavior aim to maximize subjective expected utility; however, we are limited in that by bounds imposed not only by the “external environment,” but also an “internal environment” comprising of our cognition and our bodies. The external environment here refers to elements such as our physical context, the task being carried out, or the devices we interact with that shape our possible behaviors, while the internal environment includes latent psychological constructs such as perception, attention, and memory that may have capacity limits. Observed behavior is then considered an emergent consequence of rational adaptation to the totality of these constraints.

While there are several ways of instantiating computational rationality (Lewis et al., 2014 ; Lieder & Griffiths, 2020 ; Icard, 2023 ), our article is concerned with a framework that uses partially observable Markov decision processes (POMDPs; Sondik, 1971 ) to capture computational rationality as arising due to the partial observability of the external environment and internal environments. POMDP-based models are theoretically sound and flexibly allow for the incorporation of a variety of models within them, including those based on deep learning, which can be useful in capturing the complexities of real-world environments and behavior across a wide range of contexts. Methods from reinforcement learning and deep reinforcement learning can be then used for solving the POMDPs (i.e., finding approximate policies that predict observed behavior). As these models are related to practices in both, cognitive science and machine learning, we will be using terminology from both disciplines in the article. We introduce and clarify key terms when they come up to aid in comprehension.

Computational rationality assumes that people adapt their behavior to maximize expected utility under cognitive bounds. To build a computational model that instantiates this theory with reinforcement learning, a policy ( \(\pi \) ) needs to specify a decision problem where a policy ( \(\pi \) ) controls which action ( a ) to take in light of observations ( o ) to maximize rewards ( r ). Whereas standard reinforcement learning-based models applied in ML are situated directly in the external environment, computationally rational models of human behavior are only “yoked to it” via their internal environment, that is, cognition. Modeling the decision-making problem, including the internal environment, as well as its parametric variability across individuals ( \(\theta \) ), is a challenge in cognitive science

The computationally rational models that we build with reinforcement learning have some basis in traditional cognitive architecture approaches (Laird et al., 1987 ; Anderson et al., 1997 ; Bekolay et al., 2014 ), but vary greatly in terms of how they model adaptive behavior. Cognitive architectures assume that everyday tasks recruit multiple cognitive capacities that need to be carefully “architected” into a model (Card et el. 1983) and consequently model internal capacities as resource-limited modules; the architectures include a “processor” that directs the flow of information between modules based on a “cognitive program” or a set of “production rules.” Although cognitive architectures have made significant progress with respect to understanding cognition, they have sometimes been brittle as predictive models because their “programming” was often hand-coding them via “sophisticated guessing” (Shiffrin, 2010 ). Every time a different situation or individual needs to be studied, the vast rule base needs to be manually updated, which can be time-consuming and contrary to its vision of producing generally applicable models of behavior. Computationally rational models, on the other hand, capture the essence of cognitive architectures within the “internal environment” of a reinforcement learning agent (Lewis et al., 2014 ). Adaptive behavior then emerges as a consequence of bounded rational policy optimization. Boundedly optimal behavior can be derived for different situations based only on policy optimization. When building a computationally rational model, one specifies the environment and the agent’s goals and characteristics; the actual behavior in a given situation, however, emerges via a policy ( \(\pi \) ) optimized to these bounds. In practice, this policy is approximated by machine learning methods, particularly from reinforcement learning. Computationally rational models are also generative in the sense that they create stepwise simulations of an agent’s behavior in a given setting, rather than merely predicting summary statistics of empirical data. This is made possible due to parameter inference that it allows at both the individual and the group level, depending on the context of application. During the last decade, the theory has been successfully applied to data from increasingly realistic everyday tasks, including typing (Jokinen et al., 2021a ), driving (Jokinen et al., 2021b ), visual search (Radulescu et al., 2022 ; Jokinen et al., 2020 ; Todi et al., 2019 ), multitasking (Gebhardt et al., 2021 ), menu interaction (Chen et al., 2015 ), and affordances (Liao et al., 2022 ), among others including (Belousov et al., 2016 ; Chen et al., 2021 ). We note that computational rationality models built with reinforcement learning are distinct from reinforcement learning-based models of human learning such as Pavlovian learning (Rescorla, 1972 ; Zhang et al., 2020 ) and error-driven learning (Seymour et al., 2004 ; Sutton, 1988 ).

Computationally rational models are based on a specification of a decision-making problem that an agent faces. At the heart of this formulation are three elements, outlined in Fig.  1 : (i) the internal environment of the agent (that is, the cognitive resources and processes involved), (ii) a reward signal representing what is important to the agent, and (iii) the external environment in which the agent operates. Moreover, the modeler needs to decide the internal representations through which the agent is observing the world. This dictates how external stimuli are transformed into an internally represented state abstraction or a belief that informs the agent’s choice of how to act. The internal environment can be represented in a variety of forms ranging from simple mathematical models to end-to-end machine-learned modules, and it can also take various architectural forms, such as hierarchical (e.g., Gebhardt et al., 2021 ) and modular architectures (e.g., Jokinen et al., 2020 ). The place where the approach breaks decisively from cognitive architectures is in its formulation of a sequential decision-making problem. When the specification of the agent adheres to the assumptions of a Markov decision process (MDP), machine learning methods can be used to estimate an optimal policy. Reinforcement learning (RL), in particular, has been one of the most widely used frameworks used to this end (Sutton & Barto, 2018 ). It learns an optimal bounded policy through trial and error.

Despite the success of computational rationality models framed as POMDPs, producing such models in a valid and practically applicable manner is not straightforward. The usefulness of the eventual model critically depends on several modeling decisions that are made while building them from (i) the initial steps of specifying the rewards and cognitive resources to implement in the model, to (ii) formulating the task as a sequential decision-making problem, and (iii) using RL methods to approximate the optimal policy. The impetus for this paper comes from numerous struggles and dead ends encountered in grappling with computationally rational models of everyday behavior over a decade of research. It is a considerable hurdle to produce such models; the validity and applicability of these models also critically depend on choices made on the way. While there are excellent guidelines for developing other types of computational models of behavior, such as Bayesian statistical models (Gelman et al., 2020 ), decision-making models (Wilson & Collins, 2019 ), and reinforcement learning (Zhang et al., 2020 ; Patterson et al., 2023 ), computational rationality comes bundled with many unique challenges and considerations. These questions are among its complicating factors: How to choose what to handle via RL and what learning should occur with a cognitive resource? Which measures of model quality are meaningful? How does one specify the reward functions? How does one perform model checking when the models use interpretable theory-based components as well as tractable data-driven components? How much value do metrics for model parsimony offer for handling model selection in such scenarios?

This paper fills this gap by laying out a workflow framework that is able to guide modelers interested in computational rationality (diagrammed in Fig.  2 ). A key aspect of the proposed workflow is its iterative stepwise structure. This incorporates steps that are vital for cognitive modeling (model specification) but also elements necessitated by ML structures (policy optimization and parameter tuning), alongside general stages in computational modeling (checking, comparison, and selection of models). Since machine learning approaches are used in this form of cognitive modeling, our article will use relevant terminology from these disciplines.

A summary of the model building steps considered in the workflow for developing computationally rational cognitive models with POMDPs. The model building workflow is tangled and can proceed in several different directions along the arrows shown, and it is iterative with a series of model revisions based on model checks. Models often start with simple specifications and they are gradually improved in quality and complexity until they capture the key elements of empirical human data and satisfy the modeling goals

Workflows are beneficial in and of themselves and complement the progress achieved through the development of new tools and software in the field. For instance, powerful software libraries in statistics and machine learning have made it easier to develop complex models of human behavior. However, it has become equally possible to build models without putting much thought into them (Hullman et al., 2022 ; Kapoor et al., 2023 ) or to use methods such as deep learning when there is a paucity of training data, which is the scenario for almost all cognitive science experiments and user modeling approaches. Such practices often result in models that are poorly specified—that is, untrustworthy models that make patently inaccurate assumptions about the world, and are neither useful for insight and understanding nor for prediction and generalization. Misspecified models may fit observed data well but are fragile and break down under even the slightest variation in the environment. However, even though there are overheads to following a model building workflow, the many implicit sanity checks, diagnostics, and tests can increase the quality, usefulness, and robustness of models. Models developed through a structured workflow tend to be more carefully thought out, tested, and reported upon. In addition, anchoring modeling choices to a workflow is also one way to be transparent about the modeling process. Hence, we conclude that principled workflows rank among the keys to better cognitive science, conferring benefits for validity, reproducibility, and accumulating valuable scientific results. However, we must stress that workflows are better treated as guides rather than rigid prescriptions, to leave flexibility for situation-specific modeling contexts and goals.

A considerable portion of the workflow is related to specifying the agent’s internal environment. Introducing such causal underpinnings is especially beneficial in cases where explanation, control, and prediction are crucial. However, this simultaneously renders computationally rational models vulnerable to misspecification: the assumptions and processes articulated by the model might yield poor approximations of reality or turn out to be simply wrong. This is why our workflow’s iteration incorporates such steps as model checking, which studies have linked to reduced misspecification and greater robustness across multiple model classes and modeling domains (Box, 1980 ; Wilson & Collins, 2019 ; Gelman et al., 2020 ). Taking into account these considerations, we pursue the following objectives in designing the workflow:

Pre-specifying objectives: Articulating a domain of behavior in advance (“This is what we aim to model”) and specifying both the key phenomena in the data and metrics for success is essential for tracking the progress of the modeling effort. Although all modeling is explorative and subject to change accordingly, working with clear goals from the outset may safeguard against unhealthy practices such as declaring modeling goals opportunistically after the fact and without validating the model for them appropriately.

Separation of policy learning and parameter fitting: Some free parameters of a computationally rational model describe the policy, while others characterize the individual. Though the two often get optimized jointly, they are conceptually distinct.

Model checking and validation. These practices increase the quality of models by detecting overfitting and ill-defined structures and parameters.

Scaling up iteratively: Getting the model right the first time is nearly impossible, in that success rides not only on theory-based insight but, regrettably, also on luck (e.g., the policy must converge and depend on random-number seeds). Therefore, we recommend deliberately planning for iterative model building that starts with a minimal goal set and expands in each iteration towards the full scope.

Our presentation begins with a review of prior attention to workflows for this class of modeling. Against that backdrop, we then explicate the proposed workflow, with a focus on each step’s major decisions. The discussion pinpoints key considerations and highlights central choices specific to computational rationality. Because of the emerging nature of this area, many of the discussions we offer are meant to expose their respective pros and cons choices rather than prescribing an approach.

To anchor our discussion in a practical case, we present a running example from recent modeling: the gray box at the end of each section addresses an attempt to extend state-based models of human typing behavior (Jokinen et al., 2021a ) on touchscreens to a pixel-based agent (Shi et al., 2024 ). Typing exemplifies the challenge well since it involves adapting eye–hand coordination in accordance with external bounds (from design, intelligent text entry, etc.) and internal ones (e.g., working memory capacity and noise levels). The agent, limited by partial observability (foveated vision), must decide where to direct attention at any given time: the keyboard, the text display, the backspace key, or some other element. To be useful, a model of typing behavior should be able to predict the effects of changing conditions counterfactually (Oulasvirta & Hornbæk, 2022 ). For instance, how will the user’s typing strategy and performance change if an intelligent text entry system enters the loop, when the user cares less about errors, or if there is a switch in keyboard layout? Moreover, to capture individuals’ differences, the typing model needs to be tunable for individual-level data; this implies that we should be able to invert them when given humans’ data.

Background: Workflows for Rationality-Based Models of Human Behavior

In the context of modeling, a workflow is a systematic approach for building valid, reliable, and useful models through a sequence of iterative model building steps. A workflow captures both formal knowledge and tacit knowledge into clear and actionable guidelines. These guidelines may draw from numerous types of knowledge: theoretically proven solutions, best practice identified in the literature, an experienced colleague’s wisdom, awareness-raising reflection, etc. A workflow provides a systematic framework for navigating the garden of forking paths of modeling choices to produce high-quality models.

The need for workflow-based model development is accentuated in cases that combine the two cultures of modeling (Breiman, 2001 )—where our model simultaneously combines generative processes informed by psychology theory with those learned from data using machine learning—computationally rationality models that use reinforcement learning are an example of such a hybrid approach.

Workflows for computational models often distill them into four main steps: (i) model specification, (ii) model fitting, (iii) model checking, and (iv) model selection. Each step comprises sub-steps, diagnostic checks, and best practices for model building. In this article, we do not deal with data collection as a part of the modeling workflow, even though data are important for modeling. We assume here that data has already been collected in a valid and rigorous manner and focus on the iterative model building workflow given such data.

While the general objective behind modeling workflows (e.g., see Schad et al., 2021 ; Zhang et al., 2020 ; Wilson & Collins, 2019 ; Gelman et al., 2020 ; Schad et al., 2022 ; Patterson et al., 2023 ; Grinsztajn et al., 2021 ) is to enhance the quality of scientific research, their specific steps and emphases differ, reflecting the distinctive goals and methods of the respective domains. This article specifically targets the development of computationally rational models instantiated using reinforcement learning.

This paper extends workflows for rationality-based models of human behavior to the case of computational rationality. In rational analysis (Anderson, 1991 ; Chater & Oaksford, 2000 ), the idea is that cognition adapts to requirements of the environment. Accordingly, the modeling centers on developing agents that produce optimal behavior for a given task and the statistical structure of its environment. Lieder and Griffiths ( 2020 ) recently sketched out a high-level workflow for resource-rational (computationally rational) analysis (see also Howes et al., 2009 ), splitting it into five steps:

Start with a functional description of an aspect of cognition

Articulate the algorithms for cognition, with cost and utility values

Pick the algorithm that optimally trades off cost and utility

Compare with empirical data

Iterate until satisfied with the result

While this outline provides a good sense of the general flow, the emphasis is on understanding a particular aspect of cognition, and situating these perspectives in Marr’s levels of analysis framework (Marr & Poggio, 1976 ), rather than modeling behavior in complex, everyday contexts. Consequently, it overlooks the detailed steps necessary for practical modeling, especially in defining decision-making problem and estimating the (boundedly) optimal policy of the agent (Russell & Subramanian, 1994 ). There is a gap in integrating these cognitive and rationality-focused aspects of computationally rational models with the general computational modeling building frameworks in cognitive science, machine learning, and statistics. Our article bridges this gap by presenting a comprehensive workflow for developing computational rationality models using reinforcement learning. This workflow is informed by our experience developing such models and integrated with established modeling approaches; it aims to avoid common pitfalls and improve the validity and effectiveness of our resultant model.

No known workflow met all of our needs when we began work on the computationally rational typing model. Such development requires understanding of rational analysis, deep reinforcement learning, and computational statistics.

Defining the Goals of Modeling

Before starting to build a model, it can be very useful to explicate the goals of modeling and plans for achieving them. Preregistration of modeling goals and procedures have been promoted for their possible reproducibility benefits (Lee et al., 2019 ; Hofman et al., 2023 ; Kapoor et al., 2024 ), but we consider it useful for guiding the modeling workflow and decisions made along the way, for model evaluation against our articulated objectives, for transparency, and improved communication behind the motivation of choices made. The general aspects of the model worth articulating ahead of model building include the following:

Purpose: Why is the model being created?

Scope: What theories, behavior, and data are covered, and to what extent? Which human behaviors do we want to reproduce, under what types of theoretical assumptions, and in line with which data?

Baselines: What models can address the relevant goals and scope?

Metrics: Which metrics are best suited to evaluating the model’s quality?

Clarifying Purposes

Developing a model should start by laying out the general goal for the model: what is its purpose, and why does it need to be created? For example, the goals for modeling human behavior may be theoretical—to understand and explain general mechanisms underlying human cognition across varied contexts, and advance scientific knowledge. Or the goals may be practical, where they are relevant for a particular application setting, where aspects such as predictive performance and computational efficiency may override explanatory considerations. Each modeling endeavor may have different objectives and purposes, and it helps to articulate them before building a model. Articulating goals upfront can serve as sanity checks to keep in mind as we iteratively develop a model and make decisions throughout the model building process.

Setting the Scope

After explicitly stating the overarching purpose, we need to define the model’s intended scope. There are three aspects to this: (i) theory scope, (ii) behavior scope, and (iii) data scope.

The first involves specifying the psychological assumptions in terms of modeling decisions. For example, if we are interested in modeling visual search of natural scenes, we might decide to include assumptions about foveated vision and how the ability to detect visual features drops off in peripheral vision (Kieras & Hornof, 2014 ). Setting the theory scope involves picking out the relevant aspects of cognition that will form the foundation of the model: we might choose to focus on those related to attention, memory, perception, or goals and motivation.

Secondly, we must specify the behavior scope. Involving the empirical phenomena and environments that the model should cover, the behavior scope delineates the conditions in which the model is assumed to operate and how it does so. Often, when we model data collected in an experimental study or a set of studies, the behavior scope can be dictated by our theory scope, experiment settings, conditions, and behaviors—the types of stimuli presented, the objectives given to the participants, the nature of responses elicited from them, etc. The practical application context can be another determinant of the behavior scope. For instance, we may have in mind a model of typing that makes predictions for novel keyboard layouts. In that case, the behavior scope should cover such additional keyboards.

Finally, any data and data patterns (i.e., behavioral patterns) that we want the model to account for fall under data scope. Examining and understanding the data is important for gaining insights into the data patterns we would like the eventual model to predict and explain. Exploratory data analysis (EDA) refers to such a process of examining the data and its variables by various means such as visualizations and descriptive statistics (Wilson & Collins, 2019 ; Gelman et al., 2020 ). The objective here is to concretize the modeling goals by grounding them within the data at hand. EDA can also act as a check on the quality of the dataset and allow us to examine if there are any outliers or critical errors during data collection which reduces the usefulness of the data. Also, one has to assess the quality of the dataset, pinning down factors such as noise and whether there are outliers or missing data. Even more critical is the need to visualize the patterns identified as belonging to the data scope. Tools such as scatterplots, violin plots, histograms, and animations can serve this purpose. These equip us with a comparator that enables better gauging the model’s behavior at the model checking stage.

Defining Baselines

Choosing meaningful baseline models is important for demonstrating the value and novelty of our proposed model in a valid manner, within the context of existing approaches. Hence, once interesting data patterns have been identified and prioritized, it helps to consider existing models from the literature that are relevant to the task and patterns we consider important. Predictive performance is a key indicator of a model’s effectiveness, and it is hence a common practice to choose current state-of-the-art approaches in the field as a baseline against which to compare our eventual computationally rational model. In some cases, theoretically interesting alternatives may serve as more natural baselines as they allow us to contrast the implications of different assumptions on model behavior. In addition to testing the effectiveness of our eventual model, these baselines can also inspire ideas about what to incorporate in our model and serve as a basis on which our model can be iteratively built. The initial goal may be to perform at least as well as the baselines, with the ultimate aim being to surpass their performance. If the baselines we select are available in code, we recommend trying them out against the visualizations produced in the steps above.

Establishing Model Performance Metrics and Diagnostics

Model performance metrics are quantitative measures that can be used to assess how well a model performs against its objectives. For example, common metrics to measure predictive performance against observed data can be metrics such as accuracy, precision, and root mean squared error. In addition to the broad prediction metrics, we may also be interested in how well predictions capture qualitative data patterns that we had declared as important to capture (e.g., in visual search, a pattern of search times being longer when the target and distractors are of the same color). We recommend mapping out key metrics that align with the modeling objectives to ensure that the workflow remains focused on the modeling goals and to measure progress on these objectives.

Diagnostics are measures that can identify problems that arise during modeling and often provide insight into the inner workings of the model. For example, feature importance (Casalicchio et al., 2019 ), which indicates the features most influential for a model, can be diagnostic to determine if any neural network components used in the model are emphasizing sensible features or not; sensitivity analysis, which evaluates the model’s performance under different settings, can provide insights into the model’s robustness and sensitivity to different assumptions. It is recommended to think of these main metrics and diagnostics up front to be used throughout the various stages of modeling. We offer several examples in the course of the discussion below, but in the ideal case, one should select metrics that reflect the scope well and pick diagnostics are maximally informative with regard to how well the modeling is progressing.

Planning the Project

The complexity of models in this domain makes it nearly impossible to create a valid model that fulfills all modeling goals in one attempt. An iterative modeling workflow that starts with simpler accounts for observed data has several benefits. Simpler models have fewer components and allow us to better diagnose and debug modelling issues as they come up. An iterative workflow also makes it easier for modelers to gradually improve their understanding of observed behavior and model behavior. Such an iterative workflow structure can be strategically planned, and we turn to the notion of a research ladder, describing a few milestones for the modeling process in progressively higher fidelity. Because policy optimization is often a time-consuming step, it also makes sense to work with simpler versions of the problem at first and ensure we are on the right track before increasing complexity. Our planning typically defines ladders with 3–4 steps, each for matching (i) a certain human phenomenon at (ii) a certain level of fidelity (e.g., trends only or absolute numbers). On some occasions, one may choose the alternative strategy of starting from a higher rung of the ladder from a known and well-understood model, and then stepping down by simplifying its assumptions until we come to a better understanding of how the original model needs to be changed; thereafter, one can scale up the ladder again until the changes yield satisfactory predictive performance and satisfies our modeling goals.

The primary goal set for the touchscreen-typing model was to gain insight into the behavior of human typists and subsequently use the model to evaluate keyboard designs. Accordingly, we specified that the model should coordinate finger and gaze movements for typing phrases on touchscreens in a human-like manner. We wanted to improve accessibility by simulating users who vary in capabilities and predicting design changes’ impact on their performance. We set the scope for the typing model by way of three considerations.

Firstly, the model should have the capacity to replicate main tendencies in an empirical dataset of typing (Jiang et al., 2020 ). Its policy should closely align with human data and replicate key metrics and phenomena documented in the paper. Secondly, typing patterns vary considerably between individuals, with some displaying faster typing, some being slower, some making more errors, and some proofreading more. It is vital to model these individual differences, especially for applications that support special user groups. We aimed to replicate the distribution of behavior that reflects a wide range of user populations, as illustrated in the figure below. Finally, we sought a model that performs well not only for the specific keyboard it was trained on but also with previously unseen keyboard layouts. It had to function well across a wide range of keyboard designs, layouts, and intelligent features.

To ascertain how well the model matches real-world eye and finger movements in terms of typing speed, error correction, and proofreading, we chose six representative metrics from work by Wobbrock ( 2007 ); Arif and Stuerzlinger ( 2009 ), and Feit et al. ( 2016 ): words per minute (WPM), inter-key interval (IKI), amount of backspacing, error rate, number of gaze shifts, and gaze-on-keyboard time ratio. We selected the latest state-based RL model (Jokinen et al., 2021a ) as our baseline.

To scale up our modeling efforts, we developed a research ladder with a simple finger-movement pattern as its lowest rung. More complex eye–hand coordination patterns were higher up the ladder. After attending to finger behavior, specifically typing speed (represented by WPM and IKI figures), we added the layer of difficulty from error-correction strategy, by incorporating error rate and Backspace presses. Finally, we modeled gaze behavior, which involves more complicated mechanisms connected with proofreading strategy (gaze-shifting, etc.).

Model Specification

In the implementations of computational rationality via POMDPs, we model humans as agents interacting with their external environment through the lens of their own cognitive limitations, such as bounded rationality, limited attention, and imperfect memory; models are conceptually partitioned into an internal and an external environment, as Fig.  1 illustrates. The external environment represents the task environment encountered by the agent. For example, a model addressing a car-driver’s external environment might contain simulations of the steering wheel, the pedals, and the vehicle as a dynamic object in traffic. The internal environment , in contrast, encompasses the cognitive processes internal to the agent, including the generation of observations and reward signals. This internal/external division is critical. The agent does not interact with the external environment directly; instead, engagement is with an internal representation of the world, shaped by the agent’s cognitive processes. The internal representation allows the agent to interact with its environment in a way that is computationally feasible for everyday tasks and situations (Lieder & Griffiths, 2020 ). A partially observable Markov decision process, or POMDP (discussed below), formalizes how these environments are available to the agent. It translates the underlying state of the external world, which is only partially observable, into a form that the agent can perceive and act upon.

Specifying the Internal Environment

While optimal adaptation exclusively to the design of the external environment would be an outcome of (unbounded) rational behavior, optimal adaptation to the internal environment results in a computationally rational behavior that adapts not only to the external environment but also to the agent’s computational bounds imposed by its cognitive limitations. The agent’s design typically involves cognitive processes such as memory, perception, and other faculties through which it perceives and acts in its world (the external environment). The representations produced cover a spectrum from simple noisy perception to complex representations of the agent’s goals, beliefs about the external environment, tool systems usable in the external environment, and other constructs. Other factors captured within the internal environment might consider fatigue, stress, and similar physiological phenomena.

For a computationally rational model, hypotheses about cognition are encoded in the internal environment. There are various ways to implement the assumptions for the internal environment: via symbolic approaches, Bayesian inference, rules, or neural networks, among other mechanisms. Modelers express the bounds of the internal environment through the cognitive model’s parameters. This step goes by the term “parameter specification” and involves “fixing” relevant parameters to a particular value so as to codify an assumption we are making about the agent or environment. For example, we may want to fix or specify the parameters of not only a known external environment, but also the psychological assumptions about the agent such as a particular working memory capacity that is generating observed behavior. Parameters are typically fixed or specified based on prior knowledge, known empirical findings, or theoretical considerations. With Bayesian methods, it is possible to specify degrees of beliefs in parameters’ values by constructing a prior probability distribution (Mikkola et al., 2023 ) and update these prior beliefs into a posterior probability distribution given data. Bayesian approaches to computationally rational POMDP models are currently uncommon, but quite possible—for example, Kangasrääsiö et al. ( 2017 ) used a Gaussian prior for plausible fixation durations in their menu search model; Shi et al. ( 2024 ), placed a uniform prior distribution over a range of plausible parameter values, and explained in the typing example. Also, see Aushev et al. ( 2023 ). However, Bayesian approaches are not yet common in the field, due to the computing costs when using a distribution over parameter values. After appropriately specifying any parameter’s value or prior, it is also important to identify which parameters vary at different levels: individual-level parameters that vary between individuals, group-level parameters that vary between groups, and population-level parameters that remain constant across the entire population. While we specify theoretical assumptions by parameter specification or the specification of their priors, an agent may also be designed to perceive some of those variables (e.g., its stress level) and even manipulate them (choosing what to store in working memory, etc.) as it interacts with its environments.

Designing the Reward Function

The second step in model building involves specifying these internal reward signals. This reward can be defined based on one of two perspectives: (i) the reward signals are considered to be generated by the external environment, and these rewards are a proxy for internal rewards, or (ii) it can be defined internally based on representations in the internal environment. The former approach is the more straightforward—from this point of view, rewards indicate the goodness of any given action as defined by the task, and the agent aims to maximize the long-term rewards even if it is done in a computationally rational manner taking into account constraints posed by both environments.

From a psychological perspective, the rewards are always internal. External rewards can sometimes be a proxy for internal rewards; but, when this assumption fails, we need to explicitly specify a psychological mechanism for how humans generate internal rewards (Lee et al., 2023 ). Human policies are not completely determined by the task environment—we use curiosity and intrinsic motivation to explore the world even when it lacks explicit rewards.

When reward functions are specified as a feature of the internal environment, we need to investigate the learned policies to understand the behaviors that these subjective rewards produce. That is, the agent has goals that it is externally expected to achieve (e.g., pressing a particular key), however, the reward function may, in fact lead to other types of behavior (e.g., pressing no keys if mistakes are heavily penalized). The way to verify this is via running simulations of the model and examining whether the reward function is leading the agent towards the desired objectives.This type of verification differs from model checking in the sense that we are mainly ensuring that the agent can technically achieve the objectives it has been specified.

When a reward function is not specified accurately, we may observe a phenomenon termed “reward hacking” (Clark & Amodei, 2016 ), where the agent discovers unforeseen shortcuts to achieve high rewards without actually learning the desired behavior. As the agent and environment increase in complexity, it becomes harder to design reward functions that do not reward hack. Recent research provides subworkflows to prevent this phenomenon, for example, by detecting anomalous or aberrant policies (Pan et al., 2022 ) or by iteratively shaping reward functions (Gajcin et al., 2023 ).

Specifying the External Environment

The third step is to specify the external environment. It can represent a task environment, for example, a setting where a user is supposed to type, or it can be an open world like Minecraft. Practically, however, projects often start with a gridworld or similar “toy environments”, which may help focus on the bare essential aspects of the problem at hand. There is a wide variety of software available for creating and working with external environments ranging from physics simulators (e.g., MuJoCo) to agent simulators (e.g., Sumo and TFAgents), as well as software emulators (e.g., AndroidEnv), among others. What matters is that the software that is used is (i) fast enough to permit training policies and (ii) allows interaction: the internal environment must be able to provide data to the perception and enact the agent’s actions.

Defining the Agent’s Decision-making Problem

The elements defined in the previous sections, together, specify a POMDP: a sequential decision-making problem under partial observability. The POMDP is defined by the tuple \((\mathcal {S}, \mathcal {A}, \mathcal {O}, T, O, R, \gamma )\) where \(\mathcal {S}\) is the state space, \(\mathcal {A}\) is the action space, \(\mathcal {O}\) is the observation space, \(T: \mathcal {S} \times \mathcal {A} \times \mathcal {S} \rightarrow [0, 1]\) is the state transition probability function, \(O: \mathcal {S} \times \mathcal {A} \times \mathcal {O} \rightarrow [0, 1]\) is the observation probability function, \(R: \mathcal {S} \times \mathcal {A} \rightarrow \mathbb {R}\) is the reward function and \(\gamma \in [0, 1]\) is the discount factor. Given an action \(a \in \mathcal {A}\) and current state \(s \in \mathcal {S}\) , the agent transitions to a new state \(s' \in \mathcal {S}\) with probability \(T(s, a, s')\) , receives a reward R ( s ,  a ), and observes \(o \in \mathcal {O}\) with probability O ( s ,  a ,  o ).

The POMDP formalism is designed to align with the principles of computational rationality. It recognizes that an agent’s knowledge of the world is often incomplete and models this uncertainty through partial observability. The state transition and observation probabilities, along with the action space, encapsulate the concept of a dynamic and probabilistic world, which an agent needs to navigate with its limited cognitive resources. Cognitive resources can be flexibly represented within this framework as part of the transition function, observatin, and/or actions. Finally, agents act to maximize the sum of discounted future rewards, reflecting the process of utility optimization.

A Note on State Abstractions

A critical design choice concerns the observation function. Because the full state of cognition (the internal state) is complex, a computationally rational agent typically only observes part of it. While partial observations hold clear value—they make exploration and policy optimization significantly easier—this simplification loses information, some of which might be relevant for the task. The choices made in defining the observation function carry a risk of errors compounding over a long planning horizon (Talvitie, 2014 ; Ye et al., 2021 ; Starre et al., 2022 ). Prior work offers some guidance for creating effective observations. Theoretical research has established that observations can safely aggregate across states that are identical in their reward and transition probabilities; likewise, states with identical Q -values under the optimal policy can be aggregated (Li et al., 2006 ). The literature also deals with stronger aggregations, which combine more states together. However, policy optimization such as Q-learning on these aggregated state spaces is not always guaranteed to produce a policy that is optimal (under the original state space) (Li et al., 2006 ). Approximate versions of the aggregation criteria are also available to aggregate all states with reward values and transition probabilities that are not identical but within \(\varepsilon \) of each other. Though approximate aggregations introduce some error, the resulting value loss is polynomially bounded for specific aggregation strategies (Abel et al., 2016 ).

The model we built is a computationally rational typist (CRTypist). We designed its internal environment to specify several crucial bounds of human vision, motor control, and working memory. That environment functioned as a bridge between the agent and the touchscreen. The Vision module processes information as pixels from a small focal area and blurred information from peripheral vision. Finger simulates pointing to a specific pixel on the screen, which may entail position errors stemming from rapid movement or lack of visual guidance. Finally, Working Memory stores information from the vision and finger movements, assessing the typed text while accounting for uncertainty due to time decay. All components of the internal environment feature separate parameters representing their capabilities. The agent’s controller does not interact with the touchscreen directly. It interacts with the internal environment by setting goals for the eye and finger movements. Pursuing these goals, the Finger and Vision modules point to a specific position on the touchscreen.

We design the reward function by considering the speed–accuracy tradeoff. In essence, the reward received is a compromise between how accurately and how quickly one can type. The reward is given at the end of each episode, upon pressing the Enter key. The mobile touchscreen served as the external environment. We captured pixel-representation images from a software emulator to obtain the visual information from the keyboard and text display both.

Within the framework outlined above, we formulated the POMDP thus:

Within the state space, \(\mathcal {S}\) , a state \(s_t\) consists of the pixel representation of the touchscreen display at timestep t , including both the keyboard and the text area.

In the observation space, donated as \(\mathcal {O}\) , an observation ( \(o_t\) ) consists of the beliefs from the working memory (which stores information gathered from foveal and peripheral vision).

An action a in the internal environment sets target goals for both vision and finger—within the action space, \(\mathcal {A}\) . It leads to the behavior the typist can execute on touchscreens—gaze movements, tapping with a finger, etc.

In the context of typing, the reward function \(\mathcal {R}\) is expressed with a speed–accuracy tradeoff in the cognitive model of the mind: the goal is to type correct target phrases as quickly as possible.

Policy Optimization

As with any other class of computational models, the performance and predictions of computationally rational models are influenced by how the models are configured. We can divide the model-configuration variables into two types: hyperparameters and model parameters. Hyperparameters are variables that are external to the model and whose values are not learned from the data but are specified by the modeler to determine how the model learns. Model parameters, on the other hand, help to form the internal structure of the computationally rational model, and are inferred or estimated based on the data to which the models are fitted. Appropriately specifying model parameters and hyperparameters is essential for obtaining valid inferences and predictions from the model.

Model parameters can be further divided into three categories: (i) the parameters of the internal environment that have a clear interpretation and a theoretical grounding, (ii) the parameters that represent the optimal policy of the POMDP, and (iii) parameters of the external environment, such as those representing task distributions.

The parameters of the internal environment are psychologically meaningful parameters that may either be specified to reflect modeling assumptions (see previous section), or they can be estimated from observed data through parameter fitting (see next section).

On the other hand, the policy parameters parameterize a neural network that represents the policy and, therefore, are difficult to interpret. Given some initial values for the POMDP, a policy repeatedly directs the internal environment and is given a reward. Over many episodes, the policy is trained to maximize the reward received until a given stop condition is met, which is indicative of satisfactory level of performance. While POMPDs have proven more useful than MDPs as representations of human cognition, they are notoriously challenging to solve in practical problem instances due to partial observability. The optimization of the POMDP policies almost never results in exact solutions; working with them requires a careful selection of a suitable approximation method. At the moment of writing, there is no one-stop solution available; rather, the best method depends on a number of factors and typically requires the attention of an ML engineer.

An exhaustive review of the methods is beyond the scope of this article. Nevertheless, we find the following attributes of POMDPs important to consider while choosing the policy optimization method (see also Kurniawati, 2022 ): (i) the size and structure of the state and action spaces, (ii) whether the state and action spaces are continuous or discrete, (iii) the nature of the components embedded within POMDPs (e.g., a neural network component), (iv) whether inference is needed online or offline, (v) the amount of computational resources available, and (vi) the size and structure of the observation space.

Available RL methods for policy optimization of POMDPs can be divided according to four dimensions relevant here: (i) model-free vs. model-based, (ii) on-policy vs. off-policy, (iii) deep vs. classical RL, and (iv) policy vs. value-based approaches. Model-free methods that include deep Q-Networks (DQN; Mnih et al., 2015 ), Proximal Policy Optimization (PPO; Schulman et al., 2017 ) and Q-learning (Watkins & Dayan, 1992 ) do not require an explicit model of the transition function, while model-based methods do. The benefit of a model-based approach such as Partially Observable Monte Carlo Planning (POMCP; Silver & Veness, 2010 ) is that planning (of actions) can be done directly using the model; however, learning the model for POMDPs remains an open problem, and these methods are slower to execute. On-policy methods such as actor-critic asynchronous advantage (A3C; Mnih et al., 2016 ) update policy during learning, while off-policy methods (e.g. PPO) collect rollouts offline and learn based on them. The best known classical method is Q-learning. This method is limited to small state-action spaces. Most work presently uses deep RL-based methods because of their superior generalizability and efficiency. Nevertheless, the use of deep-RL-based methods is a challenge due to their generally unstable behavior during training. Policy-based methods directly optimize the policy to maximize rewards and can handle continuous action spaces, while value-based methods which optimize the value functions tend to be preferred for discrete action spaces (Kurniawati, 2022 ).

Reward Shaping

Reinforcement learning (RL) methods, both value-based and policy-based, aim to optimize an agent’s behavior to maximize the cumulative reward. However, in scenarios where the reward function is sparse and where few of the state transitions come with informative rewards, the process of identifying reward-maximizing behavior hinges on extensive stochastic exploration. Therefore, when the inherent reward structure for a task is sparse (e.g., primarily a terminal reward when the task is completed), it may be important to expedite learning for practical reasons. This may be achieved by introducing additional rewards to guide the RL process towards the optimal policy, by what is known as reward shaping. For instance, we might add a distance-based reward that focuses on exploration in parts of the policy space that are likely to offer good models of cognition.

Reward shaping techniques need to be implemented with careful consideration of how they impact agent behavior; small changes to reward functions can significantly change the optimal policy. One recommended mitigation mechanism is using a potential-based shaping term: suppose that we have a reward function \(R(s,a,s')\) rewarding a transition from state s to \(s'\) through action a . We could specify a shaping term \(R_s(s,a) = \gamma \Phi (s') - \Phi (s)\) where \(\gamma \) is the discount factor and \(\Phi \) is an arbitrary function. It can be shown that the shaped reward \(R(s,a,s') + R_s(s,a)\) has the same optimal policy as \(R(s,a,s')\) (Ng et al., 1999 ). In the case of distance-based rewards discussed above, we could, for instance, define \(\Phi (s)\) as the distance from the goal state, thereby rewarding the agent for getting closer to the goal and penalizing moving further away from it. The more common practice of ad hoc reward design is however typically “unsafe,” meaning that it leads to unintended or undesirable outcomes (Knox et al., 2023 ; Booth et al., 2023 ). A particular concern is reward shaping that is intended to increase efficiency but which changes the cognitive theory. Much like the practice of model building, designing more robust rewards can also benefit from an iterative workflow, and continuous refinement of the reward function, including with the help of methods that surfaces problematic edge cases where undesired behaviors are being incentivized by the shaped rewards (He & Dragan, 2021 ).

Curriculum Learning

Just as humans do, RL algorithms sometimes struggle when faced with highly complex tasks. In many cases, it is possible to “kickstart” the learning by beginning with training from a simplified version of the task and gradually increasing the complexity of the task. This is called curriculum learning (Wang et al., 2021 ; Bengio et al., 2009 ). Curriculum design is essential for effective curriculum learning, yet there is little theory-based guidance in such a design at present. In general, it is best to adapt the curriculum to the speed at which the RL algorithm learns to perform these increasingly complex versions of the task, increasing the complexity only when it has learned to handle the current step in the curriculum.

Hyperparameter Tuning

Hyperparameters—dropout rate, batch size, number of epochs, learning rate, etc.—are of practical importance because of their role in learning performance. To ensure that the model can adequately account for the data, we recommend careful hyperparameter tuning. The best option is to use an optimization method that, after enough trials, guarantees values approaching the optimum (e.g., Bayesian optimization). The next-best option is to rely on others’ values, from prior articles. Whatever the method of choice, open science practices require reporting of values that were used.

We trained the typing model’s policy with the goal of generalizability. The application goal was for it to cope with various keyboard designs and various individual-specific factors with links to cognitive capabilities (e.g., related to vision, finger agility, and working memory). To that end, the training process employed two loops: an outer loop randomly selects keyboard images and cognitive parameters, and an inner loop applies RL to learn the policy for optimizing the reward. For the algorithm, we chose Proximal Policy Optimization (Schulman et al., 2017 ), which offers a reasonable compromise in terms of practical implementation, sample complexity, and ready tuning.

To “boost” to the training, we employed reward shaping and curriculum learning. Because the traditional Boolean feedback for correct/incorrect typed text proved too sparse. The typing model uses the character-error rate for a distance-based reward, instead of a Boolean reward. This approach incentivizes progress towards typing the given text accurately. To facilitate initial learning, we could start with individual characters, then proceed to word level and ultimately advance to typing of phrases.

As for the tuning of hyperparameters, we began with settings informed by our baseline model (Jokinen et al., 2021a ), for total timesteps, learning rate, and batch size. We then made greedy adjustments to these hyperparameters on the basis of the training-process convergence and average episode length.

Parameter Fitting

The parameters of the internal environment are important in a computationally rational model because they can be adjusted to “fit” the model’s behavior to human data and generate predictions that approximate human behavior in a given situation, by the process known as parameter fitting or parameter inference. Parameter fitting is used to estimate the values of free parameters based on observed data and is not relevant for the parameters fixed to specific constant values during parameter specification. The fundamental assumption underlying a computational rationality model is that the policy controlling the internal environment is optimal. Accordingly, the policy (and its parameters) will be derived directly from the internal environment via an optimality condition. Adapting the model to reproduce human behavior is therefore done by manipulating the parameters defining the internal environment. This in turn results in a change in policy and consequently a change in predicted behavior.

Computationally rational models generally are not amenable to traditional parameter fitting techniques based on maximizing the likelihood of the observations (Myung, 2003 ). This is because (i) calculating the observations’ likelihood seldom falls to these models’ techniques and (ii) estimating it from model-output samples is generally infeasible due to the wide variety of behaviors possible. These conditions necessitate a different notion of model fit, often expressed in terms of deviation between what the model predicts and what was observed. To avoid calculating distances between high-dimensional data, it is a generally recommended practice to define this discrepancy in terms of the difference between statistics or other summaries calculated on both the predictions and the observations. This also lets us strip out irrelevant differences between the observations and predictions. Generally, any suitable distance or divergence function can serve to express the amount of deviation.

Various automated processes exist for fitting model parameters once the discrepancy between the model’s output and real-world observations has been captured. Manual fitting of the parameters in the traditional manner (trying out values until the model’s predictions “look like” the training data) might be tempting initially. However, this can turn out to be extremely labor intensive, especially in cases involving numerous parameters, and there is, in fact, no guarantee of finding the optimal values for them. Automated methods, on the other hand, do not suffer from these shortcomings.

Which automated methods are preferred? Given that mapping internal environment parameter values to the divergence evident in the model’s predictions involves optimization of the policy, calculating a gradient applicable to those parameters is impossible in most cases. Therefore, we confine our discussion here to gradient-free methods. The simplest potentially relevant automated technique is grid search: trying all possible combinations of parameter values and selecting the one that yields the smallest discrepancy. While this method does find an optimum, doing so is prohibitively expensive when the parameter space is large. Furthermore, parameters with a continuous domain require us to select an appropriate discretization, which is not necessarily straightforward. An alternative is to take an Approximate Bayesian Computation (ABC) approach. This refers to a large class of methods that can approximate a posterior over the model’s parameters with only sampling-level access to the model (Sunnåker et al., 2013 ; Aushev et al., 2023 ). Efficient implementations such as BOLFI (Gutmann & Corander, 2016 ) can quickly identify parameter values with a high posterior density. However, ABC is often too computationally intensive for real-time use. When we need rapid inference of the parameters, we can amortize the cost of these inferences. This is usually achieved via an ML model trained to predict the correct parameters for the model—i.e., the inference something like an ABC method would have drawn for the given set of observations (Moon et al., 2023 ).

The typing model implemented several internal parameters related to vision, finger action, and working memory. The vision module’s parameterization affects the speed of encoding, while finger-related values capture movement accuracy and the final set of parameters represents the uncertainty of the information held in working memory. To optimize the model for median human behavior, we fitted these parameters to the dataset. During the optimization process, we relied on the Jensen–Shannon divergence when designing the acquisition function. This measurement helps to accurately assess the distance at the distribution level between the simulation- and human-generated data. A shorter distance (less divergence) indicates greater similarity between the model and the human baseline.

Model Checking

Model checking refers to qualitative and quantitative procedures that aid in verifying the validity of the models we build (Gelman & Shalizi, 2013 ; Mayo, 2018 ). It involves comparing model predictions with observed or expected data and examining the model in other respects. This is an important part of the iterative modeling workflow, and influences the decision to modify or accept models. Model checking is often carried out after fitting the model to empirical data, is fundamental for any process of iterative model development. Among the general classes of procedures available for model checking are (i) examining prediction accuracies and variances, (ii) graphically examining observed vs. predicted values, (iii) and turning to residuals, etc. for clues as to where the model is working well and where it is not.

Prior and Posterior Predictive Checks

Model checking can begin as early as when we pick prior distributions on parameters or specify fixed parameter values. The prior predictive check involves simulating predictions based on these specifications (known as the prior predictive) and checking if they predict plausible data in a given experiment. If a prior predictive check fails, we return to the drawing board to re-specify the parameters or their priors so that they better align with our theoretical intuitions as well as prior beliefs about empirical data; we may even decide to change our the model architecture if we realize that our assumptions are not sound enough. Posterior predictive checks are similar, but involve simulations after observing the data (known as the posterior predictive), and carrying out parameter fitting or inference—checks here are about whether predictions based on the inferred parameters are able to predict the observed data at all (Baribault & Collins, 2023 ). If we are dealing with posterior distributions of parameters, predictions are generated by sampling from the posteriors and executing the model.

Checks for Goodness of Fit

Model checking helps us assess goodness of fit, potentially identify model misspecification, find model refinement opportunities by detecting peculiarities in the empirical data, and compare models qualitatively. Several factors may lie behind invalidity or inaccuracy revealed by this step, such as (i) spurious or missing data, (ii) flaws in the model assumptions, (iii) inappropriate model structure, (iv) coding errors, (v) overfitting/underfitting for the data, and (vi) misapplication of theory. By iteratively refining and checking models, we converge to a plausible and well-scrutinized model that satisfactorily fits the data. This step can be carried out both qualitatively by visualizing model predictions against our data and examining the goodness of fit, and whether the data patterns of interest laid out earlier are captured by the model. Quantitative methods to assess goodness of fit often involve some measure of prediction error.

Parameter Recovery Checks

Parameter recovery checks offer another powerful way to assess model behavior (Wilson & Collins, 2019 ; Heathcote et al., 2015 ). Having generated data from a known parameterization of the given model, one can evaluate whether the model fitting procedure chosen recovers the parameters originally used (or reproduces their influence). Researchers commonly generate visualizations that plot the parameter values estimated against a chosen range of “ground truth” parameter values. If these recovery plots indicate close correspondence between the two, the parameter recovery ability is deemed sufficient. Rigorous checking of parameter recovery dictates considering diverse sets of parameter values and examining any regions of the parameter space that seem particularly problematic. Among the tools available for this step are simulation-based calibration methods (Talts et al., 2018 ). By these and other means, parameter recovery checks support accounting appropriately for outliers and for issues such as any inherent temporal dependencies that the parameters should reflect.

Ablation Studies

An ablation study tests the performance effects of removing one of the model’s theoretical constructs from the model (e.g., Kalman-filter-based belief updates where that is relevant). By conducting ablation studies, we can discern the significance and role of each component, thereby shedding light on their relative importance and potential interactions within the system. For example, we might want to ensure that the effects of working memory manipulation in experiments are linked back to the parameter that controls working memory size rather than an extraneous hyperparameter. Hence, ablation studies can help with model checking and further model improvement even when we are observing good model fits to produce models that are actually instantiating the theories we think they are instantiating.

Outlier Analysis

Even if the model clearly passes the bar of the performance targets set, further tests are necessary, since no model is perfect. Human behavior almost always displays a long tail, as do models in some conditions. Accordingly, it is important to understand the causes of pattern of outliers. While it may be tempting to remove outliers, this is not good practice unless they can be definitively pinned to a specific confounding factor, such as participants misunderstanding the task.

We followed several model checking methods, for multifaceted evaluation of the touchscreen-typing model:

We used existing empirical studies (Salvucci, 2001 ; Sarcar et al., 2016 ; Anderson et al., 1998 ) to establish a plausible range of parameter values for vision, finger, and working memory ( \(E_K \in [0, 0.05], F_K \in [0, 0.18], \lambda \in [0, 0.3]\) ). By assigning the range of values to these parameters and placing a uniform prior distribution over this range, we were able to capture a variety of plausible typing behavior that include both the fastest and slowest typing speeds for one-finger and two-thumb typing scenarios. The sensibility of the specified priors was verified using a prior predictive check using 100 independent typing episodes generated by random parameters from the prior. The average and median performance exhibited a right-skewed distribution, similar to what is expected in human data.

After fitting parameters of our model to empirical data, we compared the model’s posterior predictions to data observed in our user study, i.e. a posterior predictive check, in each iterative step. To better judge its performance relative to human data, we employed words-per-minute values (shown in the prior predictive check figure above), inter-key interval, number of backspacing, error rate, number of gaze shifts, and gaze-on-keyboard time ratio as metrics (see Table  1 ). These simulated results can also be used to assess the model’s goodness of fit.

Conducting an ablation study helped us evaluate the effectiveness of the working memory design. Our comparison entailed testing model designs that exclude a particular feature from working memory, to reveal the resulting impact on performance.

Trajectory visualization gave us a better understanding of the model’s behavior by depicting both gaze and finger movements (as shown below, in panels a and b). The trajectories graphically illuminated patterns of gaze and finger movements during typing.

We analyzed the model’s behavior under extreme parameter settings (for example, see panel c) to identify any anomalous behavior.

In addition, we tested the model on unfamiliar keyboards (see panel d). The validation process assessed its ability to perform comparably on keyboards it had not seen before.

Model Selection

Once model checking has filtered in at least one plausible candidate model, model selection becomes relevant. Model selection or model comparison refers to the process of comparing our candidate model(s) against each other and to other plausible models (e.g., the baseline models identified in earlier steps) to find the ones that best satisfy our modeling goals, given our data. It is a common practice to conclude that a model should be taken seriously because it performs well on model selection methods compared to competing models.

A common and important modeling goal is generalizability, so that any inference from modeling extends beyond the specific data samples and settings that it is fit to more generally applicable settings. Almost all quantitative model selection methods prioritize this goal and operationalize measures to assess generalizability. Cross-validation (Bates et al., 2023 ) is a method that assesses generalizability by leaving out data from the parameter fitting process and assessing the predictive performance of the fitted parameters on the held-out data. Given a set of models, the one that predicts held-out data the best is assessed as being the one that is most generalizable. Such assessment is useful, as it is otherwise easy to “overfit” models to random irrelevant fluctuations in the training data, consequently hindering the model’s ability to predict behavior accurately in new settings. Deep RL agents for instance are especially prone to overfitting (Zhang et al., 2018 ). One key aspect in implementing cross-validation is the choice of holdout sets when training and evaluating models of human behavior, especially the choice of study unit, i.e. are we holding out data at the level of participants, specific tasks, or individual trials? Ideally, we want models that generalize well at every level, the level of the participants and their individual tasks, the level of participants across tasks, the level of experimental groups, and the level of the population, but this may also depend on the inference goals. Variants of cross-validation also directly assess generalizability on a specific target domain instead of holding out data from the training distribution (Busemeyer & Wang, 2000 ).

Information criteria-based methods on the other hand prioritize generalizability without holding out data. They produce measures for preferring those models that best balance goodness of fit to observed data and simplicity (also known as Occam’s razor)—to avoid both overly simplistic “underfit” models and overly complex “overfit” models and find the model that has the best chance of generalizing to unobserved conditions. The information-criterion scores are a combination of a score that captures goodness of fit with another score that acts as a penalty for model complexity; these relative overall scores are often used to compare models, and the model with the lowest score is considered to be the “best” among the considered models. For example, the Bayesian information criterion is calculated as \(BIC = -2 ln(\hat{L}) + k\times ln(n)\) , and Akaike information criterion, \(AIC = -2 ln(\hat{L}) + 2k \) , where \(\hat{L}\) is the likelihood estimate of the model, k is the number of parameters, and n is the sample size of the data; the first term is a measure of the goodness of fit, and the second is a penalty for model complexity measured as some function of the number of parameters. Being calculated differently, different model selection methods provide different results. There are several other information criteria-based methods, deviance information criterion (DIC), and Watanabe-Akaike information criterion (WAIC)—see Myung and Pitt ( 2018 ) each differing on how they calculate goodness of fit and penalize complexity.

Bayes factor (Kass & Raftery, 1995 ; Shiffrin et al., 2016 ; Schad et al., 2022 ) and minimum description length (Grünwald, 2007 ) are other methods that have been used in the literature. These scores can be more valid than information-criteria-based scores as they do not directly assume that the number of parameters in a model is reflective of complexity, but rather, also consider the structure of any prior distributions over parameters and the space of predictions the models make. However, these methods as well as cross-validation are computationally expensive, and information-criterion-based methods hence remain popular as a heuristic for estimating generalizability. As model selection methods use different heuristics to implement Occam’s razor, it is sometimes a practice to compare candidate models across several metrics and pick one that is favorable in most of the comparisons. This can be a good practice for transparency while selecting between models for standardized sets of tasks and contexts. However, we believe that ideally, the most appropriate statistical model selection method for the given setting should be emphasized after taking into consideration their advantages and shortcomings.

A context-dependent use of model selection methods is also needed because generalizability, while important is merely one potential goal of modeling. Other modeling goals can include aspects such as interpretability, causal consistency, estimation speed, and fairness which may or may not be related to generalizability (Bürkner et al., 2023 ; Dubova et al., 2024 ). It is also possible for these goals to trade off with each other. Thus, we believe that model selection is best treated as a multi-objective problem. Absent robust methods to evaluate models on a given set of goals with different utilities, a good practice would be to chart the relevant measurements yielded by models across the objectives, then analyze the tradeoffs that may exist, with our pre-defined goals for modeling in mind. A common adage in modeling is that “All models are wrong but some are useful” (Box, 1980 ); model selection in this perspective is about picking the model most useful for our given context.

The first step of model comparison in our case focused on contrasting the goodness of fit of the typing model against the baseline model (Jokinen et al., 2021a ) for an empirical human-based typing dataset (Jiang et al., 2020 ). Our model’s estimates of typing speed (in words per minute, WPM), inter-key interval (IKI, in milliseconds), number of backspaces in a given sentence, typing error rate (percent of typed characters that are errors), number of gaze shifts to keyboard, and gaze-on-keyboard time ratio values all lay within one standard deviation of the humans’ data, and gaze shifts falls in two. Upon comparing our model’s performance to the baseline model’s, we concluded that ours did better on simple goodness of fit assessments on these measures. It outperformed the baseline model decisively by the last of these metrics, which the baseline model overestimated relative to the human data (see Table  1 ).

In addition to the goal of explaining the empirical data used for training well, we also cared about how well our model could adapt to unseen keyboard layouts. We trained the model on 28 keyboards and tested the model on 10 separate keyboards to evaluate its adaptability to them. When we used the model parameters optimized during training to predict performance on these keyboard layouts, we observed that the model performed well. It outperformed competing models on relevant metrics and showed comparable performance to the held-out data on the metrics (see Table  2 for an example of evaluations; the complete table for all goals and metrics can be found in Shi et al. ( 2024 )).

The traditional POMDP framework provides an elegant way to describe interactive behavior. At its core are the decision-making entity (for example, the human participating in a behavior study), termed the agent, and the environment which constitutes everything external to the agent and which the agent interacts with (for example, the task in a behavioral study) (Sutton & Barto, 2018 ). While this distinction may seem straightforward and intuitive at first, often, the boundary between agent and environment is not identical to the physical separation between the two. The potentially tricky matter of appropriately delineating the agent relative to the environment in the context of the problem can be decisive for the agent’s eventual policy. Delineating between the notions of “internal” and “external” environments is similarly tricky. Different ways of distinguishing them can influence the model’s results considerably.

Consider the context of typing. Is the user’s finger a part of the external environment or, rather, the internal one? We could sensibly turn to perception for our boundary-setting conditions: the sensory experience of the user engaging with the screen via touch forms the limit of the internal environment, while the absolute location of the finger in space would be part of the external environment. Alternatively, we could situate the boundary in terms of the manner in which the agent receives rewards: is the reward signal directly determined by the external environment, constructed in the internal one, or conditioned on both? In our opinion, any strict rule for judging what falls within which environment is counterproductive. Instead, this design decision should consider the application context; domain expertise, attention to application scenarios, iterative development, solid testing, and the model selection procedures all play a role in helping determine the appropriate boundary. That said, many projects could benefit from the development of fine-grained principles that can inform the delineation between these environments in computational rationality settings but also specifying the agent–environment interface in RL operations.

Human Adaptation, in Its Many Forms

After policy optimization, a computationally rational model predicts behavior that is optimally adapted to the fixed environment in which it was trained. Modeling should also consider that humans adapt continuously, however—when faced with an unfamiliar keyboard layout, people can slowly adapt their behavior to the new keyboard. Yet learning and adaptation have gone unexplored in computational rationality modeling, even though the theory of computational rationality implies that behavior should adjust in a computationally rational way. One reason is the raft of methodology-related challenges that accompany introducing a new level of complexity such that the model is optimally prepared to adapt its behavior to changes in the environment. Building and training of the model grow harder as the line blurs between adaptation occurring through policy optimization (primarily adjustment to fixed elements of the environment) and adaptation taking place after deployment (primarily adjustment to changing elements of the environments). One option, of course, is to make certain components of the model more elaborate, so that it captures the relevant human learning and habituation processes. A more machine-learning-oriented solution could use continual learning frameworks with neural networks and RL (Hadsell et al., 2020 ; Khetarpal et al., 2022 ). These have already begun to show encouraging results for handling distribution shifts that follow from changes in an agent’s environment. Nascent research that bridges computational rationality models with continual learning is making interesting inroads into exploring, alongside policy adaptation, changes in agents’ representation of the world as their experience grows in conditions of resource constraints (Arumugam et al., 2024 ). As for purely ML-oriented solutions, overparameterized neural networks are known to improve flexibility under covariate shift (Tripuraneni et al., 2021 ), and they may permit more adaptation to environmental changes when incorporated into neural-network-based components specified within the internal environment.

Interactive Systems that Adapt to Human Users

The development of such applications as interactive AI systems faces a key challenge from the flipside of human adaptation: the system’s ability to adapt to the user. By approximating a person’s mental state and processes, computational rationality models can make a highly valuable contribution to adaptive interactive systems that strategically intervene to assist the user; for discussion, see the work of Mozannar et al. ( 2023 ). However, this is confounded by the recognized phenomenon of users following a mental model of the AI system. This model is geared for strategically steering the system towards user-desired behavior during interaction. While systems that comply with this steering may exhibit stronger interaction performance (Colella et al., 2020 ), a more advanced system could aim to identify and learn from users’ mental models of AI, their refinement over the course of the interaction, and the influence of mutable user goals on interaction behavior. Developing such mental models of AI systems is currently a research challenge, even more so in the context of learning these online during interaction (Howes et al., 2023 ; Steyvers & Kumar, 2022 ; Bansal et al., 2019 ). Co-operative multi-agent setups (Çelikok et al., 2019 ) with the user and the AI system as interacting agents are a promising approach to improve interactive behavior by better anticipating the user and their strategies—doing so with computationally rational user models would be an interesting avenue for further research (Howes et al., 2023 ); these are bound to be confronted by computational challenges in a real-time and interactive setting. Computational efficiency and approximation to computationally rational behavior can however be achieved by employing surrogate computational rationality models using methods such as amortized inference (Moon et al., 2023 ) or likelihood-free inference (Aushev et al., 2023 ; Palestro et al., 2018 ; Hartig et al., 2011 ).

Balancing Model Complexity and Performance

Researchers need to balance the complexity of the internal environment with the accuracy of the simulation. Enhancing the model’s internal environment, when done right, can significantly improve the human-like behavior of the model, especially by simultaneously accounting for the many mental processes at play in learning, attention, memory, choice behavior, etc. However, the model then gets more complex, and building it correctly becomes more challenging. For example, in the case of the touchscreen-typing model, the current model has a simplified design that does not include reading behavior (Just & Carpenter, 1980 ) in its vision system. Neither does the module for working memory account for long-term memory (Norris, 2017 ), chunking (Yamaguchi & Logan, 2014 ), nor the impact of phrase sets. Incorporating these factors could afford valuable insight into intricate patterns of human behavior; however, tuning the hyperparameters of this type of model correspondingly can be immensely challenging. While one can use solid priors to simplify such procedures as setting parameters for the internal environment’s modules, building the model still may end up overly complicated. Hence, researchers may need to find the balance between model complexity and performance that are appropriate for their modeling goals and consequently often strive for the most realistic and satisfactory simulations without making the internal environment unduly complex.

Optimization Approaches Other than RL

While we have concentrated here on RL-based mechanisms for generating boundedly optimal behavior, these are not the only conceivable means to that end. In fact, any approach that achieves optimal results is valid, including methods for black box combinatorial optimization (e.g., Sarcar et al., 2018 ). Naturally, the choice of methods affects which aspects of the modeling workflow matter most in the case at hand. For instance, the reward specification is crucial in RL but irrelevant in active inference. Our focus on the RL setting stemmed from a desire to contribute concretely to the state of the art: off-the-shelf solutions for implementing an optimal policy are widely available.

The Relationship Between Data and Modeling

While the model development aspects of realistically predicting human behavior form the heart of our workflow, the aim, in the end, is to estimate observed data, and these are gathered in experiments. Any model, however sophisticated, is ultimately unproductive if the experiments and the measurements made are invalid, unreliable, or highly noisy. For successful inferences, the iterative modeling workflow must go hand in hand with a good workflow for experimentation. The two overlap somewhat in scope, as many experiments get informed by prior theory and model-based predictions. Therefore, they should together constitute a fundamentally iterative data-collection and model building system that enriches science.

Data Availability

No datasets were generated or analyzed during the current study.

Also known as resource rationality and algorithmic rationality.

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Acknowledgements

We thank Aditya Acharya, Patrick Ebel, Christoph Gebhardt, Michael Hedderich, and Bai Yunpeng for their comments on an initial draft.

Open Access funding provided by Aalto University. This work was supported by the Research Council of Finland (flagship program: Finnish Center for Artificial Intelligence, FCAI, grants 328400, 345604, 341763; Human Automata, grant 328813; Subjective Functions, grant 357578). Machines that Understand People, grant 330347. S.C was also supported by the Jorma Ollila Grant from Nokia Foundation.

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Chandramouli, S., Shi, D., Putkonen, A. et al. A Workflow for Building Computationally Rational Models of Human Behavior. Comput Brain Behav (2024). https://doi.org/10.1007/s42113-024-00208-6

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12 Psychology Experiments That Helped Researchers Understand Human Behavior

Take a look at a few psychology experiments that studied human behavior and greatly influenced the psychology field.

Psychology comprises the study of the unconscious and conscious phenomena. It includes sub-fields of study like human development, clinical, social behavior, and more. 

It helps us in understanding ourselves and human behavior. According to American Psychological Association, it is the scientific study of mind and behavior and aims to understand individuals and groups by establishing principles and researching specific cases. 

Psychology experiments are performed to understand people’s behavior based on cognition, emotion, intelligence, and motivation. While psychology is related to treating mental health problems, it also solves problems in different spheres of human activity. 

Psychology is not a new science; it traces back to 400-500 years BC. It was a popular topic in Europe. In Germany, Gottfried Wilhelm Leibniz applied his principles of calculus and argued that mental activity took place on a continuum. Different religions shared different aspects of psychology. 

While some psychology experiments have improved people’s lives, others created controversy. For instance, according to studies, men and women have different brains and explain objects differently. Here are other differences that you should note to understand your spouse better . 

Let us know about famous psychology experiments conducted that changed human lives. 

1. Selective Attention Test (Invisible Gorilla Test)

In the Invisible Gorilla experiment conducted by Chabris in 1999, a video is shown to the participants. In the video, there were asked how many passes occurred between basketball players in the white team. 

Keeping track of the passes is a difficult task. The participants failed to notice a man in a gorilla suit walking onto the court and standing in the center of the running screen. The study revealed that the majority of the participants did not notice the gorilla at all, proving that they are not multitaskers. 

They strongly focused on one element and missed other details. The experiment reveals that people missed a lot of things going around them and have no idea what they are missing. 

2. Surrogate Mother Experiment

In this psychology experiment, Harry Harlow separated infant rhesus monkeys from mothers after birth and left them to be raised by surrogate mothers. One surrogate mother was made with soft terry cloth but lacked food; another was made with wire with an attached bottle for food. 

The research revealed that the baby monkey was more attached to the cloth mother than the wire mother. The experiment proved that affection plays a crucial role in a child’s development. 

Furthermore, the monkeys that spent more time cuddling mothers grew up healthier. Physical body contact is more important in the parent-child bond and comparison to basic needs.

3. The Marshmallow Test

In Walter Mischel’s Marshmallow Experiment, children aged 4-6 were taken into a room, where marshmallow was kept on the table. Before leaving the children in the room, he informed the children they would get another marshmallow, only if the first one was kept on the table after he returned in 15 minutes. The experiment was performed to check for how long each child resisted eating the marshmallow and noted if it is correlated with the child’s success in adulthood. 

After the experiment, it was revealed that over 600 children ate marshmallows and only one-third of them waited to receive the second marshmallow. Some children covered their eyes and turned around so that they couldn't see the marshmallow. 

Later, it was found that those who weighted for long were more competent and received better scores than others. This psychology experiment also found that this characteristic remains with a person for life. 

4. The Monster Study

This psychology experiment received a negative title due to unethical methods used to determine the effects on children. Wendell Johnson chose twenty-two-orphan children. They were divided into two groups: one with stutters and one without stutters. 

Those who were with stutter were placed in positive speech therapy and were praised for their fluency. The non-stutterers were placed in negative speech therapy. The experiment found that children who received negative speech therapy retained speech problems for their entire life and became examples of the significance of positive reinforcement in education. Here are other dangerous human experiments in history . 

5. Magical Number Seven

Referred to as Miller’s law, the Magical Number Seven experiments about the number of objects a person can hold in working memory. The human memory includes strings of words and concepts ranging from 5-9. The magical number seven was published in 1956 by psychologist George A. Miller. In the article, he discussed a concurrence between the limits of one-dimensional judgment and limits of short-term memory. 

In a one-dimensional judgment task, a candidate is presented with several stimuli that vary from one dimension and respond to stimuli with a corresponding response. 

The performance was perfect up to six different stimuli but declined as the number of stimuli increased. This explains that a human’s performance on a one-dimensional judgment can be described as an information store with a capacity of 2-3 bits of information. 

6. Little Albert Experiment

The study conducted in 1920 by John Watson and Rosalie Rayner, the Little Albert Experiment, is considered to be unethical experiments of all time. It was a controlled experiment and showed empirical evidence of classical conditioning in humans. Its results were first published in the February 1920 issue of the Journal of Experimental Psychology. 

The experiment began by keeping a white rat in front of the infant who had no fear of animals. He then produced a sound by striking a steel bar with the hammer every time Albert was (a kid) presented with the rat. After some time, the boy began to cry and showed signs of fear every time he saw the rat. He then created similar situations with different animals until he feared with all. This psychology experiment proved that adult fears are related to childhood experiences. 

7. Car Crash Experiment

The study conducted by Elizabeth Loftus and John Palmer reveals how deceiving memories can be. In 1974, a car crash experiment was performed to evaluate whether wrong wordings could influence a participant’s recall. The participants were shown a video of a car accident and were asked what happened as they witnessed it. 

The researchers asked them the same question with different wordings and found that the two groups give different answers. They were asked, “how fast was the car driving at the time of impact?” and “how fast was the car going when it smashed into the other car?”

This psychology experiment revealed that memory could be manipulated through questioning techniques.

8. The Asch Conformity Experiment

In 1950, psychologist Solomon Asch performed a series of experiments to demonstrate the powers of conformity in groups. The study found that people are susceptible to going along with a group, even after knowing the group is wrong. The students were told to take a vision test and identify which of the three lines was similar to that of the target line. 

When students were asked alone, they gave accurate answers. In other trials, the participants (different from the experiment) selected the wrong lines. The experiment proved how conformity could be powerful and subtle to human behavior. 

9. Fantz’s Looking Chamber

The experiment by Robert L. Fantz is important in the field of infant development and vision. During that time, when the experiment was conducted, only a few ways were introduced to know what’s happening in an infant’s mind. He realized that the best way to know this is to watch the actions and reactions of infants. 

To test this concept, he set a display board with two pictures. One picture was of a bull's eye, and another was the sketch of a human face. Then from behind the board, he looked at infants' reactions and what he looked at. The study revealed that the baby looked twice at the human face in comparison to the bull’s eye. 

The experiment revealed that human babies have powers of pattern and form selection. He also found that infants look longer at patterned stimuli like concentric circles than at red, white, and yellow discs. Later, several studies were performed, and it was concluded that infants entered the world with an idea of the human face.

10. Learned Helplessness Experiment

The study conducted by Seligman involved the ringing of a bell and the administration of a light shock to a dog. After several pairings, a dog reacted to the shock even before it happened. During the study, the dogs were placed in a large crate that was divided with a low fence, and the dog could see and jump over the fence. 

The floor on one side of the fence was electrified. He placed each dog on the electrifying side and administered a light shock. The researcher expected the dog to jump on the non-shocking side, but it laid down. This psychology experiment proved that dogs learned from the experiment; there’s nothing they could do to avoid the shock & give up easily. 

To prove this hypothesis, a new set of dogs experimented with no history of an experiment, and they jumped over the fence. The condition was described as learned helplessness, where an animal or human does not try for the next attempt (in a negative situation) if the past has taught them they can’t do anything. 

11. A Class Divided

A study conducted in 1968 by Jane Elliott was inspired by the assassination of Dr. Martin Luther King and his life. Jane divided a class into two groups. One with blue-eyed students and another with brown-eyes. She labeled blue-eyed student groups as the superior group and gave them extra privileges, leaving behind the brown-eyed students as the minority group. 

She didn’t allow them to interact and ask the superior one to point out the negative characteristics of the minority one. The exercise showed that the children's behavior changed. The brown-eyed ones showed the worst performance and showed lower self-confidence, whereas the blue-eyed ones performed excellently. Later, she reversed the groups and did the same. Later, the students were taught to embrace everyone and not to judge based on outward appearances. 

12. Milgram’s Obedience Experiments

The study was conducted to measure people’s willingness when they are instructed to perform acts that are against morals. The study was based on the fact that humans will take direction from their superiors. 

The participants were told to deliver electrical shocks to the learner whenever a wrong answer was given. The learner was a confederate who pretended to be shocked. 

He found that nearly 65% of participants were eager to deliver the shock, even after knowing the learner is unconscious. 

Final Words 

Several psychology experiments conducted helped the researchers in knowing human’s behavior. Do you know about other psychology experiments that turned controversial or lasted a major impact on history? Do share them with us! 

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What are some research human behavior examples?

Table of Contents

Examples of human behavior include conflict, communication, cooperation, creativity, play, social interaction, tradition, and work. This article throws light on the five important models of individual behavior, i.e, (1) Rational Economic Man, (2) Social Man, (3) Organizational Man, (4) The Self Actuating Man, and (5) Complex Man. They found that the three key drivers of behaviour change are motivation and capability, which are internal conditions, and opportunity, which is an external condition. These are all interlinked and can influence each other. Strongly rooted in psychology and sociology, studies of human behavior give us an academic understanding of motivations, productivity, and how teams work. In turn, these insights can help make workplaces or any group setting more productive. Our culture shapes the way we work and play, and it makes a difference in how we view ourselves and others. It affects our values—what we consider right and wrong. This is how the society we live in influences our choices. But our choices can also influence others and ultimately help shape our society.

What is human behavior and examples?

Types of human behavior include moral versus molecular, overt versus covert, voluntary versus involuntary, and conscious versus unconscious. Examples of human behavior include conflict, communication, cooperation, creativity, play, social interaction, tradition, and work. A study on human behavior has revealed that 90% of the population can be classified into four basic personality types: Optimistic, Pessimistic, Trusting and Envious. However, the latter of the four types, Envious, is the most common, with 30% compared to 20% for each of the other groups. A study on human behavior has revealed that 90% of the population can be classified into four basic personality types: Optimistic, Pessimistic, Trusting and Envious. However, the latter of the four types, Envious, is the most common, with 30% compared to 20% for each of the other groups. Behavioral psychology, or behaviorism, is a theory suggesting that environment shapes human behavior. In a most basic sense, behavioral psychology is the study and analysis of observable behavior. This field of psychology influenced thought heavily throughout the middle of the 20th century. 6) Human Behaviour Flows From three Sources: Desire, Emotion, And Knowledge.

What type of research is behavior?

Behavioral research is a way to examine and understand individual and social behavior through measurement and interpretation. This course investigates the theoretical principles that underlie behavioral research and the application of current research techniques. Psychology is the scientific study of the mind and behavior. Psychologists are actively involved in studying and understanding mental processes, brain functions, and behavior. Behavior modeling typically involved five steps: modeling, retention, rehearsal, feed back, and transfer of training. Qualitative research is a method of inquiry often used in the social sciences. Qualitative researchers aim to gather an in-depth understanding of human behavior and the reasons that govern such behavior. The qualitative method investigates the why and how of decision making, not just what, where, when.

What are the topic about human behavior?

Human behavior is studied by the social sciences, which include psychology, sociology, ethology, and their various branches and schools of thought. The study of human behavior includes how the human mind evolved and how the nervous system controls behavior. Human behavior can be studied scientifically, but the methods for doing so differ depending on whether you are investigating the behaviors or the hows and whys behind them. Psychology is the scientific study of the mind and behavior. Psychologists are actively involved in studying and understanding mental processes, brain functions, and behavior. The brain receives information and internal and external influences that enable the most appropriate behaviors to be triggered at any time. In addition, our behavior has environmental consequences, which can be experienced as positive or negative for us. Beyond gaining a better appreciation of our own psychology, gaining a better understanding of human behaviour can help us understand the various internal and external factors that shape our experiences. It can also help us understand others better and improve how we engage and interact.

What are some human behavior theories?

The most-often used theories of health behavior are Social Cognitive Theory, The Transtheoretical Model/Stages of Change, the Health Belief Model, and the Theory of Planned Behavior. The most-often mentioned theoretical model that has not been fully applied in research and practice is the Social Ecological Model. The most-often used theories of health behavior are Social Cognitive Theory, The Transtheoretical Model/Stages of Change, the Health Belief Model, and the Theory of Planned Behavior. The most-often mentioned theoretical model that has not been fully applied in research and practice is the Social Ecological Model. The most-often used theories of health behavior are Social Cognitive Theory, The Transtheoretical Model/Stages of Change, the Health Belief Model, and the Theory of Planned Behavior. Four models that present a logical and reasonable approach to behavioral change include the Health Belief Model, the Theory of Self Efficacy, the Theory of Reasoned Action, and the Multiattribute Utility Model. Behavior modeling in the workplace, a component of social learning theory, is the act of showing employees how to do something and guiding them through the process of imitating the modeled behavior. Behavioral and social sciences research helps predict, prevent, and manage illness — in individuals and in whole populations. This research also helps people change their behaviors, understand treatments, and learn how to stick with them.

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