different types of research on psychology and learning

Psychological Research Methods: Types and Tips

Categories Research Methods

Psychological research methods are the techniques used by scientists and researchers to study human behavior and mental processes. These methods are used to gather empirical evidence.

The goal of psychological research methods is to obtain objective and verifiable data collected through scientific experimentation and observation. 

The research methods that are used in psychology are crucial for understanding how and why people behave the way they do, as well as for developing and testing theories about human behavior.

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Reasons to Learn More About Psychological Research Methods

One of the key goals of psychological research is to make sure that the data collected is reliable and valid.

• Reliability means that the data is consistent and can be replicated
• Validity refers to the accuracy of the data collected

Researchers must take great care to ensure that their research methods are reliable and valid, as this is essential for drawing accurate conclusions and making valid claims about human behavior.

High school and college students who are interested in psychology can benefit greatly from learning about research methods. Understanding how psychologists study human behavior and mental processes can help students develop critical thinking skills and a deeper appreciation for the complexity of human behavior.

Having an understanding of these research methods can prepare students for future coursework in psychology, as well as for potential careers in the field.

Quantitative vs. Qualitative Psychological Research Methods

Psychological research methods can be broadly divided into two main types: quantitative and qualitative. These two methods differ in their approach to data collection and analysis.

Quantitative Research Methods

Quantitative research methods involve collecting numerical data through controlled experiments, surveys, and other objective measures.

The goal of quantitative research is to identify patterns and relationships in the data that can be analyzed statistically.

Researchers use statistical methods to test hypotheses, identify significant differences between groups, and make predictions about future behavior.

Qualitative Research Methods

Qualitative research methods, on the other hand, involve collecting non-numerical data through open-ended interviews, observations, and other subjective measures.

Qualitative research aims to understand the subjective experiences and perspectives of individuals and groups.

Researchers use methods such as content analysis and thematic analysis to identify themes and patterns in the data and to develop rich descriptions of the phenomenon under study.

How Quantitative and Qualitative Methods Are Used

While quantitative and qualitative research methods differ in their approach to data collection and analysis, they are often used together to gain a more complete understanding of complex phenomena.

For example, a researcher studying the impact of social media on mental health might use a quantitative survey to gather numerical data on social media use and a qualitative interview to gain insight into participants’ subjective experiences with social media.

Types of Psychological Research Methods

There are several types of research methods used in psychology, including experiments, surveys, case studies, and observational studies. Each method has its strengths and weaknesses, and researchers must choose the most appropriate method based on their research question and the data they hope to collect.

Case Studies

A case study is a research method used in psychology to investigate an individual, group, or event in great detail. In a case study, the researcher gathers information from a variety of sources, including:

• Observation
• Document analysis

These methods allow researchers to gain an in-depth understanding of the case being studied.

Case studies are particularly useful when the phenomenon under investigation is rare or complex, and when it is difficult to replicate in a laboratory setting.

Surveys are a commonly used research method in psychology that involve gathering data from a large number of people about their thoughts, feelings, behaviors, and attitudes.

Surveys can be conducted in a variety of ways, including:

• In-person interviews
• Online questionnaires
• Paper-and-pencil surveys

Surveys are particularly useful when researchers want to study attitudes or behaviors that are difficult to observe directly or when they want to generalize their findings to a larger population.

Experimental Psychological Research Methods

Experimental studies are a research method commonly used in psychology to investigate cause-and-effect relationships between variables. In an experimental study, the researcher manipulates one or more variables to see how they affect another variable, while controlling for other factors that may influence the outcome.

Experimental studies are considered the gold standard for establishing cause-and-effect relationships, as they allow researchers to control for potential confounding variables and to manipulate variables in a systematic way.

Correlational Psychological Research Methods

Correlational research is a research method used in psychology to investigate the relationship between two or more variables without manipulating them. The goal of correlational research is to determine the extent to which changes in one variable are associated with changes in another variable.

In other words, correlational research aims to establish the direction and strength of the relationship between two or more variables.

Naturalistic Observation

Naturalistic observation is a research method used in psychology to study behavior in natural settings, without any interference or manipulation from the researcher.

The goal of naturalistic observation is to gain insight into how people or animals behave in their natural environment without the influence of laboratory conditions.

Meta-Analysis

A meta-analysis is a research method commonly used in psychology to combine and analyze the results of multiple studies on a particular topic.

The goal of a meta-analysis is to provide a comprehensive and quantitative summary of the existing research on a topic, in order to identify patterns and relationships that may not be apparent in individual studies.

Tips for Using Psychological Research Methods

Here are some tips for high school and college students who are interested in using psychological research methods:

Understand the different types of research methods: 

Before conducting any research, it is important to understand the different types of research methods that are available, such as surveys, case studies, experiments, and naturalistic observation.

Each method has its strengths and limitations, and selecting the appropriate method depends on the research question and variables being investigated.

Develop a clear research question: 

A good research question is essential for guiding the research process. It should be specific, clear, and relevant to the field of psychology. It is also important to consider ethical considerations when developing a research question.

Use proper sampling techniques: 

Sampling is the process of selecting participants for a study. It is important to use proper sampling techniques to ensure that the sample is representative of the population being studied.

Random sampling is considered the gold standard for sampling, but other techniques, such as convenience sampling, may also be used depending on the research question.

Use reliable and valid measures:

It is important to use reliable and valid measures to ensure the data collected is accurate and meaningful. This may involve using established measures or developing new measures and testing their reliability and validity.

Consider ethical issues:

It is important to consider ethical considerations when conducting psychological research, such as obtaining informed consent from participants, maintaining confidentiality, and minimizing any potential harm to participants.

In many cases, you will need to submit your study proposal to your school’s institutional review board for approval.

Analyze and interpret the data appropriately : 

After collecting the data, it is important to analyze and interpret the data appropriately. This may involve using statistical techniques to identify patterns and relationships between variables, and using appropriate software tools for analysis.

Communicate findings clearly: 

Finally, it is important to communicate the findings clearly in a way that is understandable to others. This may involve writing a research report, giving a presentation, or publishing a paper in a scholarly journal.

Clear communication is essential for advancing the field of psychology and informing future research.

Frequently Asked Questions

What are the 5 methods of psychological research.

The five main methods of psychological research are:

• Experimental research : This method involves manipulating one or more independent variables to observe their effect on one or more dependent variables while controlling for other variables. The goal is to establish cause-and-effect relationships between variables.
• Correlational research : This method involves examining the relationship between two or more variables, without manipulating them. The goal is to determine whether there is a relationship between the variables and the strength and direction of that relationship.
• Survey research : This method involves gathering information from a sample of participants using questionnaires or interviews. The goal is to collect data on attitudes, opinions, behaviors, or other variables of interest.
• Case study research : This method involves an in-depth analysis of a single individual, group, or event. The goal is to gain insight into specific behaviors, attitudes, or phenomena.
• Naturalistic observation research : This method involves observing and recording behavior in natural settings without any manipulation or interference from the researcher. The goal is to gain insight into how people or animals behave in their natural environment.

What is the most commonly used psychological research method?

The most common research method used in psychology varies depending on the research question and the variables being investigated. However, correlational research is one of the most frequently used methods in psychology.

This is likely because correlational research is useful in studying a wide range of psychological phenomena, and it can be used to examine the relationships between variables that cannot be manipulated or controlled, such as age, gender, and personality traits. 

Experimental research is also a widely used method in psychology, particularly in the areas of cognitive psychology , social psychology , and developmental psychology .

Other methods, such as survey research, case study research, and naturalistic observation, are also commonly used in psychology research, depending on the research question and the variables being studied.

How do you know which research method to use?

Deciding which type of research method to use depends on the research question, the variables being studied, and the practical considerations involved. Here are some general guidelines to help students decide which research method to use:

• Identify the research question : The first step is to clearly define the research question. What are you trying to study? What is the hypothesis you want to test? Answering these questions will help you determine which research method is best suited for your study.
• Choose your variables : Identify the independent and dependent variables involved in your research question. This will help you determine whether an experimental or correlational research method is most appropriate.
• Consider your resources : Think about the time, resources, and ethical considerations involved in conducting the research. For example, if you are working on a tight budget, a survey or correlational research method may be more feasible than an experimental study.
• Review existing literature : Conducting a literature review of previous studies on the topic can help you identify the most appropriate research method. This can also help you identify gaps in the literature that your study can fill.
• Consult with a mentor or advisor : If you are still unsure which research method to use, consult with a mentor or advisor who has experience in conducting research in your area of interest. They can provide guidance and help you make an informed decision.

Scholtz SE, de Klerk W, de Beer LT. The use of research methods in psychological research: A systematised review . Front Res Metr Anal . 2020;5:1. doi:10.3389/frma.2020.00001

Palinkas LA. Qualitative and mixed methods in mental health services and implementation research . J Clin Child Adolesc Psychol . 2014;43(6):851-861. doi:10.1080/15374416.2014.910791

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011;11(1):100. doi:10.1186/1471-2288-11-100

APA Handbook of Research Methods in Psychology

Available formats.

• Table of contents
• Contributor bios
• Book details

With significant new and updated content across dozens of chapters, this second edition  presents the most exhaustive treatment available of the techniques psychologists and others have developed to help them pursue a shared understanding of why humans think, feel, and behave the way they do.

The initial chapters in this indispensable three-volume handbook address broad, crosscutting issues faced by researchers: the philosophical, ethical, and societal underpinnings of psychological research. Next, chapters detail the research planning process, describe the range of measurement techniques that psychologists most often use to collect data, consider how to determine the best measurement techniques for a particular purpose, and examine ways to assess the trustworthiness of measures.

Additional chapters cover various aspects of quantitative, qualitative, neuropsychological, and biological research designs, presenting an array of options and their nuanced distinctions. Chapters on techniques for data analysis follow, and important issues in writing up research to share with the community of psychologists are discussed in the handbook’s concluding chapters.

Among the newly written chapters in the second edition, the handbook’s stellar roster of authors cover literature searching, workflow and reproducibility, research funding, neuroimaging, various facets of a wide range of research designs and data analysis methods, and updated information on the publication process, including research data management and sharing, questionable practices in statistical analysis, and ethical issues in manuscript preparation and authorship.

Volume 1. Foundations, Planning, Measures, and Psychometrics

Editorial Board

About the Editors

Contributors

A Note from the Publisher

Introduction: Objectives of Psychological Research and Their Relations to Research Methods

Part I. Philosophical, Ethical, and Societal Underpinnings of Psychological Research

• Chapter 1. Perspectives on the Epistemological Bases for Qualitative Research Carla Willig
• Chapter 2. Frameworks for Causal Inference in Psychological Science Peter M. Steiner, William R. Shadish, and Kristynn J. Sullivan
• Chapter 3. Ethics in Psychological Research: Guidelines and Regulations Adam L. Fried and Kate L. Jansen
• Chapter 4. Ethics and Regulation of Research With Nonhuman Animals Sangeeta Panicker, Chana K. Akins, and Beth Ann Rice
• Chapter 5. Cross-Cultural Research Methods David Masumoto and Fons J. R. van de Vijver
• Chapter 6.Research With Populations that Experience Marginalization George P. Knight, Rebecca M. B. White, Stefanie Martinez-Fuentes, Mark W. Roosa, and Adriana J. Umaña-Taylor

Part II. Planning Research

• Chapter 7. Developing Testable and Important Research Questions Frederick T. L. Leong, Neal Schmitt, and Brent J. Lyons
• Chapter 8. Searching With a Purpose: How to Use Literature Searching to Support Your Research Diana Ramirez and Margaret J. Foster
• Chapter 9. Psychological Measurement: Scaling and Analysis Heather Hayes and Susan E. Embretson
• Chapter 10. Sample Size Planning Ken Kelley, Samantha F. Anderson, and Scott E. Maxwell
• Chapter 11. Workflow and Reproducibility Oliver Kirchkamp
• Chapter 12. Obtaining and Evaluating Research Funding Jonathan S. Comer and Amanda L. Sanchez

Part III. Measurement Methods

• Chapter 13. Behavioral Observation Roger Bakeman and Vicenç Quera
• Chapter 14. Question Order Effects Lisa Lee, Parvati Krishnamurty, and Struther Van Horn
• Chapter 15. Interviews and Interviewing Techniques Anna Madill
• Chapter 16. Using Intensive Longitudinal Methods in Psychological Research Masumi Iida, Patrick E. Shrout, Jean-Philippe Laurenceau, and Niall Bolger
• Chapter 17. Automated Analyses of Natural Language in Psychological Research Laura K. Allen, Arthur C. Graesser, and Danielle S. McNamara
• Chapter 18. Objective Tests as Instruments of Psychological Theory and Research David Watson
• Chapter 19. Norm- and Criterion-Referenced Testing Kurt F. Geisinger
• Chapter 20. The Current Status of "Projective" "Tests" Robert E. McGrath, Alec Twibell, and Elizabeth J. Carroll
• Chapter 21. Brief Instruments and Short Forms Emily A. Atkinson, Carolyn M. Pearson Carter, Jessica L. Combs Rohr, and Gregory T. Smith
• Chapter 22. Eye Movements, Pupillometry, and Cognitive Processes Simon P. Liversedge, Sara V. Milledge, and Hazel I. Blythe
• Chapter 23. Response Times Roger Ratcliff
• Chapter 24. Psychophysics: Concepts, Methods, and Frontiers Allie C. Hexley, Takuma Morimoto, and Manuel Spitschan
• Chapter 25. The Perimetric Physiological Measurement of Psychological Constructs Louis G. Tassinary, Ursula Hess, Luis M. Carcoba, and Joseph M. Orr
• Chapter 26. Salivary Hormone Assays Linda Becker, Nicholas Rohleder, and Oliver C. Schultheiss
• Chapter 27. Electro- and Magnetoencephalographic Methods in Psychology Eddie Harmon-Jones, David M. Amodio, Philip A. Gable, and Suzanne Dikker
• Chapter 28. Event-Related Potentials Steven J. Luck
• Chapter 29. Functional Neuroimaging Megan T. deBettencourt, Wilma A. Bainbridge, Monica D. Rosenberg
• Chapter 30. Noninvasive Stimulation of the Cerebral Cortex Dennis J. L. G. Schutter
• Chapter 31. Combined Neuroimaging Methods Marius Moisa and Christian C. Ruff
• Chapter 32. Neuroimaging Analysis Methods Yanyu Xiong and Sharlene D. Newman

Part IV. Psychometrics

• Chapter 33. Reliability Sean P. Lane, Elizabeth N. Aslinger, and Patrick E. Shrout
• Chapter 34. Generalizability Theory Xiaohong Gao and Deborah J. Harris
• Chapter 35. Construct Validity Kevin J. Grimm and Keith F. Widaman
• Chapter 36. Item-Level Factor Nisha C. Gottfredson, Brian D. Stucky, and A. T. Panter
• Chapter 37. Item Response Theory Steven P. Reise and Tyler M. Moore
• Chapter 38. Measuring Test Performance With Signal Detection Theory Techniques Teresa A. Treat and Richard J. Viken

Volume 2. Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological

Part I. Qualitative Research Methods

• Chapter 1. Developments in Qualitative Inquiry Sarah Riley and Andrea LaMarre
• Chapter 2. Metasynthesis of Qualitative Research Sally Thorne
• Chapter 3. Grounded Theory and Psychological Research Robert Thornberg, Elaine Keane, and Malgorzata Wójcik
• Chapter 4. Thematic Analysis Virginia Braun and Victoria Clarke
• Chapter 5. Phenomenological Methodology, Methods, and Procedures for Research in Psychology Frederick J. Wertz
• Chapter 6. Narrative Analysis Javier Monforte and Brett Smith
• Chapter 7. Ethnomethodology and Conversation Analysis Paul ten Have
• Chapter 8. Discourse Analysis and Discursive Psychology Chris McVittie and Andy McKinlay
• Chapter 9. Ethnography in Psychological Research Elizabeth Fein and Jonathan Yahalom
• Chapter 10. Visual Research in Psychology Paula Reavey, Jon Prosser, and Steven D. Brown
• Chapter 11. Researching the Temporal Karen Henwood and Fiona Shirani

Part II. Working Across Epistemologies, Methodologies, and Methods

• Chapter 12. Mixed Methods Research in Psychology Timothy C. Guetterman and Analay Perez
• Chapter 13. The "Cases Within Trials" (CWT) Method: An Example of a Mixed-Methods Research Design Daniel B. Fishman
• Chapter 14. Researching With American Indian and Alaska Native Communities: Pursuing Partnerships for Psychological Inquiry in Service to Indigenous Futurity Joseph P. Gone
• Chapter 15. Participatory Action Research as Movement Toward Radical Relationality, Epistemic Justice, and Transformative Intervention: A Multivocal Reflection Urmitapa Dutta, Jesica Siham Fernández, Anne Galletta, and Regina Day Langhout

Part III. Sampling Across People and Time

• Chapter 16. Introduction to Survey Sampling Roger Tourangeau and Ting Yan
• Chapter 17. Epidemiology Rumi Kato Price and Heidi H. Tastet
• Chapter 18. Collecting Longitudinal Data: Present Issues and Future Challenges Simran K. Johal, Rohit Batra, and Emilio Ferrer
• Chapter 19. Using the Internet to Collect Data Ulf-Dietrich Reips

Part IV. Building and Testing Models

• Chapter 20. Statistical Mediation Analysis David P. MacKinnon, Jeewon Cheong, Angela G. Pirlott, and Heather L. Smyth
• Chapter 21. Structural Equation Modeling with Latent Variables Rick H. Hoyle and Nisha C. Gottfredson
• Chapter 22. Mathematical Psychology Parker Smith, Yanjun Liu, James T. Townsend, and Trisha Van Zandt
• Chapter 23. Computational Modeling Adele Diederich
• Chapter 24. Fundamentals of Bootstrapping and Monte Carlo Methods William Howard Beasley, Patrick O'Keefe, and Joseph Lee Rodgers
• Chapter 25. Designing Simulation Studies Xitao Fan
• Chapter 26. Bayesian Modeling for Psychologists: An Applied Approach Fred M. Feinberg and Richard Gonzalez

Part V. Designs Involving Experimental Manipulations

• Chapter 27. Randomized Designs in Psychological Research Larry Christensen, Lisa A. Turner, and R. Burke Johnson
• Chapter 28. Nonequivalent Comparison Group Designs Henry May and Zachary K. Collier
• Chapter 29. Regression Discontinuity Designs Charles S. Reichardt and Gary T. Henry
• Chapter 30. Treatment Validity for Intervention Studies Dianne L. Chambless and Steven D. Hollon
• Chapter 31. Translational Research Michael T. Bardo, Christopher Cappelli, and Mary Ann Pentz
• Chapter 32. Program Evaluation: Outcomes and Costs of Putting Psychology to Work Brian T. Yates

Part VI. Quantitative Research Designs Involving Single Participants or Units

• Chapter 33. Single-Case Experimental Design John M. Ferron, Megan Kirby, and Lodi Lipien
• Chapter 34. Time Series Designs Bradley J. Bartos, Richard McCleary, and David McDowall

Part VII. Designs in Neuropsychology and Biological Psychology

• Chapter 35. Case Studies in Neuropsychology Randi C. Martin, Simon Fischer-Baum, and Corinne M. Pettigrew
• Chapter 36. Group Studies in Experimental Neuropsychology Avinash R Vaidya, Maia Pujara, and Lesley K. Fellows
• Chapter 37. Genetic Methods in Psychology Terrell A. Hicks, Daniel Bustamante, Karestan C. Koenen, Nicole R. Nugent, and Ananda B. Amstadter
• Chapter 38. Human Genetic Epidemiology Floris Huider, Lannie Ligthart, Yuri Milaneschi, Brenda W. J. H. Penninx, and Dorret I. Boomsma

Volume 3. Data Analysis and Research Publication

Part I. Quantitative Data Analysis

• Chapter 1. Methods for Dealing With Bad Data and Inadequate Models: Distributions, Linear Models, and Beyond Rand R. Wilcox and Guillaume A. Rousselet
• Chapter 2. Maximum Likelihood and Multiple Imputation Missing Data Handling: How They Work, and How to Make Them Work in Practice Timothy Hayes and Craig K. Enders
• Chapter 3. Exploratory Data Analysis Paul F. Velleman and David C. Hoaglin
• Chapter 4. Graphic Displays of Data Leland Wilkinson
• Chapter 5. Estimating and Visualizing Interactions in Moderated Multiple Regression Connor J. McCabe and Kevin M. King
• Chapter 6. Effect Size Estimation Michael Borenstein
• Chapter 7. Measures of Clinically Significant Change Russell J. Bailey, Benjamin M. Ogles, and Michael J. Lambert
• Chapter 8. Analysis of Variance and the General Linear Model James Jaccard and Ai Bo
• Chapter 9. Generalized Linear Models David Rindskopf
• Chapter 10. Multilevel Modeling for Psychologists John B. Nezlek
• Chapter 11. Longitudinal Data Analysis Andrew K. Littlefield
• Chapter 12. Event History Analysis Fetene B. Tekle and Jeroen K. Vermunt
• Chapter 13. Latent State-Trait Models Rolf Steyer, Christian Geiser, and Christiane Loß​nitzer
• Chapter 14. Latent Variable Modeling of Continuous Growth David A. Cole, Jeffrey A. Ciesla, and Qimin Liu
• Chapter 15. Dynamical Systems and Differential Equation Models of Change Steven M. Boker and Robert G. Moulder
• Chapter 16. A Multivariate Growth Curve Model for Three-Level Data Patrick J. Curran, Chris L. Strauss, Ethan M. McCormick, and James S. McGinley
• Chapter 17. Exploratory Factor Analysis and Confirmatory Factor Analysis Keith F. Widaman and Jonathan Lee Helm
• Chapter 18. Latent Class and Latent Profile Models Brian P. Flaherty, Liying Wang, and Cara J. Kiff
• Chapter 19. Decision Trees and Ensemble Methods in the Behavioral Sciences Kevin J. Grimm, Ross Jacobucci, and John J. McArdle
• Chapter 20. Using the Social Relations Model to Understand Interpersonal Perception and Behavior P. Niels Christensen, Deborah A. Kashy, and Katelin E. Leahy
• Chapter 21. Dyadic Data Analysis Richard Gonzalez and Dale Griffin
• Chapter 22. The Data of Others: New and Old Faces of Archival Research Sophie Pychlau and David T. Wagner
• Chapter 23. Social Network Analysis in Psychology: Recent Breakthroughs in Methods and Theories Wei Wang, Tobias Stark, James D. Westaby, Adam K. Parr, and Daniel A. Newman
• Chapter 24. Meta-Analysis Jeffrey C. Valentine, Therese D. Pigott, and Joseph Morris

Part II. Publishing and the Publication Process

• Chapter 25. Research Data Management and Sharing Katherine G. Akers and John A. Borghi
• Chapter 26. Questionable Practices in Statistical Analysis Rex B. Kline
• Chapter 27. Ethical Issues in Manuscript Preparation and Authorship Jennifer Crocker

Harris Cooper, PhD, is the Hugo L. Blomquist professor, emeritus, in the Department of Psychology and Neuroscience at Duke University. His research interests concern research synthesis and research methodology, and he also studies the application of social and developmental psychology to education policy. His book Research Synthesis and Meta-Analysis: A Step-by-Step Approach (2017) is in its fifth edition. He is the coeditor of the Handbook of Research Synthesis and Meta-Analysis (3 rd ed. 2019).

In 2007, Dr. Cooper was the recipient of the Frederick Mosteller Award for Contributions to Research Synthesis Methodology, and in 2008 he received the Ingram Olkin Award for Distinguished Lifetime Contribution to Research Synthesis from the Society for Research Synthesis Methodology.

He served as the chair of the Department of Psychology and Neuroscience at Duke University from 2009 to 2014, and from 2017 to 2018 he served as the dean of social science at Duke. Dr. Cooper chaired the first APA committee that developed guidelines for information about research that should be included in manuscripts submitted to APA journals. He currently serves as the editor of American Psychologist, the flagship journal of APA.

Marc N. Coutanche, PhD, is an associate professor of psychology and research scientist in the Learning Research and Development Center at the University of Pittsburgh. Dr. Coutanche directs a program of cognitive neuroscience research and develops and tests new computational techniques to identify and understand the neural information present within neuroimaging data.

His work has been funded by the National Institutes of Health, National Science Foundation, American Psychological Foundation, and other organizations, and he has published in a variety of journals.

Dr. Coutanche received his PhD from the University of Pennsylvania, and conducted postdoctoral training at Yale University. He received a Howard Hughes Medical Institute International Student Research Fellowship and Ruth L. Kirschstein Postdoctoral National Research Service Award, and was named a 2019 Rising Star by the Association for Psychological Science.

Linda M. McMullen, PhD, is professor emerita of psychology at the University of Saskatchewan, Canada. Over her career, she has contributed to the development of qualitative inquiry in psychology through teaching, curriculum development, and pedagogical scholarship; original research; and service to the qualitative research community.

Dr. McMullen introduced qualitative inquiry into both the graduate and undergraduate curriculum in her home department, taught courses at both levels for many years, and has published articles, coedited special issues, and written a book ( Essentials of Discursive Psychology ) that is part of APA’s series on qualitative methodologies, among other works. She has been engaged with building the Society for Qualitative Inquiry in Psychology (SQIP; a section of Division 5 of the APA) into a vibrant scholarly society since its earliest days, and took on many leadership roles while working as a university professor.

Dr. McMullen’s contributions have been recognized by Division 5 of the APA, the Canadian Psychological Association, and the Saskatchewan Psychological Association.

Abigail Panter, PhD, is the senior associate dean for undergraduate education and a professor of psychology in the L. L. Thurstone Psychometric Laboratory at University of North Carolina at Chapel Hill. She is past president of APA’s Division 5, Quantitative and Qualitative Methods.

As a quantitative psychologist, she develops instruments, research designs and data-analytic strategies for applied research questions in higher education, personality, and health. She serves as a program evaluator for UNC’s Chancellor’s Science Scholars Program, and was also principal investigator for The Finish Line Project, a $3 million grant from the U.S. Department of Education that systematically investigated new supports and academic initiatives, especially for first-generation college students.

Her books include the  APA Dictionary of Statistics and Research Methods  (2014), the APA Handbook of Research Methods in Psychology  (first edition; 2012), the Handbook of Ethics in Quantitative Methodology  (2011), and the SAGE Handbook of Methods in Social Psychology (2004), among others.

David Rindskopf, PhD, is distinguished professor at the City University of New York Graduate Center, specializing in research methodology and statistics. His main interests are in Bayesian statistics, causal inference, categorical data analysis, meta-analysis, and latent variable models.

He is a fellow of the American Statistical Association and the American Educational Research Association, and is past president of the Society of Multivariate Experimental Psychology and the New York Chapter of the American Statistical Association.

Kenneth J. Sher, PhD, is chancellor’s professor and curators’ distinguished professor of psychological sciences, emeritus, at the University of Missouri. He received his PhD in clinical psychology from Indiana University (1980) and his clinical internship training at Brown University (1981).

His primary areas of research focus on etiological processes in the development of alcohol dependence, factors that affect the course of drinking and alcohol use disorders throughout adulthood, longitudinal research methodology, psychiatric comorbidity, and nosology. At the University of Missouri he directed the predoctoral and postdoctoral training program in alcohol studies, and his research has been continually funded by the National Institute on Alcohol Abuse and Alcoholism for more than 35 years.

Dr. Sher’s research contributions have been recognized by professional societies including the Research Society on Alcoholism and APA, and throughout his career, he has been heavily involved in service to professional societies and scholarly publications.

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Ch 4: Psychology of Learning

The summer sun shines brightly on a deserted stretch of beach. Suddenly, a tiny grey head emerges from the sand, then another and another. Soon the beach is teeming with loggerhead sea turtle hatchlings (Figure 1). Although only minutes old, the hatchlings know exactly what to do. Their flippers are not very efficient for moving across the hot sand, yet they continue onward, instinctively. Some are quickly snapped up by gulls circling overhead and others become lunch for hungry ghost crabs that dart out of their holes. Despite these dangers, the hatchlings are driven to leave the safety of their nest and find the ocean.

Not far down this same beach, Ben and his son, Julian, paddle out into the ocean on surfboards. A wave approaches. Julian crouches on his board, then jumps up and rides the wave for a few seconds before losing his balance. He emerges from the water in time to watch his father ride the face of the wave.

Unlike baby sea turtles, which know how to find the ocean and swim with no help from their parents, we are not born knowing how to swim (or surf). Yet we humans pride ourselves on our ability to learn. In fact, over thousands of years and across cultures, we have created institutions devoted entirely to learning. But have you ever asked yourself how exactly it is that we learn? What processes are at work as we come to know what we know? This chapter focuses on the primary ways in which learning occurs.

In this section, you’ll learn about learning. It might not be “learning” as you typically think of the word, because we’re not talking about going to school, or studying, or even effortfully trying to remember something. Instead, you’ll see that one of the main types of behavioral learning that we do is simply through an automatic process of association, known as classical conditioning. In classical conditioning, organisms learn to associate events that repeatedly happen together, and researchers study how a reflexive response to a stimulus can be mapped to a different stimulus—by training an association between the two stimuli. Ivan Pavlov’s experiments show how stimulus-response bonds are formed. Watson, the founder of behaviorism, was greatly influenced by Pavlov’s work. He tested humans by conditioning fear in an infant known as Little Albert. His findings suggest that classical conditioning can explain how some fears develop. We will then explore operant conditioning and observational learning.

Learning Objectives

• Recognize and define three basic forms of learning—classical conditioning, operant conditioning, and observational learning

What is Learning?

Birds build nests and migrate as winter approaches. Infants suckle at their mother’s breast. Dogs shake water off wet fur. Salmon swim upstream to spawn, and spiders spin intricate webs. What do these seemingly unrelated behaviors have in common? They all are unlearned behaviors. Both instincts and reflexes are innate behaviors that organisms are born with. Reflexes are a motor or neural reaction to a specific stimulus in the environment. They tend to be simpler than instincts, involve the activity of specific body parts and systems (e.g., the knee-jerk reflex and the contraction of the pupil in bright light), and involve more primitive centers of the central nervous system (e.g., the spinal cord and the medulla). In contrast, instincts  are innate behaviors that are triggered by a broader range of events, such as aging and the change of seasons. They are more complex patterns of behavior, involve movement of the organism as a whole (e.g., sexual activity and migration), and involve higher brain centers.

Both reflexes and instincts help an organism adapt to its environment and do not have to be learned. For example, every healthy human baby has a sucking reflex, present at birth. Babies are born knowing how to suck on a nipple, whether artificial (from a bottle) or human. Nobody teaches the baby to suck, just as no one teaches a sea turtle hatchling to move toward the ocean.

Learning , like reflexes and instincts, allows an organism to adapt to its environment. But unlike instincts and reflexes, learned behaviors involve change and experience: learning is a relatively permanent change in behavior or knowledge that results from experience. In contrast to the innate behaviors discussed above, learning involves acquiring knowledge and skills through experience. Looking back at our surfing scenario, Julian will have to spend much more time training with his surfboard before he learns how to ride the waves like his father.

Learning to surf, as well as any complex learning process (e.g., learning about the discipline of psychology), involves a complex interaction of conscious and unconscious processes. Learning has traditionally been studied in terms of its simplest components—the associations our minds automatically make between events. Our minds have a natural tendency to connect events that occur closely together or in sequence. Associative learning  occurs when an organism makes connections between stimuli or events that occur together in the environment. You will see that associative learning is central to all three basic learning processes discussed in this module; classical conditioning tends to involve unconscious processes, operant conditioning tends to involve conscious processes, and observational learning adds social and cognitive layers to all the basic associative processes, both conscious and unconscious. These learning processes will be discussed in detail later, but it is helpful to have a brief overview of each as you begin to explore how learning is understood from a psychological perspective.

In classical conditioning, also known as Pavlovian conditioning, organisms learn to associate events—or stimuli—that repeatedly happen together. We experience this process throughout our daily lives. For example, you might see a flash of lightning in the sky during a storm and then hear a loud boom of thunder. The sound of the thunder naturally makes you jump (loud noises have that effect by reflex). Because lightning reliably predicts the impending boom of thunder, you may associate the two and jump when you see lightning. Psychological researchers study this associative process by focusing on what can be seen and measured—behaviors. Researchers ask if one stimulus triggers a reflex, can we train a different stimulus to trigger that same reflex? In operant conditioning, organisms learn, again, to associate events—a behavior and its consequence (reinforcement or punishment). A pleasant consequence encourages more of that behavior in the future, whereas a punishment deters the behavior. Imagine you are teaching your dog, Hodor, to sit. You tell Hodor to sit, and give him a treat when he does. After repeated experiences, Hodor begins to associate the act of sitting with receiving a treat. He learns that the consequence of sitting is that he gets a doggie biscuit (Figure 3). Conversely, if the dog is punished when exhibiting a behavior, it becomes conditioned to avoid that behavior (e.g., receiving a small shock when crossing the boundary of an invisible electric fence).

Observational learning extends the effective range of both classical and operant conditioning. In contrast to classical and operant conditioning, in which learning occurs only through direct experience, observational learning is the process of watching others and then imitating what they do. A lot of learning among humans and other animals comes from observational learning. To get an idea of the extra effective range that observational learning brings, consider Ben and his son Julian from the introduction. How might observation help Julian learn to surf, as opposed to learning by trial and error alone? By watching his father, he can imitate the moves that bring success and avoid the moves that lead to failure. Can you think of something you have learned how to do after watching someone else?

All of the approaches covered in this chapter are part of a particular tradition in psychology, called behaviorism. However, these approaches you’ll be introduced to do not represent the entire study of learning. Separate traditions of learning have taken shape within different fields of psychology, such as memory and cognition, so you will find that other chapters of this book will round out your understanding of the topic. Over time these traditions tend to converge. For example, in this chapter you will see how cognition has come to play a larger role in behaviorism, whose more extreme adherents once insisted that behaviors are triggered by the environment with no intervening thought.

For a sneak peak and overview of the main different types of learning, watch the CrashCourse psychology below. We’ll learn about each of these topics in greater depth throughout this module.

You can view the transcript for “How to Train a Brain: Crash Course Psychology #11” here (opens in new window) .

Think It Over

• What is your personal definition of learning? How do your ideas about learning compare with the definition of learning presented in this text?
• What kinds of things have you learned through the process of classical conditioning? Operant conditioning? Observational learning? How did you learn them?

Classical Conditioning

• Explain how classical conditioning occurs
• Identify the NS, UCS, UCR, CS, and CR in classical conditioning situations
• Describe the processes of acquisition, extinction, spontaneous recovery, generalization, and discrimination

Does the name Ivan Pavlov ring a bell? Even if you are new to the study of psychology, chances are that you have heard of Pavlov and his famous dogs.

Pavlov (1849–1936), a Russian scientist, performed extensive research on dogs and is best known for his experiments in classical conditioning (Figure 4). As we discussed briefly in the previous section, classical conditioning  is a process by which we learn to associate stimuli and, consequently, to anticipate events.

Pavlov came to his conclusions about how learning occurs completely by accident. Pavlov was a physiologist, not a psychologist. Physiologists study the life processes of organisms, from the molecular level to the level of cells, organ systems, and entire organisms. Pavlov’s area of interest was the digestive system (Hunt, 2007). In his studies with dogs, Pavlov surgically implanted tubes inside dogs’ cheeks to collect saliva. He then measured the amount of saliva produced in response to various foods. Over time, Pavlov (1927) observed that the dogs began to salivate not only at the taste of food, but also at the sight of food, at the sight of an empty food bowl, and even at the sound of the laboratory assistants’ footsteps. Salivating to food in the mouth is reflexive, so no learning is involved. However, dogs don’t naturally salivate at the sight of an empty bowl or the sound of footsteps.

These unusual responses intrigued Pavlov, and he wondered what accounted for what he called the dogs’ “psychic secretions” (Pavlov, 1927). To explore this phenomenon in an objective manner, Pavlov designed a series of carefully controlled experiments to see which stimuli would cause the dogs to salivate. He was able to train the dogs to salivate in response to stimuli that clearly had nothing to do with food, such as the sound of a bell, a light, and a touch on the leg. Through his experiments, Pavlov realized that an organism has two types of responses to its environment: (1) unconditioned (unlearned) responses, or reflexes, and (2) conditioned (learned) responses.

In Pavlov’s experiments, the dogs salivated each time meat powder was presented to them. The meat powder in this situation was an un conditioned stimulus (UCS) : a stimulus that elicits a reflexive response in an organism. The dogs’ salivation was an unconditioned response (UCR) : a natural (unlearned) reaction to a given stimulus. Before conditioning, think of the dogs’ stimulus and response like this:

In classical conditioning, a neutral stimulus is presented immediately before an unconditioned stimulus. Pavlov would sound a tone (like ringing a bell) and then give the dogs the meat powder (Figure 5). The tone was the neutral stimulus (NS), which is a stimulus that does not naturally elicit a response. Prior to conditioning, the dogs did not salivate when they just heard the tone because the tone had no association for the dogs. Quite simply this pairing means:

When Pavlov paired the tone with the meat powder over and over again, the previously neutral stimulus (the tone) also began to elicit salivation from the dogs. Thus, the neutral stimulus became the conditioned stimulus (CS) , which is a stimulus that elicits a response after repeatedly being paired with an unconditioned stimulus. Eventually, the dogs began to salivate to the tone alone, just as they previously had salivated at the sound of the assistants’ footsteps. The behavior caused by the conditioned stimulus is called the conditioned response (CR) . In the case of Pavlov’s dogs, they had learned to associate the tone (CS) with being fed, and they began to salivate (CR) in anticipation of food.

View the following video to learn more about Pavlov and his dogs:

You can view the transcript for “Classical Conditioning – Ivan Pavlov” here (opens in new window) .

Real World Application of Classical Conditioning

Everyday Connection: Classical Conditioning at Stingray City

Kate and her husband Scott recently vacationed in the Cayman Islands, and booked a boat tour to Stingray City, where they could feed and swim with the southern stingrays. The boat captain explained how the normally solitary stingrays have become accustomed to interacting with humans. About 40 years ago, fishermen began to clean fish and conch (unconditioned stimulus) at a particular sandbar near a barrier reef, and large numbers of stingrays would swim in to eat (unconditioned response) what the fishermen threw into the water; this continued for years. By the late 1980s, word of the large group of stingrays spread among scuba divers, who then started feeding them by hand. Over time, the southern stingrays in the area were classically conditioned much like Pavlov’s dogs. When they hear the sound of a boat engine (neutral stimulus that becomes a conditioned stimulus), they know that they will get to eat (conditioned response).

As soon as Kate and Scott reached Stingray City, over two dozen stingrays surrounded their tour boat. The couple slipped into the water with bags of squid, the stingrays’ favorite treat. The swarm of stingrays bumped and rubbed up against their legs like hungry cats (Figure 7). Kate and Scott were able to feed, pet, and even kiss (for luck) these amazing creatures. Then all the squid was gone, and so were the stingrays.

Classical conditioning also applies to humans, even babies. For example, Sara buys formula in blue canisters for her six-month-old daughter, Angelina. Whenever Sara takes out a formula container, Angelina gets excited, tries to reach toward the food, and most likely salivates. Why does Angelina get excited when she sees the formula canister? What are the UCS, CS, UCR, and CR here?

So far, all of the examples have involved food, but classical conditioning extends beyond the basic need to be fed. Consider our earlier example of a dog whose owners install an invisible electric dog fence. A small electrical shock (unconditioned stimulus) elicits discomfort (unconditioned response). When the unconditioned stimulus (shock) is paired with a neutral stimulus (the edge of a yard), the dog associates the discomfort (unconditioned response) with the edge of the yard (conditioned stimulus) and stays within the set boundaries.

Link to Learning

Can you think of an example in your life of how classical conditioning has produced a positive emotional response, such as happiness or excitement? How about a negative emotional response, such as fear, anxiety, or anger?

Processes in Classical Conditioning

Of course, these processes also apply in humans. For example, let’s say that every day when you walk to campus, an ice cream truck passes your route. Day after day, you hear the truck’s music (neutral stimulus), so you finally stop and purchase a chocolate ice cream bar. You take a bite (unconditioned stimulus) and then your mouth waters (unconditioned response). This initial period of learning is known as acquisition, when you begin to connect the neutral stimulus (the sound of the truck) and the unconditioned stimulus (the taste of the chocolate ice cream in your mouth). During acquisition, the conditioned response gets stronger and stronger through repeated pairings of the conditioned stimulus and unconditioned stimulus. Several days (and ice cream bars) later, you notice that your mouth begins to water (conditioned response) as soon as you hear the truck’s musical jingle—even before you bite into the ice cream bar. Then one day you head down the street. You hear the truck’s music (conditioned stimulus), and your mouth waters (conditioned response). However, when you get to the truck, you discover that they are all out of ice cream. You leave disappointed. The next few days you pass by the truck and hear the music, but don’t stop to get an ice cream bar because you’re running late for class. You begin to salivate less and less when you hear the music, until by the end of the week, your mouth no longer waters when you hear the tune. This illustrates extinction. The conditioned response weakens when only the conditioned stimulus (the sound of the truck) is presented, without being followed by the unconditioned stimulus (chocolate ice cream in the mouth). Then the weekend comes. You don’t have to go to class, so you don’t pass the truck. Monday morning arrives and you take your usual route to campus. You round the corner and hear the truck again. What do you think happens? Your mouth begins to water again. Why? After a break from conditioning, the conditioned response reappears, which indicates spontaneous recovery.

Acquisition and extinction involve the strengthening and weakening, respectively, of a learned association. Two other learning processes—stimulus discrimination and stimulus generalization—are involved in distinguishing which stimuli will trigger the learned association. Animals (including humans) need to distinguish between stimuli—for example, between sounds that predict a threatening event and sounds that do not—so that they can respond appropriately (such as running away if the sound is threatening). When an organism learns to respond differently to various stimuli that are similar, it is called stimulus discrimination . In classical conditioning terms, the organism demonstrates the conditioned response only to the conditioned stimulus. Pavlov’s dogs discriminated between the basic tone that sounded before they were fed and other tones (e.g., the doorbell), because the other sounds did not predict the arrival of food. Similarly, Tiger, the cat, discriminated between the sound of the can opener and the sound of the electric mixer. When the electric mixer is going, Tiger is not about to be fed, so she does not come running to the kitchen looking for food.

On the other hand, when an organism demonstrates the conditioned response to stimuli that are similar to the condition stimulus, it is called stimulus generalization , the opposite of stimulus discrimination. The more similar a stimulus is to the condition stimulus, the more likely the organism is to give the conditioned response. For instance, if the electric mixer sounds very similar to the electric can opener, Tiger may come running after hearing its sound. But if you do not feed her following the electric mixer sound, and you continue to feed her consistently after the electric can opener sound, she will quickly learn to discriminate between the two sounds (provided they are sufficiently dissimilar that she can tell them apart).

Sometimes, classical conditioning can lead to habituation. Habituation  occurs when we learn not to respond to a stimulus that is presented repeatedly without change. As the stimulus occurs over and over, we learn not to focus our attention on it. For example, imagine that your neighbor or roommate constantly has the television blaring. This background noise is distracting and makes it difficult for you to focus when you’re studying. However, over time, you become accustomed to the stimulus of the television noise, and eventually you hardly notice it any longer.

Classical Conditioning and Behaviorism

John B. Watson, shown in Figure 9, is considered the founder of behaviorism. Behaviorism is a school of thought that arose during the first part of the 20th century, which incorporates elements of Pavlov’s classical conditioning (Hunt, 2007). In stark contrast with Freud, who considered the reasons for behavior to be hidden in the unconscious, Watson championed the idea that all behavior can be studied as a simple stimulus-response reaction, without regard for internal processes. Watson argued that in order for psychology to become a legitimate science, it must shift its concern away from internal mental processes because mental processes cannot be seen or measured. Instead, he asserted that psychology must focus on outward observable behavior that can be measured.

Watson’s ideas were influenced by Pavlov’s work. According to Watson, human behavior, just like animal behavior, is primarily the result of conditioned responses. Whereas Pavlov’s work with dogs involved the conditioning of reflexes, Watson believed the same principles could be extended to the conditioning of human emotions (Watson, 1919). Thus began Watson’s work with his graduate student Rosalie Rayner and a baby called Little Albert. Through their experiments with Little Albert, Watson and Rayner (1920) demonstrated how fears can be conditioned.

In 1920, Watson was the chair of the psychology department at Johns Hopkins University. Through his position at the university he came to meet Little Albert’s mother, Arvilla Merritte, who worked at a campus hospital (DeAngelis, 2010). Watson offered her a dollar to allow her son to be the subject of his experiments in classical conditioning. Through these ‘experiments,’ Little Albert was exposed to and conditioned to fear certain things. Initially he was presented with various neutral stimuli, including a rabbit, a dog, a monkey, masks, cotton wool, and a white rat. He was not afraid of any of these things. Then Watson, with the help of Rayner, conditioned Little Albert to associate these stimuli with an emotion—fear. For example, Watson handed Little Albert the white rat, and Little Albert enjoyed playing with it. Then Watson made a loud sound, by striking a hammer against a metal bar hanging behind Little Albert’s head, each time Little Albert touched the rat. Little Albert was frightened by the sound—demonstrating a reflexive fear of sudden loud noises—and began to cry. Watson repeatedly paired the loud sound with the white rat. Soon Little Albert became frightened by the white rat alone. [This to yourself… In this case, what are the UCS, CS, UCR, and CR?] Days later, Little Albert demonstrated stimulus generalization—he became afraid of other furry things: a rabbit, a furry coat, and even a Santa Claus mask (Figure 10). Watson had succeeded in conditioning a fear response in Little Albert, thus demonstrating that emotions could become conditioned responses. It had been Watson’s intention to produce a phobia—a persistent, excessive fear of a specific object or situation— through conditioning alone, thus countering Freud’s view that phobias are caused by deep, hidden conflicts in the mind. However, there is no evidence that Little Albert experienced phobias in later years. Little Albert’s mother moved away, ending the experiment, and Little Albert himself died a few years later of unrelated causes. While Watson’s research provided new insight into conditioning, it would be considered unethical by today’s standards.

View scenes from John Watson’s “experiment” in which Little Albert was conditioned to respond in fear to furry objects.

As you watch the video, look closely at Little Albert’s reactions and the manner in which Watson and Rayner present the stimuli before and after conditioning. Based on what you see, would you come to the same conclusions as the researchers?

Everyday Connection: Advertising and Associative Learning

Advertising executives are pros at applying the principles of associative learning. Think about the car commercials you have seen on television. Many of them feature an attractive model. By associating the model with the car being advertised, you come to see the car as being desirable (Cialdini, 2008). You may be asking yourself, does this advertising technique actually work? According to Cialdini (2008), men who viewed a car commercial that included an attractive model later rated the car as being faster, more appealing, and better designed than did men who viewed an advertisement for the same car minus the model.

Have you ever noticed how quickly advertisers cancel contracts with a famous athlete following a scandal? As far as the advertiser is concerned, that athlete is no longer associated with positive feelings; therefore, the athlete cannot be used as an unconditioned stimulus to condition the public to associate positive feelings (the unconditioned response) with their product (the conditioned stimulus).

Now that you are aware of how associative learning works, see if you can find examples of these types of advertisements on television, in magazines, or on the Internet.

Operant Conditioning

You’ve already learned about classical conditioning, or conditioning by association. This section will focus on operant conditioning, which emphasizes reinforcement for behaviors. In operant conditioning, the motivation for a behavior happens after the behavior is demonstrated. An animal or a human receives a consequence (reinforcer or punisher) after performing a specific behavior. You’ll learn that all types of reinforcement (positive or negative) increase  the likelihood of a behavioral response, while all types of punishment  decrease  the likelihood of a behavioral response.

Watch this video for a review of classical conditioning and an introduction of operant conditioning to help you differentiate between the two types of learning.

You can view the transcript for “The difference between classical and operant conditioning – Peggy Andover” here (opens in new window) .

• Define and give examples of operant conditioning
• Explain the difference between reinforcement and punishment (including positive and negative reinforcement and positive and negative punishment)
• Define shaping
• Differentiate between primary and secondary reinforcers
• Distinguish between reinforcement schedules

The previous section of this module focused on the type of associative learning known as classical conditioning. Remember that in classical conditioning, something in the environment triggers a reflex automatically, and researchers train the organism to react to a different stimulus. Now we turn to the second type of associative learning, operant conditioning. In operant conditioning , organisms learn to associate a behavior and its consequence (Table 1). A pleasant consequence makes that behavior more likely to be repeated in the future. For example, Spirit, a dolphin at the National Aquarium in Baltimore, does a flip in the air when her trainer blows a whistle. The consequence is that she gets a fish.

Psychologist B. F. Skinner saw that classical conditioning is limited to existing behaviors that are reflexively elicited, and it doesn’t account for new behaviors such as riding a bike. He proposed a theory about how such behaviors come about. Skinner believed that behavior is motivated by the consequences we receive for the behavior: the reinforcements and punishments. His idea that learning is the result of consequences is based on the law of effect , which was first proposed by psychologist Edward Thorndike. According to the law of effect, behaviors that are followed by consequences that are satisfying to the organism are more likely to be repeated, and behaviors that are followed by unpleasant consequences are less likely to be repeated (Thorndike, 1911). Essentially, if an organism does something that brings about a desired result, the organism is more likely to do it again. If an organism does something that does not bring about a desired result, the organism is less likely to do it again. An example of the law of effect is in employment. One of the reasons (and often the main reason) we show up for work is because we get paid to do so. If we stop getting paid, we will likely stop showing up—even if we love our job.

Working with Thorndike’s law of effect as his foundation, Skinner began conducting scientific experiments on animals (mainly rats and pigeons) to determine how organisms learn through operant conditioning (Skinner, 1938). He placed these animals inside an operant conditioning chamber, which has come to be known as a “Skinner box” (Figure 11). A Skinner box contains a lever (for rats) or disk (for pigeons) that the animal can press or peck for a food reward via the dispenser. Speakers and lights can be associated with certain behaviors. A recorder counts the number of responses made by the animal.

Watch the following clip to learn more about operant conditioning and to watch an interview with Skinner as he talks about conditioning pigeons.

You can view the transcript for “Operant conditioning” here (opens in new window) .

Reinforcement and Punishment

In discussing operant conditioning, we use several everyday words—positive, negative, reinforcement, and punishment—in a specialized manner. In operant conditioning, positive and negative do not mean good and bad. Instead, positive means you are adding something, and negative means you are taking something away. Reinforcement means you are increasing a behavior, and punishment means you are decreasing a behavior. Reinforcement can be positive or negative, and punishment can also be positive or negative. All reinforcers (positive or negative) increase the likelihood of a behavioral response. All punishers (positive or negative) decrease the likelihood of a behavioral response. Now let’s combine these four terms: positive reinforcement, negative reinforcement, positive punishment, and negative punishment (Table 2).

Reinforcement

The most effective way to teach a person or animal a new behavior is with positive reinforcement. In positive reinforcement , a desirable stimulus is added to increase a behavior.

For example, you tell your five-year-old son, Jerome, that if he cleans his room, he will get a toy. Jerome quickly cleans his room because he wants a new art set. Let’s pause for a moment. Some people might say, “Why should I reward my child for doing what is expected?” But in fact we are constantly and consistently rewarded in our lives. Our paychecks are rewards, as are high grades and acceptance into our preferred school. Being praised for doing a good job and for passing a driver’s test is also a reward. Positive reinforcement as a learning tool is extremely effective. It has been found that one of the most effective ways to increase achievement in school districts with below-average reading scores was to pay the children to read. Specifically, second-grade students in Dallas were paid $2 each time they read a book and passed a short quiz about the book. The result was a significant increase in reading comprehension (Fryer, 2010). What do you think about this program? If Skinner were alive today, he would probably think this was a great idea. He was a strong proponent of using operant conditioning principles to influence students’ behavior at school. In fact, in addition to the Skinner box, he also invented what he called a teaching machine that was designed to reward small steps in learning (Skinner, 1961)—an early forerunner of computer-assisted learning. His teaching machine tested students’ knowledge as they worked through various school subjects. If students answered questions correctly, they received immediate positive reinforcement and could continue; if they answered incorrectly, they did not receive any reinforcement. The idea was that students would spend additional time studying the material to increase their chance of being reinforced the next time (Skinner, 1961).

In negative reinforcement , an undesirable stimulus is removed to increase a behavior. For example, car manufacturers use the principles of negative reinforcement in their seatbelt systems, which go “beep, beep, beep” until you fasten your seatbelt. The annoying sound stops when you exhibit the desired behavior, increasing the likelihood that you will buckle up in the future. Negative reinforcement is also used frequently in horse training. Riders apply pressure—by pulling the reins or squeezing their legs—and then remove the pressure when the horse performs the desired behavior, such as turning or speeding up. The pressure is the negative stimulus that the horse wants to remove.

Many people confuse negative reinforcement with punishment in operant conditioning, but they are two very different mechanisms. Remember that reinforcement, even when it is negative, always increases a behavior. In contrast, punishment always decreases a behavior. In positive punishment, you add an undesirable stimulus to decrease a behavior. An example of positive punishment is scolding a student to get the student to stop texting in class. In this case, a stimulus (the reprimand) is added in order to decrease the behavior (texting in class). In negative punishment , you remove a pleasant stimulus to decrease a behavior. For example, when a child misbehaves, a parent can take away a favorite toy. In this case, a stimulus (the toy) is removed in order to decrease the behavior.

Punishment, especially when it is immediate, is one way to decrease undesirable behavior. For example, imagine your four year-old son, Brandon, hit his younger brother. You have Brandon write 50 times “I will not hit my brother” (positive punishment). Chances are he won’t repeat this behavior. While strategies like this are common today, in the past children were often subject to physical punishment, such as spanking. It’s important to be aware of some of the drawbacks in using physical punishment on children. First, punishment may teach fear. Brandon may become fearful of the hitting, but he also may become fearful of the person who delivered the punishment—you, his parent. Similarly, children who are punished by teachers may come to fear the teacher and try to avoid school (Gershoff et al., 2010). Consequently, most schools in the United States have banned corporal punishment. Second, punishment may cause children to become more aggressive and prone to antisocial behavior and delinquency (Gershoff, 2002). They see their parents resort to spanking when they become angry and frustrated, so, in turn, they may act out this same behavior when they become angry and frustrated. For example, because you spank Margot when you are angry with her for her misbehavior, she might start hitting her friends when they won’t share their toys.

While positive punishment can be effective in some cases, Skinner suggested that the use of punishment should be weighed against the possible negative effects. Today’s psychologists and parenting experts favor reinforcement over punishment—they recommend that you catch your child doing something good and reward her for it.

Make sure you understand the distinction between negative reinforcement and punishment in the following video:

You can view the transcript for “Learning: Negative Reinforcement vs. Punishment” here (opens in new window) .

In his operant conditioning experiments, Skinner often used an approach called shaping. Instead of rewarding only the target behavior, in shaping , we reward successive approximations of a target behavior. Why is shaping needed? Remember that in order for reinforcement to work, the organism must first display the behavior. Shaping is needed because it is extremely unlikely that an organism will display anything but the simplest of behaviors spontaneously. In shaping, behaviors are broken down into many small, achievable steps. The specific steps used in the process are the following: Reinforce any response that resembles the desired behavior. Then reinforce the response that more closely resembles the desired behavior. You will no longer reinforce the previously reinforced response. Next, begin to reinforce the response that even more closely resembles the desired behavior. Continue to reinforce closer and closer approximations of the desired behavior. Finally, only reinforce the desired behavior.

Shaping is often used in teaching a complex behavior or chain of behaviors. Skinner used shaping to teach pigeons not only such relatively simple behaviors as pecking a disk in a Skinner box, but also many unusual and entertaining behaviors, such as turning in circles, walking in figure eights, and even playing ping pong; the technique is commonly used by animal trainers today. An important part of shaping is stimulus discrimination. Recall Pavlov’s dogs—he trained them to respond to the tone of a bell, and not to similar tones or sounds. This discrimination is also important in operant conditioning and in shaping behavior.

Here is a brief video of Skinner’s pigeons playing ping pong.

You can view the transcript for “BF Skinner Foundation – Pigeon Ping Pong Clip” here (opens in new window) .

It’s easy to see how shaping is effective in teaching behaviors to animals, but how does shaping work with humans? Let’s consider parents whose goal is to have their child learn to clean his room. They use shaping to help him master steps toward the goal. Instead of performing the entire task, they set up these steps and reinforce each step. First, he cleans up one toy. Second, he cleans up five toys. Third, he chooses whether to pick up ten toys or put his books and clothes away. Fourth, he cleans up everything except two toys. Finally, he cleans his entire room.

Primary and Secondary Reinforcers

Rewards such as stickers, praise, money, toys, and more can be used to reinforce learning. Let’s go back to Skinner’s rats again. How did the rats learn to press the lever in the Skinner box? They were rewarded with food each time they pressed the lever. For animals, food would be an obvious reinforcer.

What would be a good reinforce for humans? For your daughter Sydney, it was the promise of a toy if she cleaned her room. How about Joaquin, the soccer player? If you gave Joaquin a piece of candy every time he made a goal, you would be using a primary reinforcer. Primary reinforcers are reinforcers that have innate reinforcing qualities. These kinds of reinforcers are not learned. Water, food, sleep, shelter, sex, and touch, among others, are primary reinforcers . Pleasure is also a primary reinforcer. Organisms do not lose their drive for these things. For most people, jumping in a cool lake on a very hot day would be reinforcing and the cool lake would be innately reinforcing—the water would cool the person off (a physical need), as well as provide pleasure.

A secondary reinforcer  has no inherent value and only has reinforcing qualities when linked with a primary reinforcer. Praise, linked to affection, is one example of a secondary reinforcer, as when you called out “Great shot!” every time Joaquin made a goal. Another example, money, is only worth something when you can use it to buy other things—either things that satisfy basic needs (food, water, shelter—all primary reinforcers) or other secondary reinforcers. If you were on a remote island in the middle of the Pacific Ocean and you had stacks of money, the money would not be useful if you could not spend it. What about the stickers on the behavior chart? They also are secondary reinforcers.

Sometimes, instead of stickers on a sticker chart, a token is used. Tokens, which are also secondary reinforcers, can then be traded in for rewards and prizes. Entire behavior management systems, known as token economies, are built around the use of these kinds of token reinforcers. Token economies have been found to be very effective at modifying behavior in a variety of settings such as schools, prisons, and mental hospitals. For example, a study by Cangi and Daly (2013) found that use of a token economy increased appropriate social behaviors and reduced inappropriate behaviors in a group of autistic school children. Autistic children tend to exhibit disruptive behaviors such as pinching and hitting. When the children in the study exhibited appropriate behavior (not hitting or pinching), they received a “quiet hands” token. When they hit or pinched, they lost a token. The children could then exchange specified amounts of tokens for minutes of playtime.

Everyday Connection: Behavior Modification in Children

Parents and teachers often use behavior modification to change a child’s behavior. Behavior modification uses the principles of operant conditioning to accomplish behavior change so that undesirable behaviors are switched for more socially acceptable ones. Some teachers and parents create a sticker chart, in which several behaviors are listed (Figure 12). Sticker charts are a form of token economies, as described in the text. Each time children perform the behavior, they get a sticker, and after a certain number of stickers, they get a prize, or reinforcer. The goal is to increase acceptable behaviors and decrease misbehavior. Remember, it is best to reinforce desired behaviors, rather than to use punishment. In the classroom, the teacher can reinforce a wide range of behaviors, from students raising their hands, to walking quietly in the hall, to turning in their homework. At home, parents might create a behavior chart that rewards children for things such as putting away toys, brushing their teeth, and helping with dinner. In order for behavior modification to be effective, the reinforcement needs to be connected with the behavior; the reinforcement must matter to the child and be done consistently.

Time-out is another popular technique used in behavior modification with children. It operates on the principle of negative punishment. When a child demonstrates an undesirable behavior, she is removed from the desirable activity at hand (Figure 13). For example, say that Sophia and her brother Mario are playing with building blocks. Sophia throws some blocks at her brother, so you give her a warning that she will go to time-out if she does it again. A few minutes later, she throws more blocks at Mario. You remove Sophia from the room for a few minutes. When she comes back, she doesn’t throw blocks.

There are several important points that you should know if you plan to implement time-out as a behavior modification technique. First, make sure the child is being removed from a desirable activity and placed in a less desirable location. If the activity is something undesirable for the child, this technique will backfire because it is more enjoyable for the child to be removed from the activity. Second, the length of the time-out is important. The general rule of thumb is one minute for each year of the child’s age. Sophia is five; therefore, she sits in a time-out for five minutes. Setting a timer helps children know how long they have to sit in time-out. Finally, as a caregiver, keep several guidelines in mind over the course of a time-out: remain calm when directing your child to time-out; ignore your child during time-out (because caregiver attention may reinforce misbehavior); and give the child a hug or a kind word when time-out is over.

• Explain the difference between negative reinforcement and punishment, and provide several examples of each based on your own experiences.
• Think of a behavior that you have that you would like to change. How could you use behavior modification, specifically positive reinforcement, to change your behavior? What is your positive reinforcer?

Reinforcement Schedules

Remember, the best way to teach a person or animal a behavior is to use positive reinforcement. For example, Skinner used positive reinforcement to teach rats to press a lever in a Skinner box. At first, the rat might randomly hit the lever while exploring the box, and out would come a pellet of food. After eating the pellet, what do you think the hungry rat did next? It hit the lever again, and received another pellet of food. Each time the rat hit the lever, a pellet of food came out. When an organism receives a reinforcer each time it displays a behavior, it is called continuous reinforcement . This reinforcement schedule is the quickest way to teach someone a behavior, and it is especially effective in training a new behavior. Let’s look back at the dog that was learning to sit earlier in the module. Now, each time he sits, you give him a treat. Timing is important here: you will be most successful if you present the reinforcer immediately after he sits, so that he can make an association between the target behavior (sitting) and the consequence (getting a treat).

Now let’s combine these four terms. A fixed interval reinforcement schedule  is when behavior is rewarded after a set amount of time. For example, June undergoes major surgery in a hospital. During recovery, she is expected to experience pain and will require prescription medications for pain relief. June is given an IV drip with a patient-controlled painkiller. Her doctor sets a limit: one dose per hour. June pushes a button when pain becomes difficult, and she receives a dose of medication. Since the reward (pain relief) only occurs on a fixed interval, there is no point in exhibiting the behavior when it will not be rewarded.

With a variable interval reinforcement schedule , the person or animal gets the reinforcement based on varying amounts of time, which are unpredictable. Say that Manuel is the manager at a fast-food restaurant. Every once in a while someone from the quality control division comes to Manuel’s restaurant. If the restaurant is clean and the service is fast, everyone on that shift earns a $20 bonus. Manuel never knows when the quality control person will show up, so he always tries to keep the restaurant clean and ensures that his employees provide prompt and courteous service. His productivity regarding prompt service and keeping a clean restaurant are steady because he wants his crew to earn the bonus.

With a fixed ratio reinforcement schedule , there are a set number of responses that must occur before the behavior is rewarded. Carla sells glasses at an eyeglass store, and she earns a commission every time she sells a pair of glasses. She always tries to sell people more pairs of glasses, including prescription sunglasses or a backup pair, so she can increase her commission. She does not care if the person really needs the prescription sunglasses, Carla just wants her bonus. The quality of what Carla sells does not matter because her commission is not based on quality; it’s only based on the number of pairs sold. This distinction in the quality of performance can help determine which reinforcement method is most appropriate for a particular situation. Fixed ratios are better suited to optimize the quantity of output, whereas a fixed interval, in which the reward is not quantity based, can lead to a higher quality of output.

In a variable ratio reinforcement schedule , the number of responses needed for a reward varies. This is the most powerful partial reinforcement schedule. An example of the variable ratio reinforcement schedule is gambling. Imagine that Sarah—generally a smart, thrifty woman—visits Las Vegas for the first time. She is not a gambler, but out of curiosity she puts a quarter into the slot machine, and then another, and another. Nothing happens. Two dollars in quarters later, her curiosity is fading, and she is just about to quit. But then, the machine lights up, bells go off, and Sarah gets 50 quarters back. That’s more like it! Sarah gets back to inserting quarters with renewed interest, and a few minutes later she has used up all her gains and is $10 in the hole. Now might be a sensible time to quit. And yet, she keeps putting money into the slot machine because she never knows when the next reinforcement is coming. She keeps thinking that with the next quarter she could win $50, or $100, or even more. Because the reinforcement schedule in most types of gambling has a variable ratio schedule, people keep trying and hoping that the next time they will win big. This is one of the reasons that gambling is so addictive—and so resistant to extinction.

Review the schedules of reinforcement in the following video.

You can view the transcript for “Learning: Schedules of Reinforcement” here (opens in new window) .

In operant conditioning , extinction of a reinforced behavior occurs at some point after reinforcement stops, and the speed at which this happens depends on the reinforcement schedule. In a variable ratio schedule, the point of extinction comes very slowly, as described above. But in the other reinforcement schedules, extinction may come quickly. For example, if June presses the button for the pain relief medication before the allotted time her doctor has approved, no medication is administered. She is on a fixed interval reinforcement schedule (dosed hourly), so extinction occurs quickly when reinforcement doesn’t come at the expected time. Among the reinforcement schedules, variable ratio is the most productive and the most resistant to extinction. Fixed interval is the least productive and the easiest to extinguish (Figure 14).

Everyday Connections: Gambling and the Brain

Skinner (1953) stated, “If the gambling establishment cannot persuade a patron to turn over money with no return, it may achieve the same effect by returning part of the patron’s money on a variable-ratio schedule” (p. 397).

Skinner uses gambling as an example of the power and effectiveness of conditioning behavior based on a variable ratio reinforcement schedule. In fact, Skinner was so confident in his knowledge of gambling addiction that he even claimed he could turn a pigeon into a pathological gambler (“Skinner’s Utopia,” 1971). Beyond the power of variable ratio reinforcement, gambling seems to work on the brain in the same way as some addictive drugs. The Illinois Institute for Addiction Recovery (n.d.) reports evidence suggesting that pathological gambling is an addiction similar to a chemical addiction (Figure 15). Specifically, gambling may activate the reward centers of the brain, much like cocaine does. Research has shown that some pathological gamblers have lower levels of the neurotransmitter (brain chemical) known as norepinephrine than do normal gamblers (Roy, et al., 1988). According to a study conducted by Alec Roy and colleagues, norepinephrine is secreted when a person feels stress, arousal, or thrill; pathological gamblers use gambling to increase their levels of this neurotransmitter. Another researcher, neuroscientist Hans Breiter, has done extensive research on gambling and its effects on the brain. Breiter (as cited in Franzen, 2001) reports that “Monetary reward in a gambling-like experiment produces brain activation very similar to that observed in a cocaine addict receiving an infusion of cocaine” (para. 1). Deficiencies in serotonin (another neurotransmitter) might also contribute to compulsive behavior, including a gambling addiction.

It may be that pathological gamblers’ brains are different than those of other people, and perhaps this difference may somehow have led to their gambling addiction, as these studies seem to suggest. However, it is very difficult to ascertain the cause because it is impossible to conduct a true experiment (it would be unethical to try to turn randomly assigned participants into problem gamblers). Therefore, it may be that causation actually moves in the opposite direction—perhaps the act of gambling somehow changes neurotransmitter levels in some gamblers’ brains. It also is possible that some overlooked factor, or confounding variable, played a role in both the gambling addiction and the differences in brain chemistry.

Other Types of Learning

Classical and operant conditioning are responsible for a good bit of the behaviors we learn and develop, but certainly there are other things we learn simply through observation and thought. Latent learning is a form of learning that occurs without any obvious reinforcement of the behavior or associations that are learned.

According to Albert Bandura, learning can occur by watching others and then modeling what they do or say. This is known as observational learning. There are specific steps in the process of modeling that must be followed if learning is to be successful. These steps include attention, retention, reproduction, and motivation. Through modeling, Bandura has shown that children learn many things both good and bad simply by watching their parents, siblings, and others. What have you learned by observation?

• Explain latent learning and cognitive maps
• Explain observational learning and the steps in the modeling process

Latent Learning

Although strict behaviorists such as Skinner and Watson refused to believe that cognition (such as thoughts and expectations) plays a role in learning, another behaviorist, Edward C. Tolman, had a different opinion. Tolman’s experiments with rats demonstrated that organisms can learn even if they do not receive immediate reinforcement (Tolman & Honzik, 1930; Tolman, Ritchie, & Kalish, 1946).

Latent learning  is a form of learning that is not immediately expressed in an overt response. It occurs without any obvious reinforcement of the behavior or associations that are learned. Latent learning is not readily apparent to the researcher because it is not shown behaviorally until there is sufficient motivation. This type of learning broke the constraints of behaviorism, which stated that processes must be directly observable and that learning was the direct consequence of conditioning to stimuli.

Latent learning also occurs in humans. Children may learn by watching the actions of their parents but only demonstrate it at a later date, when the learned material is needed. For example, suppose that Ravi’s dad drives him to school every day. In this way, Ravi learns the route from his house to his school, but he’s never driven there himself, so he has not had a chance to demonstrate that he’s learned the way. One morning Ravi’s dad has to leave early for a meeting, so he can’t drive Ravi to school. Instead, Ravi follows the same route on his bike that his dad would have taken in the car. This demonstrates latent learning. Ravi had learned the route to school, but had no need to demonstrate this knowledge earlier.

Everyday Connection: This Place Is Like a Maze

Observational learning.

Previous sections of this chapter focused on classical and operant conditioning, which are forms of associative learning. In observational learning , we learn by watching others and then imitating, or modeling, what they do or say. The individuals performing the imitated behavior are called models. Research suggests that this imitative learning involves a specific type of neuron, called a mirror neuron (Hickock, 2010; Rizzolatti, Fadiga, Fogassi, & Gallese, 2002; Rizzolatti, Fogassi, & Gallese, 2006).

Humans and other animals are capable of observational learning. As you will see, the phrase “monkey see, monkey do” really is accurate (Figure 18). The same could be said about other animals. For example, in a study of social learning in chimpanzees, researchers gave juice boxes with straws to two groups of captive chimpanzees. The first group dipped the straw into the juice box, and then sucked on the small amount of juice at the end of the straw. The second group sucked through the straw directly, getting much more juice. When the first group, the “dippers,” observed the second group, “the suckers,” what do you think happened? All of the “dippers” in the first group switched to sucking through the straws directly. By simply observing the other chimps and modeling their behavior, they learned that this was a more efficient method of getting juice (Yamamoto, Humle, and Tanaka, 2013).

Imitation is much more obvious in humans, but is imitation really the sincerest form of flattery? Consider Claire’s experience with observational learning. Claire’s nine-year-old son, Jay, was getting into trouble at school and was defiant at home. Claire feared that Jay would end up like her brothers, two of whom were in prison. One day, after yet another bad day at school and another negative note from the teacher, Claire, at her wit’s end, beat her son with a belt to get him to behave. Later that night, as she put her children to bed, Claire witnessed her four-year-old daughter, Anna, take a belt to her teddy bear and whip it. Claire was horrified, realizing that Anna was imitating her mother. It was then that Claire knew she wanted to discipline her children in a different manner.

Like Tolman, whose experiments with rats suggested a cognitive component to learning, psychologist Albert Bandura’s ideas about learning were different from those of strict behaviorists. Bandura and other researchers proposed a brand of behaviorism called social learning theory , which took cognitive processes into account. According to Bandura, pure behaviorism could not explain why learning can take place in the absence of external reinforcement. He felt that internal mental states must also have a role in learning and that observational learning involves much more than imitation. In imitation, a person simply copies what the model does. Observational learning is much more complex. According to Lefrançois (2012) there are several ways that observational learning can occur: You learn a new response. After watching your coworker get chewed out by your boss for coming in late, you start leaving home 10 minutes earlier so that you won’t be late. You choose whether or not to imitate the model depending on what you saw happen to the model. Remember Julian and his father? When learning to surf, Julian might watch how his father pops up successfully on his surfboard and then attempt to do the same thing. On the other hand, Julian might learn not to touch a hot stove after watching his father get burned on a stove. You learn a general rule that you can apply to other situations.

Bandura identified three kinds of models: live, verbal, and symbolic. A live model demonstrates a behavior in person, as when Ben stood up on his surfboard so that Julian could see how he did it. A verbal instructional model does not perform the behavior, but instead explains or describes the behavior, as when a soccer coach tells his young players to kick the ball with the side of the foot, not with the toe. A symbolic model can be fictional characters or real people who demonstrate behaviors in books, movies, television shows, video games, or Internet sources (Figure 19).

Steps in the Modeling Process

Of course, we don’t learn a behavior simply by observing a model. Bandura described specific steps in the process of modeling that must be followed if learning is to be successful: attention, retention, reproduction, and motivation. First, you must be focused on what the model is doing—you have to pay attention. Next, you must be able to retain, or remember, what you observed; this is retention. Then, you must be able to perform the behavior that you observed and committed to memory; this is reproduction. Finally, you must have motivation. You need to want to copy the behavior, and whether or not you are motivated depends on what happened to the model. If you saw that the model was reinforced for her behavior, you will be more motivated to copy her. This is known as vicarious reinforcement . On the other hand, if you observed the model being punished, you would be less motivated to copy her. This is called vicarious punishment . For example, imagine that four-year-old Allison watched her older sister Kaitlyn playing in their mother’s makeup, and then saw Kaitlyn get a time out when their mother came in. After their mother left the room, Allison was tempted to play in the make-up, but she did not want to get a time-out from her mother. What do you think she did? Once you actually demonstrate the new behavior, the reinforcement you receive plays a part in whether or not you will repeat the behavior.

Bandura researched modeling behavior, particularly children’s modeling of adults’ aggressive and violent behaviors (Bandura, Ross, & Ross, 1961). He conducted an experiment with a five-foot inflatable doll that he called a Bobo doll. In the experiment, children’s aggressive behavior was influenced by whether the teacher was punished for her behavior. In one scenario, a teacher acted aggressively with the doll, hitting, throwing, and even punching the doll, while a child watched. There were two types of responses by the children to the teacher’s behavior. When the teacher was punished for her bad behavior, the children decreased their tendency to act as she had. When the teacher was praised or ignored (and not punished for her behavior), the children imitated what she did, and even what she said. They punched, kicked, and yelled at the doll.

Watch the following to see a portion of the famous Bobo doll experiment, including an interview with Albert Bandura.

What are the implications of this study? Bandura concluded that we watch and learn, and that this learning can have both prosocial and antisocial effects. Prosocial (positive) models can be used to encourage socially acceptable behavior. Parents in particular should take note of this finding. If you want your children to read, then read to them. Let them see you reading. Keep books in your home. Talk about your favorite books. If you want your children to be healthy, then let them see you eat right and exercise, and spend time engaging in physical fitness activities together. The same holds true for qualities like kindness, courtesy, and honesty. The main idea is that children observe and learn from their parents, even their parents’ morals, so be consistent and toss out the old adage “Do as I say, not as I do,” because children tend to copy what you do instead of what you say. Besides parents, many public figures, such as Martin Luther King, Jr. and Mahatma Gandhi, are viewed as prosocial models who are able to inspire global social change. Can you think of someone who has been a prosocial model in your life?

The antisocial effects of observational learning are also worth mentioning. As you saw from the example of Claire at the beginning of this section, her daughter viewed Claire’s aggressive behavior and copied it. Research suggests that this may help to explain why abused children often grow up to be abusers themselves (Murrell, Christoff, & Henning, 2007). In fact, about 30% of abused children become abusive parents (U.S. Department of Health & Human Services, 2013). We tend to do what we know. Abused children, who grow up witnessing their parents deal with anger and frustration through violent and aggressive acts, often learn to behave in that manner themselves. Sadly, it’s a vicious cycle that’s difficult to break.

Some studies suggest that violent television shows, movies, and video games may also have antisocial effects (Figure 20) although further research needs to be done to understand the correlational and causational aspects of media violence and behavior. Some studies have found a link between viewing violence and aggression seen in children (Anderson & Gentile, 2008; Kirsch, 2010; Miller, Grabell, Thomas, Bermann, & Graham-Bermann, 2012). These findings may not be surprising, given that a child graduating from high school has been exposed to around 200,000 violent acts including murder, robbery, torture, bombings, beatings, and rape through various forms of media (Huston et al., 1992). Not only might viewing media violence affect aggressive behavior by teaching people to act that way in real life situations, but it has also been suggested that repeated exposure to violent acts also desensitizes people to it. Psychologists are working to understand this dynamic.

Putting It Together: Learning

In this chapter, you learned to

• explain learning and the process of classical conditioning
• explain operant conditioning, reinforcement, and punishment
• describe latent learning and observational learning

Are you superstitious? If so, you are definitely not alone. There are quite a few famous athletes who have reported a long list of superstitious behaviors (see DeLessio (2015)). Michael Jordan wore his University of North Carolina basketball shorts under his Chicago Bulls uniform, tennis superstar Serena Williams is known to bounce the ball five times before her first serve and two times before her second, basketballer Kevin Garnett (and many others since him) insist on eating peanut butter and jelly sandwiches before games. How might these behaviors be linked to the concepts you learned about conditioning in this module?

Curiously, even B.F. Skinner began to see signs of superstitious behavior in pigeons during his experiments. Pigeons, like humans, associate rewards with superstitious rituals when they see positive results. When pigeons looked over their left shoulder (operant conditioning), they were hopeful that a reward would come, just as an athlete who wears the same lucky socks comes to associate the special socks with superior performance.

Research into superstition has shown that, even if the behaviors seem silly, they can be effective in improving performance, most likely due to the increased confidence and security people feel when they engage in these rituals.

You can  view the transcript for “Superstitious Behavior – Pidgin Reward” here (opens in new window) .

Hopefully, you can continue to see and find examples of all types of conditioning in your life. From classically conditioned food aversions, operantly conditioned rewards, or surprising latent learning, there are applications of learning all around you.

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unlearned, automatic response by an organism to a stimulus in the environment

unlearned knowledge, involving complex patterns of behavior; instincts are thought to be more prevalent in lower animals than in humans

durable change in behavior or knowledge that is the result of experience

form of learning that involves connecting certain stimuli or events that occur together in the environment (classical and operant conditioning)

learning in which the stimulus or experience occurs before the behavior and then gets paired or associated with the behavior

stimulus that elicits a response due to its being paired with an unconditioned stimulus

natural (unlearned) behavior to a given stimulus

stimulus that does not initially elicit a response

response caused by the conditioned stimulus

(also, second-order conditioning) using a conditioned stimulus to condition a neutral stimulus

period of initial learning in classical conditioning in which a human or an animal begins to connect a neutral stimulus and an unconditioned stimulus so that the neutral stimulus will begin to elicit the conditioned response

decrease in the conditioned response when the unconditioned stimulus is no longer paired with the conditioned stimulus

return of a previously extinguished conditioned response

ability to respond differently to similar stimuli

demonstrating the conditioned response to stimuli that are similar to the conditioned stimulus

learning not to respond to a stimulus that is presented repeatedly without change

form of learning in which the stimulus/experience happens after the behavior is demonstrated

behavior that is followed by consequences satisfying to the organism will be repeated and behaviors that are followed by unpleasant consequences will be discouraged

implementation of a consequence in order to increase a behavior

implementation of a consequence in order to decrease a behavior

adding a desirable stimulus to increase a behavior

taking away an undesirable stimulus to increase a behavior

adding an undesirable stimulus to stop or decrease a behavior

taking away a pleasant stimulus to decrease or stop a behavior

rewarding successive approximations toward a target behavior

has innate reinforcing qualities (e.g., food, water, shelter, sex)

has no inherent value unto itself and only has reinforcing qualities when linked with something else (e.g., money, gold stars, poker chips)

rewarding a behavior every time it occurs

rewarding behavior only some of the time

behavior is rewarded after a set amount of time

behavior is rewarded after unpredictable amounts of time have passed

set number of responses must occur before a behavior is rewarded

number of responses differ before a behavior is rewarded

learning that occurs, but it may not be evident until there is a reason to demonstrate it

mental picture of the layout of the environment

type of learning that occurs by watching others

process where the observer sees the model rewarded, making the observer more likely to imitate the model’s behavior

process where the observer sees the model punished, making the observer less likely to imitate the model’s behavior

Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The Psychology of Learning: Theories & Types Explained

Have you ever wondered why you remember the lyrics to a song from 10 years ago but can’t recall where you left your keys this morning? Welcome to the fascinating world of the psychology of learning ! In this post, we’ll journey through the complex pathways of how we learn, with a dash of humor and some fun tidbits to keep you entertained. From Pavlov’s drooling dogs to the modern-day mysteries of neuroscience, we’re about to explore how our minds soak up knowledge like sponges—sometimes, selectively. Let’s get started!

Table of Contents

What is the psychology of learning.

Before we get into the specifics, let’s establish a common understanding of what learning in psychology means . It’s not just about hitting the books or taking exams; it’s about how our brains adapt, grow, and change based on our experiences. Think of it as your brain’s own version of a software update, always working to improve its performance (hopefully fixing those annoying bugs, like forgetting where the keys are).

In the psychological sense, learning is about changing behaviors, acquiring new skills , and adapting to new information. Picture your brain as a supercomputer constantly rewiring to become faster and more efficient.

Personal Learning Impact : A study by the American Psychological Association found that  87%  of “personal learners” say that learning helped them feel more capable and well-rounded. Additionally,  69%  say it opened up new perspectives about their lives,  64%  say it helped them make new friends, and  58%  say it made them feel more connected to their local community.

Every new experience, piece of information, and skill you acquire contributes to this ongoing upgrade. Whether you learn to ride a bike, master a new language, or remember not to touch a hot stove, your brain continually evolves.

What are the three elements of learning in psychology?

• Definition : The initial stage of learning is when a response is established.
• Explanation : During acquisition, individuals are introduced to new information or skills. This is when they first begin to learn and understand the material. For example, when you start learning to ride a bike, the acquisition is when you first get on the bike, understand how to balance, and learn to pedal.
• Definition : The process of maintaining the acquired knowledge or skills over time.
• Explanation : Retention involves storing the learned information in long-term memory for future use. This is about ensuring that what has been learned is not forgotten. Continuing with the bike example, retention happens after you have practiced riding for several days or weeks, and the knowledge of how to ride a bike becomes ingrained in your memory.
• Definition : The ability to retrieve and apply the learned information when needed.
• Explanation : This phase involves accessing and using the learned information or skills. It’s the demonstration of learning through performance. For instance, recall/performance is when you confidently ride a bike after not having ridden one for a while, showing that you can still remember and apply the skill you learned.

History of the Psychology of Learning

The history of learning theories reads like a who’s who of psychological thought. From Pavlov and his drooling dogs to Skinner’s pecking pigeons , the journey of understanding how we learn has been quite the roller coaster. So, buckle up as we explore some of the most groundbreaking discoveries in the field.

Ivan Pavlov kicked things off in the early 20th century with his experiments on classical conditioning. He found that dogs could learn to associate the sound of a bell with food, leading them to salivate even when no food was present. This revolutionary discovery opened the door to understanding how associations form.

Next up, B.F. Skinner introduced the world to operant conditioning. Through his work with pigeons and rats, Skinner demonstrated that rewards and punishments could shape behavior. His findings laid the foundation for much of modern behaviorism.

Finally, Albert Bandura brought observational learning into the spotlight. His famous Bobo doll experiment showed that children could learn new behaviors simply by watching others. This highlighted the importance of social influences on learning, a relevant concept today.

Types of Learning in Psychology

Alright, let’s explain the different types of learning. Think of these as the various flavors in the ice cream shop of knowledge.

Classical Conditioning

Remember Pavlov and his dogs? Classical conditioning is all about making associations. If you’ve ever cringed at the sound of your alarm clock because it means waking up, you’ve experienced classical conditioning. It’s your brain playing matchmaker between two seemingly unrelated things.

For instance, hearing the Jaws theme music might instantly make you feel uneasy, even if you’re nowhere near the ocean. Your brain says, “Hey, remember the scary shark?”

Operant Conditioning

B.F. Skinner is the guy who figured out that rewards and punishments can shape behavior. Operant conditioning is why you might treat yourself to ice cream after a gym session (reward) or avoid touching a hot stove after getting burned once (punishment). It’s all about consequences, baby! If you’ve ever trained a pet or bribed a child with candy for good behavior, you’ve dabbled in operant conditioning.

It’s about reinforcing the behaviors you want to see more of and discouraging the ones you don’t.

Observational Learning

Have you ever caught yourself imitating someone else, like copying a dance move or mimicking an accent? That’s observational learning in action. Albert Bandura and his Bobo doll experiment , we know we can learn just by watching others. So, go ahead and blame your quirky dance moves on observational learning!

When you see someone else succeed or be rewarded for a behavior, you’re likelier to try it yourself. This is why role models and mentors can be so influential.

Learning Theories in Psychology

Now that we have learned the basics, let’s explore the advanced theories that elucidate the learning process.

Behavioral Learning Theories

Behavioral theories are like the old-school approach to learning, focusing on observable behaviors.

Learning Through Association (Classical Conditioning)

Remember Pavlov? Yep, he’s back. This type of learning is all about associating one stimulus with another, like associating the sound of a bell with the arrival of tasty food. Your brain loves making connections, so certain smells can instantly transport you back to your grandma’s kitchen, or a particular song can remind you of a high school dance.

Learning Through Reinforcement (Operant Conditioning)

Skinner’s time to shine again! Here, learning happens through reinforcement—doing more of what gets rewarded and less of what gets punished. Simple yet effective. Think of it as a game of trial and error where your brain constantly determines what actions lead to the best outcomes. This is why sticker charts work wonders for kids and why we’re all a bit like pigeons in a Skinner box, pressing levers for our rewards.

Cognitive Learning Theories

Think of cognitive theories as the Sherlock Holmes of learning theories—they dig deep into how we process information. It’s about the brain’s internal workings, like a computer processing data.

Cognitive theories emphasize the importance of mental functions such as memory, perception, and problem-solving. They help us understand why cramming for a test might not be as effective as spaced repetition and why some people are visual learners while others prefer hands-on experiences.

Constructivist Learning Theories

Constructivist theories are like building blocks of knowledge. They propose that the most effective way to learn is to construct our own understanding of the world through experiences and reflection on them. It’s like LEGO for the brain!

According to this theory , learning is an active, constructive process. When you encounter new information, you don’t just passively absorb it; you integrate it with what you already know, building a more complex and nuanced understanding of the world.

Social Learning Theories

Albert Bandura , our observational learning guru, takes the stage here. Social learning theories emphasize that we learn by watching others and modeling their behavior. It’s why you might pick up phrases from your favorite TV show or adopt habits from friends.

Basic Principles of Social Learning Theory

• Attention : You must notice the behavior. If you don’t, you won’t learn much.
• Retention : You need to remember what you observed. This involves storing information in your memory for later retrieval.
• Reproduction : You have to be able to replicate the behavior. It’s one thing to watch a pro skateboarder; it’s another to pull off those tricks yourself.
• Motivation : You need a reason to imitate the behavior. If there’s no incentive, why bother?

Experiential Learning Theories

Experiential learning is all about learning through doing. That’s why internships, hands-on workshops, and real-world experiences are valuable. You learn best when you’re actively involved in the learning process.

Imagine learning how to ride a bike by reading a book about it—it’s not nearly as effective as hopping on a bike and giving it a go. Experiential learning emphasizes the importance of direct experience and reflection.

Modern Views

Modern views on learning incorporate everything from neuroscience to technology. Today, we understand that learning is a complex process influenced by countless factors, from genetics to environment. It’s a multi-faceted gem of a field!

Advances in brain imaging and cognitive science have given us deeper insights into how learning happens at a neurological level. The digital age has transformed how we access and engage with information, making lifelong learning more accessible.

Learning is an incredible journey that spans our lives, shaping who we are and how we interact with the world. From Pavlov and Skinner’s early experiments to the modern insights provided by cognitive and social learning theories, we’ve come a long way in understanding the intricate processes that drive our learning ability.

Whether through classical conditioning, where our brains play matchmaker between stimuli, or operant conditioning, which teaches us through rewards and punishments, each type of learning adds a unique piece to the puzzle. Observational learning reminds us of the power of role models, while cognitive, constructivist, and experiential theories highlight the active, hands-on nature of true understanding.

In our fast-paced, ever-changing world, modern views on learning integrate everything from neuroscience to digital technology, emphasizing that learning is a dynamic, multifaceted process influenced by many factors.

So, as you guide your daily experiences, remember that every moment is an opportunity to learn something new. Embrace the quirks of your memory, the thrill of discovery, and the endless possibilities of being an ever-evolving learner. And next time you forget where your keys are, just laugh it off and appreciate the complex, fascinating journey that is the psychology of learning.

Read Also: 25 Psychological Manipulation Techniques

What is meant by the psychology of learning?

The psychology of learning studies how people acquire, process, and retain knowledge and skills, focusing on mental processes and behavioral changes.

What is the psychology theory of learning?

It encompasses theories explaining learning, including behavioral (classical and operant conditioning), cognitive, constructivist, and social learning theories.

What is the general psychology of learning?

It involves understanding the principles and processes of learning, including perception, processing, and storage of information, as well as factors like motivation and emotions.

What are the 4 main learning styles?

Visual Learners : Learn through images and spatial understanding. Auditory Learners : Learn through listening. Reading/Writing Learners : Learn through reading and writing. Kinesthetic Learners : Learn through hands-on activities.

What are the 3 main types of learning?

Classical Conditioning : Learning through association. Operant Conditioning : Learning through consequences. Observational Learning : Learning by watching others.

What are the major factors affecting learning?

Motivation : The drive and desire to learn. Environment : The physical and social context in which learning occurs. Prior Knowledge : Existing knowledge and experiences that influence new learning. Reinforcement : Rewards and punishments that shape behavior. Cognitive Processes : Mental activities such as attention, perception, and memory.

If you want to read more articles similar to  The Psychology of Learning: Theories & Types Explained , we recommend that you enter our  Psychology  category.

I'm Waqar, a passionate psychologist and dedicated content writer. With a deep interest in understanding human behavior, I aim to share insights and knowledge in the field of psychology through this blog. Feel free to reach out for collaborations, queries, or discussions. Let's dig into the fascinating world of psychology together!

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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What Is the Psychology of Learning?

Learning in psychology is based on a person's experiences

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

 James Lacy, MLS, is a fact-checker and researcher.

The psychology of learning focuses on a range of topics related to how people learn and interact with their environments.

Are you preparing for a big test in your psychology of learning class? Or are you just interested in a review of learning and behavioral psychology topics? This learning study guide offers a brief overview of some of the major learning issues including behaviorism, classical, and operant conditioning .

Let's learn a bit more about the psychology of learning.

Definition of Learning in Psychology

Learning can be defined in many ways, but most psychologists would agree that it is a relatively permanent change in behavior that results from experience. During the first half of the 20th century, the school of thought known as behaviorism rose to dominate psychology and sought to explain the learning process. Behaviorism sought to measure only observable behaviors.

3 Types of Learning in Psychology

Behavioral learning falls into three general categories.

Classical Conditioning

Classical conditioning is a learning process in which an association is made between a previously neutral stimulus and a stimulus that naturally evokes a response.

For example, in Pavlov's classic experiment , the smell of food was the naturally occurring stimulus that was paired with the previously neutral ringing of the bell. Once an association had been made between the two, the sound of the bell alone could lead to a response.

For example, if you don't know how to swim and were to fall into a pool, you'd take actions to avoid the pool.

Operant Conditioning

Operant conditioning is a learning process in which the probability of a response occurring is increased or decreased due to reinforcement or punishment. First studied by Edward Thorndike and later by B.F. Skinner , the underlying idea behind operant conditioning is that the consequences of our actions shape voluntary behavior.

Skinner described how reinforcement could lead to increases in behaviors where punishment would result in decreases. He also found that the timing of when reinforcements were delivered influenced how quickly a behavior was learned and how strong the response would be. The timing and rate of reinforcement are known as schedules of reinforcement .

For example, your child might learn to complete their homework because you reward them with treats and/or praise.

Observational Learning

Observational learning is a process in which learning occurs through observing and imitating others. Albert Bandura's social learning theory suggests that in addition to learning through conditioning, people also learn through observing and imitating the actions of others.

Basic Principles of Social Learning Theory

As demonstrated in his classic Bobo Doll experiments, people will imitate the actions of others without direct reinforcement. Four important elements are essential for effective observational learning: attention, motor skills, motivation, and memory.

For example, a teen's older sibling gets a speeding ticket, with the unpleasant results of fines and restrictions. The teen then learns not to speed when they take up driving.

The three types of learning in psychology are classical conditioning, operant conditioning, and observational learning.

History of the Psychology of Learning

One of the first thinkers to study how learning influences behavior was psychologist John B. Watson , who suggested in his seminal 1913 paper Psychology as the Behaviorist Views It that all behaviors are a result of the learning process. Psychology, the behaviorists believed, should be the scientific study of observable, measurable behavior. Watson's work included the famous Little Albert experiment in which he conditioned a small child to fear a white rat.

Behaviorism dominated psychology for much of the early 20th century. Although behavioral approaches remain important today, the latter part of the century was marked by the emergence of humanistic psychology, biological psychology, and cognitive psychology .

Other important figures in the psychology of learning include:

• Edward Thorndike
• Ivan Pavlov
• B.F. Skinner
• Albert Bandura

A Word From Verywell

The psychology of learning encompasses a vast body of research that generally focuses on classical conditioning, operant conditioning, and observational learning. As the field evolves, it continues to have important implications for explaining and motivating human behavior.

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

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• Review Article
• Open access
• Published: 12 January 2017

Individual differences in the learning potential of human beings

• Elsbeth Stern 1  

npj Science of Learning volume  2 , Article number:  2 ( 2017 ) Cite this article

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• Human behaviour

To the best of our knowledge, the genetic foundations that guide human brain development have not changed fundamentally during the past 50,000 years. However, because of their cognitive potential, humans have changed the world tremendously in the past centuries. They have invented technical devices, institutions that regulate cooperation and competition, and symbol systems, such as script and mathematics, that serve as reasoning tools. The exceptional learning ability of humans allows newborns to adapt to the world they are born into; however, there are tremendous individual differences in learning ability among humans that become obvious in school at the latest. Cognitive psychology has developed models of memory and information processing that attempt to explain how humans learn (general perspective), while the variation among individuals (differential perspective) has been the focus of psychometric intelligence research. Although both lines of research have been proceeding independently, they increasingly converge, as both investigate the concepts of working memory and knowledge construction. This review begins with presenting state-of-the-art research on human information processing and its potential in academic learning. Then, a brief overview of the history of psychometric intelligence research is combined with presenting recent work on the role of intelligence in modern societies and on the nature-nurture debate. Finally, promising approaches to integrating the general and differential perspective will be discussed in the conclusion of this review.

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Uniquely human intelligence arose from expanded information capacity

Genetic variation, brain, and intelligence differences

Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training

Human learning and information processing.

In psychology textbooks, learning is commonly understood as the long-term change in mental representations and behavior as a result of experience. 1 As shown by the four criteria, learning is more than just a temporary use of information or a singular adaption to a particular situation. Rather, learning is associated with changes in mental representations that can manifest themselves in behavioral changes. Mental and behavioral changes that result from learning must be differentiated from changes that originate from internal processes, such as maturation or illness. Learning rather occurs as an interaction with the environment and is initiated to adapt personal needs to the external world.

From an evolutionary perspective, 2 living beings are born into a world in which they are continuously expected to accomplish tasks (e.g., getting food, avoiding threats, mating) to survive as individuals and as species. The brains of all types of living beings are equipped with instincts that facilitate coping with the demands of the environment to which their species has been adapted. However, because environments are variable, brains have to be flexible enough to optimize their adaptation by building new associations between various stimuli or between stimuli and responses. In the case of classical conditioning, one stimulus signals the occurrence of another stimulus and thereby allows for the anticipation of a positive or negative consequence. In the case of operant conditioning, behavior is modified by its consequence. Human beings constantly react and adapt to their environment by learning through conditioning, frequently unconsciously. 1

However, there is more to human learning than conditioning, which to the best of our knowledge, makes us different from other species. All living beings must learn how to obtain access to food in their environment, but only human beings cook and have invented numerous ways to store and conserve their food. While many animals run faster than humans and are better climbers, the construction and use of vehicles or ladders is unique to humans. There is occasional evidence of tool use among non-human species passed on to the next generation, 3 , 4 but this does not compare to the tools humans have developed that have helped them to change the world. The transition from using stonewedges for hunting to inventing wheels, cars, and iPhones within a time period of a few thousand years is a testament to the unique mental flexibility of human beings given that, to the best of our knowledge, the genes that guide human brain development have not undergone remarkable changes during the last 50,000 years. 5 This means that as a species, humans are genetically adapted to accomplish requirements of the world as it existed at approximately 48,000 BC. What is so special about human information processing? Answers to this question are usually related to the unique resource of consciousness and symbolic reasoning abilities that are, first and foremost, practiced in language. Working from here, a remarkable number of insights on human cognition have been compiled in the past decades, which now allow for a more comprehensive view of human learning.

Human learning from a general cognitive perspective

Learning manifests itself in knowledge representations processed in memory. The encoding, storage, and retrieval of information have been modeled in the multi-store model of human memory depicted in Fig.  1 . 6 Sensory memory is the earliest stage of processing the large amount of continuously incoming information from sight, hearing, and other senses. To allow goal-directed behavior and selective attention, only a fractional amount of this information passes into the working memory, which is responsible for temporarily maintaining and manipulating information during cognitive activity. 7 , 8 Working memory allows for the control of attention and thereby enables goal-directed and conscious information processing. It is the gatekeeper to long-term memory, which is assumed to have an unlimited capacity. Here, information acquired through experience and learning can be stored in different modalities as well as in symbol systems (e.g., language, script, mathematical notation systems, pictorials, music prints).

A model of human information processing, developed together with Dr. Lennart Schalk

The multi-store model of human information processing is not a one-way street, and long-term memory is not to be considered a storage room or a hard-disk where information remains unaltered once it has been deposited. A more appropriate model of long-term memory is a self-organizing network, in which verbal terms, images, or procedures are represented as interlinked nodes with varying associative strength. 9 Working memory regulates the interaction between incoming information from sensory memory and knowledge activated from long-term memory. Very strong incoming stimuli (e.g., a loud noise or a harsh light), which may signal danger, can interrupt working memory activities. For the most part, however, working memory filters out irrelevant and distracting information to ensure that the necessary goals will be achieved undisturbed. This means that working memory is continuously selecting incoming information, aligning it with knowledge retrieved from long-term memory, and preparing responses to accomplishing requirements demanded by the environment or self-set goals. Inappropriate and unsuitable information intruding from sensory as well as from long-term memory has to be inhibited, while appropriate and suitable information from both sources has to be updated. 8 The strength with which a person pursues a particular goal has an impact on the degree of inhibitory control. In case of intentional learning, working memory guards more against irrelevant information than in the case of mind wandering. Less inhibitory control makes unplanned and unintended learning possible (i.e., incidental learning).

These working memory activities are permanently changing the knowledge represented in long-term memory by adding new nodes and by altering the associative strength between them. The different formats knowledge can be represented in are listed in Fig.  1 ; some of them are more closely related to sensory input and others to abstract symbolic representations. In cognitive psychology, learning is associated with modifications of knowledge representations that allow for better use of available working memory resources. Procedural knowledge (knowing how) enables actions and is based on a production-rule system. As a consequence of repeated practice, the associations between these production rules are strengthened and will eventually result in a coordinated series of actions that can activate each other automatically with a minimum or no amount of working memory resources. This learning process not only allows for carrying out the tasks that the procedural knowledge is tailored to perform more efficiently, but also frees working memory resources that can be used for processing additional information in parallel. 10 , 11 , 12

Meaningful learning requires the construction of declarative knowledge (knowing that), which is represented in symbol systems (language, script, mathematical, or visual-spatial representations). Learning leads to the regrouping of declarative knowledge, for instance by chunking multiple unrelated pieces of knowledge into a few meaningful units. Reproducing the orally presented number series “91119893101990” is beyond working memory capacity, unless one detects two important dates of German history: the day of the fall of the Berlin Wall: 9 November 1989 and the day of reunification: 3 October 1990. Individuals who have stored both dates and can retrieve them from long-term memory are able to chunk 14 single units into two units, thereby freeing working memory resources. Memory artists, who can reproduce dozens of orally presented numbers have built a very complex knowledge base that allows for the chunking of incoming information. 13

Learning also manifests itself in the extension of declarative knowledge using concept formation and inferential reasoning. Connecting the three concepts of “animal, produce, milk” forms a basic concept of cow. Often, concepts are hierarchically related with superordinate (e.g., animal) and subordinate (e.g., cow, wombat) ordering. This provides the basis for creating meaningful knowledge by deductive reasoning. If the only thing a person knows about a wombat is that it is an animal, she can nonetheless infer that it needs food and oxygen. Depending on individual learning histories, conceptual representations can contain great variations. A farmer’s or a veterinarian’s concept of a cow is connected to many more concepts than “animal, produce, milk” and is integrated into a broader network of animals. In most farmers’ long-term memory, “cow” might be strongly connected to “pig”, while veterinarians should have particularly strong links to other ruminants. A person’s conceptual network decisively determines the selection and representation of incoming information, and it determines the profile of expertise. For many academic fields, first and foremost in the STEM area (Science, Technology, Engineering, Mathematics), it has been demonstrated that experts and novices who use the same words may have entirely different representations of their meaning. This has been convincingly demonstrated for physics and particularly in the area of mechanics. 14 Children can be considered universal novices; 15 therefore, their everyday concepts are predominantly based on characteristic features while educated adults usually consider defining features, 16 , 17 , 18 as the example of “island” demonstrates. For younger children, it primarily refers to a warm place where one can spend ones’ holidays. In contrast, adults’ concept of island does refer to a tract of land that is completely surrounded by water but not large enough to be considered a continent.

The shift from characteristic to defining features is termed “conceptual change”, 16 and promoting this kind of learning is a major challenge for school education. Students’ understanding of central concepts in an academic subject can undergo fundamental changes (e.g., the concept of weight in physics). Younger elementary school children often agree that a pile of rice has weight, but they may also deny that an individual grain of rice has weight at all. This apparently implausible answer is understandable given that younger children consider the concepts of “weight” and “being heavy” as equivalent. As such, children tend to agree that a grain of rice has weight if it is put on an ant’s back. 16 As a consequence of their education, students usually understand that an object’s weight is determined with the assistance of scales and not necessarily by personal sensation. However, representing weight as the property of an object is still not compatible with scientific physics in the Newtonian sense by which weight is conceptualized as a relation between objects. Understanding weight in this sense requires an interrelated network of knowledge, including the concepts of force, gravity, and mass (among others).

As a result of classroom instruction, students are expected to acquire procedural and conceptual knowledge of the subjects they were taught. While procedures emerge as a function of repetition and practice, the acquisition of advanced concepts, which are consistent with state of the art science, is less straightforward. 14 , 19 To support this kind of conceptual learning, insights from cognitive learning research have been integrated into educational research and are increasingly informing classroom practice. Several instructional methods have been developed and evaluated that support students in restructuring and refining their knowledge and thereby promote appropriate conceptual understanding, including self-explanations, 20 contrasting cases, 21 , 22 and metacognitive questions. 23 Cognitive research has also informed the development of the “taxonomy of learning objects”. 24 This instrument is widely employed for curriculum development and in teacher training programs to support the alignment of content-specific learning goals, means of classroom practice, and assessment. The taxonomy acknowledges the distinction between procedural and conceptual knowledge and includes six cognitive processes (listed in Fig.  1 ) that describe how knowledge can be transformed into observable achievement.

How core knowledge innate to humans can meet with academic learning

What makes humans efficient learners, however, goes beyond general memory functions discussed so far. Similar to other living beings, humans do not enter the world as empty slates 2 but are equipped with so-called core knowledge (Fig.  1 ). Evidence for core knowledge comes from preferential looking experiments with infants who are first habituated to a particular stimulus or scenario. Then, the infant is shown a second scenario that differs from the first in a specific manner. If the time he or she looks at this stimulus exceeds the looking-time at the end of the habituation phase of the first stimulus, this suggests that the infant can discriminate between the stimuli. This paradigm helps to determine whether infants detect violations of principles that underlie the physical world, such as the solidity of objects, where an object cannot occupy the same space as another object. 25 , 26 Core knowledge, which allows privileged learning and behavioral functioning with little effort, also guides the unique human ability of symbolic communication and reasoning, first and foremost, langue learning. 27 , 28 It is uncontested that humans are born with capacities for language learning, which includes the awareness of phonological, grammatical, and social aspects of language. 4 , 29 , 30

Core knowledge can serve as a starting point for the acquisition of content knowledge that has emerged as a result of cultural development. This has been examined in detail for numerical and mathematical reasoning. Two core systems have been detected in infants. As early as at 6 months of age, infants show an ability for the approximate representations of numerical magnitude, which allow them to discriminate two magnitudes depending on their ratio. 31 At the same age, the system of precise representations of distinct individuals allows infants to keep track of changes in small sets of up to three elements. 32 Mathematical competencies emerge as a result of combining both core systems and linking them to number words provided by the respective culture. 33 The Arabic place value number system, which is now common in most parts of the world, was only developed a few 100 years ago. Only after the number “0” had made its way from India via the Arabic countries to Europe were the preconditions for developing our decimal system available. 34 The Arabic number system opened up the pathway to academic mathematics. Cultural transformations based on invented symbol systems were the key to advanced mathematics. Today’s children are expected to understand concepts within a few years of schooling that took mankind centennials to develop. Central content areas in mathematics curricula of high schools, such as calculus, were only developed less than three centuries ago. 35 Given the differences between the Arabic and the Roman number systems, children born 2000 years ago could not make use of their numerical core knowledge in the same way today’s children can.

Core knowledge about navigation is meant to guide the acquisition of geometry, an area involved in numerous academic fields. 36 , 37 The cornerstone of cultural development was the invention of writing, in which language is expressed by letters or other marks. Script is a rather recent cultural invention, going back approximately 5,000 years, whereas the human genome emerged approximately 50,000 years ago. 38 Clearly, unlike oral language, humans are not directly prepared for writing and reading. Nonetheless, today, most 6-year-old children become literate during their 1st years of schooling without experiencing major obstacles. Human beings are endowed with the many skills that contribute to the ability to write and read, such as, first and foremost, language as well as auditory and visual perception and drawing. These initially independent working resources were coopted when script was invented, and teaching children to write and read at school predominantly means supporting the development of associations among these resources. 39

Part of the core knowledge innate to humans has also been found in animals, for instance numerical knowledge and geometry, but to the best of our knowledge, no other animals have invented mathematics. 40 Only humans have been able to use core knowledge for developing higher order cognition, which serves as a precondition for culture, technology, and civilization. Additionally, the unique function of human working memory is the precondition for the integration of initially independent representational systems. However, the full potential of working memory is not in place at birth, but rather matures during childhood and undergoes changes until puberty. 41 Children under the age of two are unable to switch goals 42 and memorize symbol representations appropriately. 43

To summarize what has been discussed so far, there are two sources for the exceptional learning capacity of humans. The first is the function of working memory as a general-purpose resource that allows for holding several mental representations simultaneously for further manipulation. The second is the ancient corpus of the modularized core knowledge of space, quantities, and the physical and social world. Working memory allows for the connection of this knowledge to language, numerals, and other symbol systems, which provides the basis for reasoning and the acquisition of knowledge in academic domains, if appropriate learning opportunities are provided. Both resources are innate to human beings, but they are also sources of individual differences, as will be discussed in the following sections.

Learning potentials are not alike among humans: the differential perspective

In the early twentieth century, a pragmatic need for predicting the learning potential of individuals initiated the development of standardized tests. The Frenchman Alfred Binet, who held a degree in law, constructed problems designed to determine whether children who did not meet certain school requirements suffered from mental retardation or from behavioral disturbances. 44 He asked questions that still resemble items in today’s intelligence tests; children had to repeat simple sentences and series of digits forwards and backwards as well as define words such as “house” or “money”. They were asked in what respect a fly, an ant, a butterfly and a flea are alike, and they had to reproduce drawings from memory. William Stern, an early professor of psychology at the newly founded University of Hamburg/Germany, intended to quantify individual differences in intelligence during childhood and adolescence by developing the first formula for the intelligence quotient (IQ): 45 IQ = Mental age/chronological age*100. Mental age refers to the average test score for a particular age group; this means that a 6-year-old child would have an IQ = 133 if their test score was equivalent to the mean score achieved in the group of 8-year-olds. From adolescence on, however, the average mental age scores increasingly converge, and because of the linear increase in chronological age, the IQ would decline—a trend that obviously does not match reality.

Psychologists from the United States, specifically headed by the Harvard and later Yale professor Robert Yerkes, decided to look at a person’s score relative to other people of the same age group. The average test score was assigned to an IQ = 100 by convention, and an individual’s actual score is compared to this value in terms of a standard deviation, an approach that has been retained to this day. World War I pushed the development of non-verbal intelligence tests, which were used to select young male immigrants with poor English language skills for military service. 46 In the UK, the educational psychologist Cyril Burt promoted the use of intelligence tests for assigning students to the higher academic school tracks. 47 Charles Spearman from the University College London was among the first to focus on the correlations between test items based on verbal, numerical, or visual-spatial content. 48 The substantial correlations he found provided evidence for a general intelligence model (factor-g), which has been confirmed in the following decades by numerous studies performed throughout the world. 49

The high psychometric quality of the intelligence tests constructed in different parts of the world by scientists in the early decades of the twentieth century have influenced research ever since. In 1923, Edward Boring, a leading experimental psychologist concluded, “Intelligence is what the tests test. This is a narrow definition, but it is the only point of departure for a rigorous discussion of the tests. It would be better if the psychologists could have used some other and more technical term, since the ordinary connotation of intelligence is much broader. The damage is done, however, and no harm need result if we but remember that measurable intelligence is simply what the tests of intelligence test, until further scientific observation allows us to extend the definition.”(ref. 50 , p. 37). More than 70 years later, psychologists widely agreed on a definition for intelligence originally offered by Linda Gottfredsonin 1997: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do” (ref. 51 , p. 13). This definition is in line with the substantial correlations between intelligence test scores and academic success, 52 whereas correlations with measures of outside-school success, such as income or professional status, are lower but still significant. 53 , 54 Numerous longitudinal studies have revealed that IQ is a fairly stable measure across the lifespan, which has been most convincingly demonstrated in the Lothian Birth Cohorts run in Scotland. Two groups of people born in 1921 and 1936 took a test of mental ability at school when they were 11 years old. The correlation with IQ tests taken more than 60 years later was highly significant and approached r  = .70 (ref. 55 ). The same data set also demonstrated a substantial long-term impact of intelligence on various factors of life success, among them career aspects, health, and longevity. 56

Intelligence tests scores have proven to be objective, reliable, and valid measures for predicting learning outcome and more general life success. At the same time, the numerous data sets on intelligence tests that were created all over the world also contributed to a better understanding of the underlying structure of cognitive abilities. Although a factor g could be extracted in almost all data sets, correlations between subtests varied considerably, suggesting individual differences beyond general cognitive capabilities. Modality factors (verbal, numerical, or visual spatial) have been observed, showing increased correlations between tests based on the same modality, but requiring different mental operations. On the other hand, increased correlations were also observed between tests based on different modalities, but similar mental operations (e.g., either memorizing or reasoning). The hierarchical structure of intelligence, with factor g on the top and specific factors beneath, was quite obvious from the very beginning of running statistical analyses with intelligence items. Nonetheless, it appeared a major challenge for intelligence researchers to agree on a taxonomy of abilities on the second and subsequent levels. In 1993, John Carroll published his synthesis of hundreds of published data sets on the structure of intelligence after decades of research. 57 In his suggested three-stratum model, factor g is the top layer, with the middle layer encompassing broader abilities such as comprehension knowledge, reasoning, quantitative knowledge, reading and writing, and visual and auditory processing. Eighty narrower abilities, such as spatial scanning, oral production fluency, and sound discrimination, are located in the bottom layer. To date, Carroll’s work is considered the most comprehensive view of the structure of individual variations in cognitive abilities. 58 However, the interpretation of factor g is still under discussion among scientists. Factor g could be a comprehensive characteristic of the brain that makes information processing generally more or less efficient (top-down-approach). Existing data sets, however, are also compatible with a model of intelligence according to which the human brain is comprised of a large number of single abilities that have to be sampled for mental work (bottom-up approach). In this case, factor g can be considered a statistical correlate that is an emerging synergy of narrow abilities. 59

Genetic sources of individual differences in intelligence

From studies with identical and fraternal twins, we know that genetic differences can explain a considerable amount of variance in IQ. The correlation between test scores of identical twins raised together approaches r  = .80 and thereby is almost equal to the reliability coefficient of the respective test. On the other hand, IQ-correlations between raised-together same-sex fraternal twins are rarely higher than .50, a value also found for regular siblings. Given that the shared environment for regular siblings is lower than for fraternal twins, this result qualifies the impact of environmental factors on intelligence. The amount of genetic variance is judged in statistical analyses based on the difference between the intra-pair correlations for identical and fraternal twins. 60 High rates of heritability, however, do not mean that we can gauge a person’s cognitive capabilities from his or her DNA. The search for the genes responsible for the expression of cognitive capabilities has not yet had much success, despite the money and effort invested in human genome projects. It is entirely plausible that intelligence is formed by a very large number of genes, each with a small effect, spread out across the entire genome. Moreover, these genes seem to interact in very complicated ways with each other as well as with environmental cues. 61

An entirely false but nonetheless still widespread misunderstanding is to equate “genetic sources” with “inevitability” because people fail to recognize the existence of reaction norms, a concept invented in 1909 by the German biologist, Richard Woltereck. Reaction norms depict the range of phenotypes a genotype can produce depending on the environment. 62 For some few physiological individual characteristics (e.g., the color of eyes) the reaction norm is quite narrow, which means gene expression will rarely be affected by varying environments. Other physiological characteristics, such as height, have a high degree of heritability and a large reaction norm. Whether an individual reaches the height made possible by the genome depends on the nutrition during childhood and adolescence. In a wealthy country with uniform access to food, average height will be larger than in a poor country with many malnourished inhabitants. However, within both countries, people vary in height. The heritability in the wealthy country can be expected to approach 100% because everybody enjoyed sufficient nutrition. In contrast, in the poor country, some were sufficiently nourished and, therefore, reached the height expressed by their genome, while others were malnourished and, therefore, remained smaller than their genes would have allowed under more favorable conditions. For height, the reaction norm is quite large because gene expression depends on nutrition during childhood and adolescence. This explains the well-documented tendency for people who have grown up in developed countries to become progressively taller in the past decades.

The environment regulates gene expression, which means that instead of “nature vs. nurture”, a more accurate phrase is “nature via nurture”. 63 The complex interaction between genes and environment can also explain the fact that heritability of intelligence increases during the lifespan. 61 This well-established finding is a result of societies in which a broad variety of cognitive activities available in professional and private life enable adults more than children to actively select special environments that fit their genes. People who have found their niche can perfect their competencies by deliberate learning.

In the first decades of developing intelligence tests, researchers were naive to the validity of non-verbal intelligence; so-called culture-free or culture-fair tests, based on visual-spatial material such as mirror images, mazes or series and matrices of geometric figures, were supposed to be suitable for studying people of different social and cultural levels. 64 This is now considered incorrect because in the meantime, there has been overwhelming evidence for the impact of schooling on the development of intelligence and the establishment and stabilization of individual differences. Approximately 10 years of institutionalized education is necessary for the intelligence of individuals to approach its maximum potential. 65 , 66 , 67

Altogether, twin and adoption studies suggest that 50–80% of IQ variation is due to genetic differences. 61 This relatively large range in the percentage across different studies is due to the heritability of intelligence in the population studied, specifically, the large reaction norm of the genes giving rise to the development of intelligence. Generally, the amount of variance in intelligence test scores explained by genes is higher the more society members have access to school education, health care, and sufficient nutrition. There is strong evidence for a decrease in the heritability of intelligence for children from families with lower socioeconomic status (SES). For example, lower SES fraternal twins resembled each other more than higher SES ones, indicating a stronger impact of shared environment under the former condition. 68 In other words, because of the less stimulating environment in lower SES families, the expression of genes involved in the development of intelligence is likely to be hampered. Although it may be counterintuitive at first, this suggests that a high heritability rate of intelligence in a society is an indicator of economic and educational equity. Additionally, this means that countries that ensure access to nutrition, health care, and high quality education independent of social background enable their members to develop their intelligence according to their genetic potential. This was confirmed by a meta-analysis on interactions between SES and heritability rate. While studies run in the United States showed a positive correlation between SES and heritability rate, studies from Western Europe countries and Australia with a higher degree of economic and social equality did not. 69 , 70

Cognitive processes behind intelligence test scores: how individuals differ in information processing

In the first part of this paper, cognitive processes were discussed that, in principle, enable human beings to develop the academic competencies that are particularly advantageous in our world today. In the second part, intelligence test scores were shown to be valid indicators of academic and professional success, and differences in IQ were shown to have sound genetic sources. Over many decades, research on cognitive processes and psychometric intelligence has been developing largely independently of one another, but in the meantime, they have converged. Tests that were developed to provide evidence for the different components of human cognition revealed large individual differences and were substantially correlated with intelligence tests. Tests of memory function were correlated with tests of factor g. Sensory memory tests have shown that the exposure duration required for reliably identifying a simple stimulus (inspection time) is negatively correlatedwith intelligence. 71 For working memory, there is a large body of research indicating substantial relationships between all types of working memory functions and IQ, with average correlations >.50 (refs 72 , 73 , 74 ). In these studies, working memory functions are measured by speed tasks that require goal-oriented active monitoring of incoming information or reactions under interfering and distracting conditions. Neural efficiency has been identified as a major neural characteristic of intelligence; more intelligent individuals show less brain activation (measured by electroencephalogram or functional magnetic resonance imaging) when completing intelligence test items 75 , 76 as well as working memory items. 77 Differences in information-processing efficiency were already found in 4-month-old children. Most importantly, they could predict psychometric intelligence in 8-year-old children. 78

These results clearly suggest that a portion of individual differences can be traced back to differences in domain-general cognitive competencies. However, psychometric research also shows that individual differences do exist beyond factor g on a more specific level. Differences in numerical, language, and spatial abilities are well established. Longitudinal studies starting in infancy suggest that sources of these differences may be traced back to variations in core knowledge. Non-symbolic numerical competencies in infancy have an impact on mathematical achievement. 79 Similar long-term effects were found for other areas of core knowledge, 80 particularly language. 81

Endowed with general and specific cognitive resources, human beings growing up in modern societies are exposed to informal and formal learning environments that foster the acquisition of procedural as well as declarative knowledge in areas that are part of the school curriculum. Being endowed with genes that support efficient working memory functions and that provide the basis for usable core knowledge allows for the exploitation of learning opportunities provided by the environment. This facilitates the acquisition of knowledge that is broad as well as deep enough to be prepared for mastering the, as of yet, unknown demands of the future. 18 Regression analyses based on longitudinal studies have revealed that the confounded variance of prior knowledge and intelligence predicts learning outcome and expertise better than each single variable. 82 , 83 , 84 Importantly, no matter how intelligent a person is, gaining expertise in a complex and sophisticated field requires deliberate practice and an immense investment of time. 85 However, intelligence differences will come into play in the amount of time that has to be invested to reach a certain degree of expertise. 86 Moreover, intelligence builds a barrier to content areas in which a person can excel. As discussed in the first part of this paper, some content areas—first and foremost from STEM fields—are characterized by abstract concepts mainly based on defining features, which are themselves integrated into a broader network of other abstract concepts and procedures. Only individuals who clearly score above average on intelligence tests can excel in these areas. 84 , 87 For individuals who were fortunate enough to attend schools that offered high-quality education, intelligence and measures of deep and broad knowledge are highly correlated. 88 , 89 A strong impact of general intelligence has also been shown for university entrance tests such as the SAT, which mainly ask for the application of knowledge in new fields. 90 , 91 Societies that provide uniform access to cognitively stimulating environments help individuals to achieve their potential but also bring to bear differences in intelligence. Education is not the great equalizer, but rather generates individual differences rooted in genes.

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33 What Is Learning?

[latexpage]

Learning Objectives

By the end of this section, you will be able to:

• Explain how learned behaviors are different from instincts and reflexes
• Define learning
• Recognize and define three basic forms of learning—classical conditioning, operant conditioning, and observational learning

Birds build nests and migrate as winter approaches. Infants suckle at their mother’s breast. Dogs shake water off wet fur. Salmon swim upstream to spawn, and spiders spin intricate webs. What do these seemingly unrelated behaviors have in common? They all are unlearned behaviors. Both instincts and reflexes are innate behaviors that organisms are born with. Reflexes are a motor or neural reaction to a specific stimulus in the environment. They tend to be simpler than instincts, involve the activity of specific body parts and systems (e.g., the knee-jerk reflex and the contraction of the pupil in bright light), and involve more primitive centers of the central nervous system (e.g., the spinal cord and the medulla). In contrast, instincts are innate behaviors that are triggered by a broader range of events, such as aging and the change of seasons. They are more complex patterns of behavior, involve movement of the organism as a whole (e.g., sexual activity and migration), and involve higher brain centers.

Both reflexes and instincts help an organism adapt to its environment and do not have to be learned. For example, every healthy human baby has a sucking reflex, present at birth. Babies are born knowing how to suck on a nipple, whether artificial (from a bottle) or human. Nobody teaches the baby to suck, just as no one teaches a sea turtle hatchling to move toward the ocean. Learning, like reflexes and instincts, allows an organism to adapt to its environment. But unlike instincts and reflexes, learned behaviors involve change and experience: learning is a relatively permanent change in behavior or knowledge that results from experience. In contrast to the innate behaviors discussed above, learning involves acquiring knowledge and skills through experience. Looking back at our surfing scenario, Julian will have to spend much more time training with his surfboard before he learns how to ride the waves like his father.

Learning to surf, as well as any complex learning process (e.g., learning about the discipline of psychology), involves a complex interaction of conscious and unconscious processes. Learning has traditionally been studied in terms of its simplest components—the associations our minds automatically make between events. Our minds have a natural tendency to connect events that occur closely together or in sequence. Associative learning occurs when an organism makes connections between stimuli or events that occur together in the environment. You will see that associative learning is central to all three basic learning processes discussed in this chapter; classical conditioning tends to involve unconscious processes, operant conditioning tends to involve conscious processes, and observational learning adds social and cognitive layers to all the basic associative processes, both conscious and unconscious. These learning processes will be discussed in detail later in the chapter, but it is helpful to have a brief overview of each as you begin to explore how learning is understood from a psychological perspective.

In classical conditioning, also known as Pavlovian conditioning, organisms learn to associate events—or stimuli—that repeatedly happen together. We experience this process throughout our daily lives. For example, you might see a flash of lightning in the sky during a storm and then hear a loud boom of thunder. The sound of the thunder naturally makes you jump (loud noises have that effect by reflex). Because lightning reliably predicts the impending boom of thunder, you may associate the two and jump when you see lightning. Psychological researchers study this associative process by focusing on what can be seen and measured—behaviors. Researchers ask if one stimulus triggers a reflex, can we train a different stimulus to trigger that same reflex? In operant conditioning, organisms learn, again, to associate events—a behavior and its consequence (reinforcement or punishment). A pleasant consequence encourages more of that behavior in the future, whereas a punishment deters the behavior. Imagine you are teaching your dog, Hodor, to sit. You tell Hodor to sit, and give him a treat when he does. After repeated experiences, Hodor begins to associate the act of sitting with receiving a treat. He learns that the consequence of sitting is that he gets a doggie biscuit ( [link] ). Conversely, if the dog is punished when exhibiting a behavior, it becomes conditioned to avoid that behavior (e.g., receiving a small shock when crossing the boundary of an invisible electric fence).

Observational learning extends the effective range of both classical and operant conditioning. In contrast to classical and operant conditioning, in which learning occurs only through direct experience, observational learning is the process of watching others and then imitating what they do. A lot of learning among humans and other animals comes from observational learning. To get an idea of the extra effective range that observational learning brings, consider Ben and his son Julian from the introduction. How might observation help Julian learn to surf, as opposed to learning by trial and error alone? By watching his father, he can imitate the moves that bring success and avoid the moves that lead to failure. Can you think of something you have learned how to do after watching someone else?

All of the approaches covered in this chapter are part of a particular tradition in psychology, called behaviorism, which we discuss in the next section. However, these approaches do not represent the entire study of learning. Separate traditions of learning have taken shape within different fields of psychology, such as memory and cognition, so you will find that other chapters will round out your understanding of the topic. Over time these traditions tend to converge. For example, in this chapter you will see how cognition has come to play a larger role in behaviorism, whose more extreme adherents once insisted that behaviors are triggered by the environment with no intervening thought.

Instincts and reflexes are innate behaviors—they occur naturally and do not involve learning. In contrast, learning is a change in behavior or knowledge that results from experience. There are three main types of learning: classical conditioning, operant conditioning, and observational learning. Both classical and operant conditioning are forms of associative learning where associations are made between events that occur together. Observational learning is just as it sounds: learning by observing others.

Review Questions

Which of the following is an example of a reflex that occurs at some point in the development of a human being?

• child riding a bike
• teen socializing
• infant sucking on a nipple
• toddler walking

Learning is best defined as a relatively permanent change in behavior that ________.

• occurs as a result of experience
• is found only in humans
• occurs by observing others

Two forms of associative learning are ________ and ________.

• classical conditioning; operant conditioning
• classical conditioning; Pavlovian conditioning
• operant conditioning; observational learning
• operant conditioning; learning conditioning

In ________ the stimulus or experience occurs before the behavior and then gets paired with the behavior.

• associative learning
• observational learning
• operant conditioning
• classical conditioning

Critical Thinking Questions

Compare and contrast classical and operant conditioning. How are they alike? How do they differ?

Both classical and operant conditioning involve learning by association. In classical conditioning, responses are involuntary and automatic; however, responses are voluntary and learned in operant conditioning. In classical conditioning, the event that drives the behavior (the stimulus) comes before the behavior; in operant conditioning, the event that drives the behavior (the consequence) comes after the behavior. Also, whereas classical conditioning involves an organism forming an association between an involuntary (reflexive) response and a stimulus, operant conditioning involves an organism forming an association between a voluntary behavior and a consequence.

What is the difference between a reflex and a learned behavior?

A reflex is a behavior that humans are born knowing how to do, such as sucking or blushing; these behaviors happen automatically in response to stimuli in the environment. Learned behaviors are things that humans are not born knowing how to do, such as swimming and surfing. Learned behaviors are not automatic; they occur as a result of practice or repeated experience in a situation.

Personal Application Questions

What is your personal definition of learning? How do your ideas about learning compare with the definition of learning presented in this text?

What kinds of things have you learned through the process of classical conditioning? Operant conditioning? Observational learning? How did you learn them?

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Attention in Psychology, Neuroscience, and Machine Learning

Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.

1. Introduction

Attention is a topic widely discussed publicly and widely studied scientifically. It has many definitions within and across multiple fields including psychology, neuroscience, and, most recently, machine learning (Chun et al., 2011 ; Cho et al., 2015 ). As William James wrote at the dawn of experimental psychology, “Everyone knows what attention is. It is the taking possession by the mind, in clear, and vivid form, of one out of what seems several simultaneously possible objects or trains of thought.” Since James wrote this, many attempts have been made to more precisely define and quantify this process while also identifying the underlying mental and neural architectures that give rise to it. The glut of different experimental approaches and conceptualizations to study what is spoken of as a single concept, however, has led to something of a backlash amongst researchers. As was claimed in the title of a recent article arguing for a more evolution-informed approach to the concept, “No one knows what attention is” (Hommel et al., 2019 ).

Attention is certainly far from a clear or unified concept. Yet despite its many, vague, and sometimes conflicting definitions, there is a core quality of attention that is demonstrably of high importance to information processing in the brain and, increasingly, artificial systems. Attention is the flexible control of limited computational resources. Why those resources are limited and how they can best be controlled will vary across use cases, but the ability to dynamically alter and route the flow of information has clear benefits for the adaptiveness of any system.

The realization that attention plays many roles in the brain makes its addition to artificial neural networks unsurprising. Artificial neural networks are parallel processing systems comprised of individual units designed to mimic the basic input-output function of neurons. These models are currently dominating the machine learning and artificial intelligence (AI) literature. Initially constructed without attention, various mechanisms for dynamically re-configuring the representations or structures of these networks have now been added.

The following section, section 2, will cover broadly the different uses of the word attention in neuroscience and psychology, along with its connection to other common neuroscientific topics. Throughout, the conceptualization of attention as a way to control limited resources will be highlighted. Behavioral studies will be used to demonstrate the abilities and limits of attention while neural mechanisms point to the physical means through which these behavioral effects are manifested. In section 3, the state of attention research in machine learning will be summarized and relationships between artificial and biological attention will be indicated where they exist. And in section 4 additional ways in which findings from biological attention can influence its artificial counterpart will be presented.

The primary aim of this review is to give researchers in the field of AI or machine learning an understanding of how attention is conceptualized and studied in neuroscience and psychology in order to facilitate further inspiration where fruitful. A secondary aim is to inform those who study biological attention how these processes are being operationalized in artificial systems as it may influence thinking about the functional implications of biological findings.

2. Attention in Neuroscience and Psychology

The scientific study of attention began in psychology, where careful behavioral experimentation can give rise to precise demonstrations of the tendencies and abilities of attention in different circumstances. Cognitive science and cognitive psychology aim to turn these observations into models of how mental processes could create such behavioral patterns. Many word models and computational models have been created that posit different underlying mechanisms (Driver, 2001 ; Borji and Itti, 2012 ).

The influence of single-cell neurophysiology in non-human primates along with non-invasive means of monitoring human brain activity such as EEG, fMRI, and MEG have made direct observation of the underlying neural processes possible. From this, computational models of neural circuits have been built that can replicate certain features of the neural responses that relate to attention (Shipp, 2004 ).

In the following sub-sections, the behavioral and neural findings of several different broad classes of attention will be discussed.

2.1. Attention as Arousal, Alertness, or Vigilance

In its most generic form, attention could be described as merely an overall level of alertness or ability to engage with surroundings. In this way it interacts with arousal and the sleep-wake spectrum. Vigilance in psychology refers to the ability to sustain attention and is therefore related as well. Note, while the use of these words clusters around the same meaning, they are sometimes used more specifically in different niche literature (Oken et al., 2006 ).

Studying subjects in different phases of the sleep-wake cycle, under sleep deprivation, or while on sedatives offers a view of how this form of attention can vary and what the behavioral consequences are. By giving subjects repetitive tasks that require a level of sustained attention—such as keeping a ball within a certain region on a screen—researchers have observed extended periods of poor performance in drowsy patients that correlate with changes in EEG signals (Makeig et al., 2000 ). Yet, there are ways in which tasks can be made more engaging that can lead to higher performance even in drowsy or sedated states. This includes increasing the promise of reward for performing the task, adding novelty or irregularity, or introducing stress (Oken et al., 2006 ). Therefore, general attention appears to have limited reserves that won't be deployed in the case of a mundane or insufficiently rewarding task but can be called upon for more promising or interesting work.

Interestingly, more arousal is not always beneficial. The Yerkes-Dodson curve ( Figure 1B ) is an inverted-U that represents performance as a function of alertness on sufficiently challenging tasks: at low levels of alertness performance is poor, at medium levels it is good, and at high levels it becomes poor again. The original study used electric shocks in mice to vary the level of alertness, but the finding has been repeated with other measures (Diamond, 2005 ). It may explain why psychostimulants such as Adderall or caffeine can work to increase focus in some people at some doses but become detrimental for others (Wood et al., 2014 ).

General attention and alertness (A) Cells in the locus coeruleus release norepinephrine (also known as noradrenaline) onto many parts of the brain with different functions, including onto other neuromodulatory systems. This contributes to overall arousal (Samuels and Szabadi, 2008 ). Colors here represent different divisions of the brain: forebrain (green), diencephalon (yellow), and brainstem (blue). (B) The Yerkes-Dodson curve describes the nonlinear relationship between arousal and performance on challenging tasks.

The neural circuits underlying the sleep-wake cycle are primarily in the brain stem (Coenen, 1998 ). These circuits control the flow of information into the thalamus and then onto cortex. Additionally, neuromodulatory systems play a large role in the control of generalized attention. Norepinephrine, acetylcholine, and dopamine are believed to influence alertnesss, orienting to important information, and executive control of attention, respectively (Posner, 2008 ). The anatomy of neuromodulators matches their function as well. Neurons that release norepinephrine, for example, have their cell bodies in the brain stem but project very broadly across the brain, allowing them to control information processing broadly ( Figure 1A ).

2.2. Sensory Attention

In addition to overall levels of arousal and alertness, attention can also be selectively deployed by an awake subject to specific sensory inputs. Studying attention within the context of a specific sensory system allows for tight control over both stimuli and the locus of attention. Generally, to look for this type of attention the task used needs to be quite challenging. For example, in a change detection task, the to-be-detected difference between two stimuli may be very slight. More generally, task difficulty can be achieved by presenting the stimulus for only a very short period of time or only very weakly.

A large portion of the study of attention in systems neuroscience and psychology centers on visual attention in particular (Kanwisher and Wojciulik, 2000 ). This may reflect the general trend in these fields to emphasis the study of visual processing over other sensory systems (Hutmacher, 2019 ), along with the dominant role vision plays in the primate brain. Furthermore, visual stimuli are frequently used in studies meant to address more general, cognitive aspects of attention as well.

Visual attention can be broken down broadly into spatial and feature-based attention.

2.2.1. Visual Spatial Attention

Saccades are small and rapid eye movements made several times each second. As the fovea offers the highest visual resolution on the retina, choosing where to place it is essentially a choice about where to deploy limited computational resources. In this way, eye movements indicate the locus of attention. As this shift of attention is outwardly visible it is known as overt visual attention.

By tracking eye movements as subjects are presented with different images, researchers have identified image patterns that automatically attract attention. Such patterns are defined by oriented edges, spatial frequency, color contrast, intensity, or motion (Itti and Koch, 2001 ). Image regions that attract attention are considered “salient” and are computed in a “bottom-up” fashion. That is, they don't require conscious or effortful processing to identify and are likely the result of built-in feature detectors in the visual system. As such, saliency can be computed very quickly. Furthermore, different subjects tend to agree on which regions are salient, especially those identified in the first few saccades (Tatler et al., 2005 ).

Salient regions can be studied in “free-viewing” situations, that is, when the subject is not given any specific instructions about how to view the image. When a particular task is assigned, the interplay between bottom-up and “top-down” attention becomes clear. For example, when instructed to saccade to a specific visual target out of an array, subjects may incorrectly saccade to a particularly salient distractor instead (van Zoest and Donk, 2005 ). More generally, task instructions can have a significant effect on the pattern of saccades generated when subjects are viewing a complex natural image and given high-level tasks (e.g., asked to assess the age of a person or guess their socio-economic status). Furthermore, the natural pattern of eye movements when subjects perform real world tasks, like sandwich making, can provide insights to underlying cognitive processes (Hayhoe and Ballard, 2005 ).

When subjects need to make multiple saccades in a row they tend not to return to locations they have recently attended and may be slow to respond if something relevant occurs there. This phenomenon is known as inhibition of return (Itti and Koch, 2001 ). Such behavior pushes the visual system to not just exploit image regions originally deemed most salient but to explore other areas as well. It also means the saccade generating system needs to have a form of memory; this is believed to be implemented by short-term inhibition of the representation of recently-attended locations.

While eye movements are an effective means of controlling visual attention, they are not the only option. “Covert” spatial attention is a way of emphasizing processing of different spatial locations without an overt shift in fovea location. Generally, in the study of covert spatial attention, subjects must fixate on a central point throughout the task. They are cued to covertly attend to a location in their peripheral vision where stimuli relevant for their visual task will likely appear. For example, in an orientation discrimination task, after the spatial cue is provided an oriented grating will flash in the cued location and the subject will need to indicate its orientation. On invalidly-cued trials (when the stimulus appears in an uncued location), subjects perform worse than on validly-cued (or uncued) trials (Anton-Erxleben and Carrasco, 2013 ). This indicates that covert spatial attention is a limited resource that can be flexibly deployed and aids in the processing of visual information.

Covert spatial attention is selective in the sense that certain regions are selected for further processing at the expense of others. This has been referred to as the “spotlight” of attention. Importantly, for covert—as opposed to overt—attention the input to the visual system can be identical while the processing of that input is flexibly selective.

Covert spatial attention can be impacted by bottom-up saliency as well. If an irrelevant but salient object is flashed at a location that then goes on to have a task relevant stimulus, the exogenous spatial attention drawn by the irrelevant stimulus can get applied to the task relevant stimulus, possibly providing a performance benefit. If it is flashed at an irrelevant location, however, it will not help, and can harm performance (Berger et al., 2005 ). Bottom-up/exogenous attention has a quick time course, impacting covert attention for 80–130 ms after the distractor appears (Anton-Erxleben and Carrasco, 2013 ).

In some theories of attention, covert spatial attention exists to help guide overt attention. Particularly, the pre-motor theory of attention posits that the same neural circuits plan saccades and control covert spatial attention (Rizzolatti et al., 1987 ). The frontal eye field (FEF) is known to be involved in the control of eye movements. Stimulating the neurons in FEF at levels too low to evoke eye movements has been shown to create effects similar to covert attention (Moore et al., 2003 ). In this way, covert attention may be a means of deciding where to overtly look. The ability to covertly attend may additionally be helpful in social species, as eye movements convey information about knowledge and intent that may best be kept secret (Klein et al., 2009 ).

To study the neural correlates of covert spatial attention, researchers identify which aspects of neural activity differ based only on differences in the attentional cue (and not on differences in bottom-up features of the stimuli). On trials where attention is cued toward the receptive field of a recorded neuron, many changes in the neural activity have been observed (Noudoost et al., 2010 ; Maunsell, 2015 ). A commonly reported finding is an increase in firing rates, typically of 20–30% (Mitchell et al., 2007 ). However, the exact magnitude of the change depends on the cortical area studied, with later areas showing stronger changes (Luck et al., 1997 ; Noudoost et al., 2010 ). Attention is also known to impact the variability of neural firing. In particular, it decreases trial-to-trial variability as measured via the Fano Factor and decreases noise correlations between pairs of neurons. Attention has even been found to impact the electrophysiological properties of neurons in a way that reduces their likelihood of firing in bursts and also decreases the height of individual action potentials (Anderson et al., 2013 ).

In general, the changes associated with attention are believed to increase the signal-to-noise ratio of the neurons that represent the attended stimulus, however they can also impact communication between brain areas. To this end, attention's effect on neural synchrony is important. Within a visual area, attention has been shown to increase spiking coherence in the gamma band—that is at frequencies between 30 and 70 Hz (Fries et al., 2008 ). When a group of neurons fires synchronously, their ability to influence shared downstream areas is enhanced. Furthermore, attention may also be working to directly coordinate communication across areas. Synchronous activity between two visual areas can be a sign of increased communication and attention has been shown to increase synchrony between the neurons that represent the attended stimulus in areas V1 and V4, for example (Bosman et al., 2012 ). Control of this cross-area synchronization appears to be carried out by the pulvinar (Saalmann et al., 2012 ).

In addition to investigating how attention impacts neurons in the visual pathways, studies have also searched for the source of top-down attention (Noudoost et al., 2010 ; Miller and Buschman, 2014 ). The processing of bottom-up attention appears to culminate with a saliency map produced in the lateral intraparietal area (LIP). The cells here respond when salient stimuli are in their receptive field, including task-irrelevant but salient distractors. Prefrontal areas such as FEF, on the other hand, appear to house the signals needed for top-down control of spatial attention and are less responsive to distractors.

While much of the work on the neural correlates of sensory attention focuses on the cortex, subcortical areas appear to play a strong role in the control and performance benefits of attention as well. In particular, the superior colliculus assists in both covert and overt spatial attention and inactivation of this region can impair attention (Krauzlis et al., 2013 ). And, as mentioned above, the pulvinar plays a role in attention, particularly with respect to gating effects on cortex (Zhou et al., 2016 ).

2.2.2. Visual Feature Attention

Feature attention is another form of covert selective attention. In the study of feature attention, instead of being cued to attend to a particular location, subjects are cued on each trial to attend to a particular visual feature such as a specific color, a particular shape, or a certain orientation. The goal of the task may be to detect if the cued feature is present on the screen or readout another one of its qualities (e.g., to answer “what color is the square?” should result in attention first deployed to squares). Valid cueing about the attended feature enhances performance. For example, when attention was directed toward a particular orientation, subjects were better able to detect faint gratings of that orientation than of any other orientation (Rossi and Paradiso, 1995 ). While the overall task (e.g., detection of an oriented grating) remains the same, the specific instructions (detection of 90° grating vs. 60° vs. 30°) will be cued on each individual trial, or possibly blockwise. Successful trial-wise cueing indicates that this form of attention can be flexibly deployed on fast timescales.

Visual search tasks are also believed to activate feature-based attention ( Figure 2 ). In these tasks, an array of stimuli appears on a screen and subjects need to indicate—frequently with an eye movement—the location of the cued stimulus. As subjects are usually allowed to make saccades throughout the task as they search for the cued stimulus, this task combines covert feature-based attention with overt attention. In fact, signals of top-down feature-based attention have been found in FEF, the area involved in saccade choice (Zhou and Desimone, 2011 ). Because certain features can create a pop-out effect—for example, a single red shape amongst several black ones will immediately draw attention—visual search tasks also engage bottom-up attention which, depending on the task, may need to be suppressed (Wolfe and Horowitz, 2004 ).

Visual search tasks engage many forms of visual attention. Across the top row the progression of a visual search task is shown. First, a cue indicates the target of the visual search, in this case a blue X. Then a search array appears with many non-targets. Top-down feature attention to cells that represent the color blue and the shape X will increase their firing throughout the visual field but firing will be strongest where blue or Xs actually occur. These neural response will play a role in generating a map of covert spatial attention which can be used to explore visual space before saccading. After the shift in overt attention with the first saccade, the covert attention map is remade. Finally, the target is located and successfully saccaded to. If the visual array contained a pop-out stimulus (for example a green O) it may have captured covert spatial attention in a bottom-up way and led to an additional incorrect saccade.

Neural effects of feature-based attention in the visual system are generally similar to those of spatial attention. Neurons that represent the attended feature, for example, have increased firing rates, and those that represent very different features have suppressed rates (Treue and Trujillo, 1999 ). As opposed to spatial attention, however, feature-based attention is spatially-global. This means that when deploying attention to a particular feature the activity of the neurons that represent that feature anywhere in visual space are modulated (Saenz et al., 2002 ). Another difference between spatial and feature attention is the question of how sources of top-down attention target the correct neurons in the visual system. The retinotopic map, wherein nearby cells represent nearby spatial locations, makes spatial targeting straightforward, but cells are not as neatly organized according to preferred visual features.

The effects of spatial and feature attention appear to be additive (Hayden and Gallant, 2009 ). Furthermore, both feature and spatial attention are believed to create their effects by acting on the local neural circuits that implement divisive normalization in visual cortex (Reynolds and Heeger, 2009 ). Modeling work has shown that many of the neural effects of selective attention can be captured by assuming that top-down connections provide targeted synaptic inputs to cells in these circuits (Lindsay et al., 2019 ). However, models that rely on effects of the neuromodulator acetylcholine can also replicate neural correlates of attention (Sajedin et al., 2019 ).

Potential sources of top-down feature-based attention have been found in prefrontal cortex where sustained activity encodes the attended feature (Bichot et al., 2015 ; Paneri and Gregoriou, 2017 ). Inactivating the ventral prearcuate area impairs performance on search tasks. From prefrontal areas, attention signals are believed to travel in a reverse hierarchical way wherein higher visual areas send inputs to those below them (Ahissar and Hochstein, 2000 ).

A closely related topic to feature attention is object attention. Here, attention is not deployed to an abstract feature in advance of a visual stimulus, but rather it is applied to a particular object in the visual scene (Chen, 2012 ). The initial feedforward pass of activity through the visual hierarchy is able to pre-attentively segregate objects from their backgrounds in parallel across the visual field, provided these objects have stark and salient differences from the background. In more crowded or complex visual scenes, recurrent and serial processing is needed in order to identify different objects (Lamme and Roelfsema, 2000 ). Serial processing involves moving limited attentional resources from one location in the image to another; it can take the form of shifts in either covert or overt spatial attention (Buschman and Miller, 2009 ). Recurrent connections in the visual system—that is, both horizontal connections from nearby neurons in the same visual area and feedback connections from those in higher visual areas—aid in figure-ground segregation and object identification. The question of how the brain performs perceptual grouping of low-level features into a coherent object identity has been studied for nearly a century. It is believed that attention may be required for grouping, particularly for novel or complex objects (Roelfsema and Houtkamp, 2011 ). This may be especially important in visual search tasks that require locating an object that is defined by a conjunction of several features.

Neurally, the effects of object-based attention can spread slowly through space as parts of an object are mentally traced (Roelfsema et al., 1998 ). Switching attention to a location outside an object appears to incur a greater cost than switching to the same distance away but within the object (Brown and Denney, 2007 ). In addition, once attention is applied to a visual object, it is believed to activate feature-based attention for the different features of that object across the visual field (O'Craven et al., 1999 ).

Another form of attention sometimes referred to as feature attention involves attending to an entire feature dimension. An example of this is the Stroop test, wherein the names of colors are written in different colored ink and subjects either need to read the word itself or say the color of the ink. Here attention cannot be deployed to a specific feature in advance, only to the dimensions word or color. Neurally, the switch between dimensions appears to impact sensory coding in the visual stream and is controlled by frontal areas (Liu et al., 2003 ).

2.2.3. Computational Models of Visual Attention

Visual attention, being one of the most heavily-studied topics in the neuroscience of attention, has inspired many computational models of how attention works. In general, these models synthesize various neurophysiological findings in order to help explain how the behavioral impacts of attention arise (Heinke and Humphreys, 2005 ).

Several computational models meant to calculate saliency have been devised (Itti and Koch, 2001 ). These models use low-level visual feature detectors—usually designed to match those in the visual system—to create an image-specific saliency map that can predict the saccade patterns of humans in response to the same image. Another approach to calculating saliency based on information theoretic first principles has also been explored and was able to account for certain visual search behaviors (Bruce and Tsotsos, 2009 ).

Some of the behavioral and neural correlates of attention are similar whether the attention is bottom-up or top-down. In the Biased Competition Model of attention, stimuli compete against each other to dominate the neural response (Desimone, 1998 ). Attention (bottom-up or top-down) can thus work by biasing this competition toward the stimulus that is the target of attention. While the Biased Competition Model is sometimes used simply as a “word model” to guide intuition, explicit computational instantiations of it have also been built. A hierarchical model of the visual pathway that included top-down biasing as well as local competition mediated through horizontal connections was able to replicate multiple neural effects of attention (Deco and Rolls, 2004 ). A model embodying similar principles but using spiking neurons was also implemented (Deco and Rolls, 2005 ).

Similar models have been constructed explicitly to deal with attribute naming tasks such as the Stroop test described above. The Selective Attention Model (SLAM), for example, has local competition in both the sensory encoding and motor output modules and can mimic known properties of response times in easier and more challenging Stroop-like tests (Phaf et al., 1990 ).

Visual perception has been framed and modeled as a problem of Bayesian inference (Lee and Mumford, 2003 ). Within this context, attention can help resolve uncertainty under settings where inference is more challenging, typically by modulating priors (Rao, 2005 ). For example, in Chikkerur et al. ( 2010 ) spatial attention functions to reduce uncertainty about object identity and feature attention reduces spatial uncertainty. These principles can capture both behavioral and neural features of attention and can be implemented in a biologically-inspired neural model.

The feature similarity gain model of attention (FSGM) is a description of the neural effects of top-down attention that can be applied in both the feature and spatial domain (Treue and Trujillo, 1999 ). It says that the way in which a neuron's response is modulated by attention depends on that neuron's tuning. Tuning is a description of how a neuron responds to different stimuli, so according to the FSGM a neuron that prefers (that is, responds strongly to), e.g., the color blue, will have its activity enhanced by top-down attention to blue. The FSGM also says attention to non-preferred stimuli will cause a decrease in firing and that, whether increased or decreased, activity is scaled multiplicatively by attention. Though not initially defined as a computational model, this form of neural modulation has since been shown through modeling to be effective at enhancing performance on challenging visual tasks (Lindsay and Miller, 2018 ).

Other models conceptualize attention as a dynamic routing of information through a network. An implementation of this form of attention can be found in the Selective Attention for Identification Model (SAIM) (Heinke and Humphreys, 2003 ). Here, attention routes information from the retina to a representation deemed the “focus of attention”; depending on the current task, different parts of the retinal representation will be mapped to the focus of attention.

2.2.4. Attention in Other Sensory Modalities

A famous example of the need for selective attention in audition is the “cocktail party problem”: the difficulty of focusing on the speech from one speaker in a crowded room of multiple speakers and other noises (Bronkhorst, 2015 ). Solving the problem is believed to involve “early” selection wherein low level features of a voice such as pitch are used to determine which auditory information is passed on for further linguistic processing. Interestingly, selective auditory attention has the ability to control neural activity at even the earliest level of auditory processing, the cochlea (Fritz et al., 2007 ).

Spatial and feature attention have also been explored in the somatosensory system. Subjects cued to expect a tap at different parts on their body are better able to detect the sensation when that cue is valid. However, these effects seem weaker than they are in the visual system (Johansen-Berg and Lloyd, 2000 ). Reaction times are faster in a detection task when subjects are cued about the orientation of a stimulus on their finger (Schweisfurth et al., 2014 ).

In a study that tested subjects' ability to detect a taste they had been cued for it was shown that validly-cued tastes can be detected at lower concentrations than invalidly-cued ones (Marks and Wheeler, 1998 ). This mimics the behavioral effects found with feature-based visual attention. Attention to olfactory features has not been thoroughly explored, though visually-induced expectations about a scent can aid its detection (Gottfried and Dolan, 2003 ; Keller, 2011 ).

Attention can also be spread across modalities to perform tasks that require integration of multiple sensory signals. In general, the use of multiple congruent sensory signals aids detection of objects when compared to relying only on a single modality. Interestingly, some studies suggest that humans may have a bias for the visual domain, even when the signal from another domain is equally valid (Spence, 2009 ). Specifically, the visual domain appears to dominate most in tasks that require identifying the spatial location of a cue (Bertelson and Aschersleben, 1998 ). This can be seen most readily in ventriloquism, where the visual cue of the dummy's mouth moving overrides auditory evidence about the true location of the vocal source. Visual evidence can also override tactile evidence, for example, in the context of the rubber arm illusion (Botvinick and Cohen, 1998 ).

Another effect of the cross-modal nature of sensory processing is that an attentional cue in one modality can cause an orienting of attention in another modality (Spence and Driver, 2004 ). Generally, the attention effects in the non-cued modality are weaker. This cross-modal interaction can occur in the context of both endogenous (“top-down”) and exogenous (“bottom-up”) attention.

2.3. Attention and Executive Control

With multiple simultaneous competing tasks, a central controller is needed to decide which to engage in and when. What's more, how to best execute tasks can depend on history and context. Combining sensory inputs with past knowledge in order to coordinate multiple systems for the job of efficient task selection and execution is the role of executive control, and this control is usually associated with the prefrontal cortex (Miller and Buschman, 2014 ). As mentioned above, sources of top-down visual attention have also been located in prefrontal regions. Attention can reasonably be thought of as the output of executive control. The executive control system must thus select the targets of attention and communicate that to the systems responsible for implementing it. According to the reverse hierarchy theory described above, higher areas signal to those from which they get input which send the signal on to those below them and so on (Ahissar and Hochstein, 2000 ). This means that, at each point, the instructions for attention must be transformed into a representation that makes sense for the targeted region. Through this process, the high level goals of the executive control region can lead to very specific changes, for example, in early sensory processing.

Executive control and working memory are also intertwined, as the ability to make use of past information as well as to keep a current goal in mind requires working memory. Furthermore, working memory is frequently identified as sustained activity in prefrontal areas. A consequence of the three-way relationship between executive control, working memory, and attention is that the contents of working memory can impact attention, even when not desirable for the task (Soto et al., 2008 ). For example, if a subject has to keep an object in working memory while simultaneously performing a visual search for a separate object, the presence of the stored object in the search array can negatively interfere with the search (Soto et al., 2005 ). This suggests that working memory can interfere with the executive control of attention. However, there still appears to be additional elements of that control that working memory alone does not disrupt. This can be seen in studies wherein visual search performance is even worse when subjects believe they will need to report the memorized item but are shown a search array for the attended item instead (Olivers and Eimer, 2011 ). This suggests that, while all objects in working memory may have some influence over attention, the executive controller can choose which will have the most.

Beyond the flexible control of attention within a sensory modality, attention can also be shifted between modalities. Behavioral experiments indicate that switching attention either between two different tasks within a sensory modality (for example, going from locating a visual object to identifying it) or between sensory modalities (switching from an auditory task to a visual one) incurs a computational cost (Pashler, 2000 ). This cost is usually measured as the extent to which performance is worse on trials just after the task has been switched vs. those where the same task is being repeated. Interestingly, task switching within a modality seems to incur a larger cost than switching between modalities (Murray et al., 2009 ). A similar result is found when switching between or across modes of response (for example, pressing a bottom vs. verbal report), suggesting this is not specific to sensory processing (Arrington et al., 2003 ). Such findings are believed to stem from the fact that switching within a modality requires a reconfiguration of the same neural circuits, which is more difficult than merely engaging the circuitry of a different sensory system. An efficient executive controller would need to be aware of these costs when deciding to shift attention and ideally try to minimize them; it has been shown that switch costs can be reduced with training (Gopher, 1996 ).

The final question regarding the executive control of attention is how it evolves with learning. Eye movement studies indicate that searched-for items can be detected more rapidly in familiar settings rather than novel ones, suggesting that previously-learned associations guide overt attention (Chun and Jiang, 1998 ). Such benefits are believed to rely on the hippocampus (Aly and Turk-Browne, 2017 ). In general, however, learning how to direct attention is not as studied as other aspects of the attention process. Some studies have shown that subjects can enhance their ability to suppress irrelevant task information, and the generality of that suppression depends on the training procedure (Kelley and Yantis, 2009 ). Looking at the neural correlates of attention learning, imaging results suggest that the neural changes associated with learning do not occur in the sensory pathways themselves but rather in areas more associated with attentional control (Kelley and Yantis, 2010 ). Though not always easy to study, the development of attentional systems in infancy and childhood may provide further clues as to how attention can be learned (Reynolds and Romano, 2016 ).

2.4. Attention and Memory

Attention and memory have many possible forms of interaction. If memory has a limited capacity, for example, it makes sense for the brain to be selective about what is allowed to enter it. In this way, the ability of attention to dynamically select a subset of total information is well-matched to the needs of the memory system. In the other direction, deciding to recall a specific memory is a choice about how to deploy limited resources. Therefore, both memory encoding and retrieval can rely on attention.

The role of attention in memory encoding appears quite strong (Aly and Turk-Browne, 2017 ). For information to be properly encoded into memory, it is best for it be the target of attention. When subjects are asked to memorize a list of words while simultaneously engaging in a secondary task that divides their attention, their ability to consciously recall those words later is impaired (though their ability to recognize the words as familiar is not so affected) (Gardiner and Parkin, 1990 ). Imaging studies have shown that increasing the difficulty of the secondary task weakens the pattern of activity related to memory encoding in the left ventral inferior frontal gyrus and anterior hippocampus and increases the representation of secondary task information in dorsolateral prefrontal and superior parietal regions (Uncapher and Rugg, 2005 ). Therefore, without the limited neural processing power placed on the task of encoding, memory suffers. Attention has also been implicated in the encoding of spatially-defined memories and appears to stabilize the representations of place cells (Muzzio et al., 2009 ).

Implicit statistical learning can also be biased by attention. For example, in Turk-Browne et al. ( 2005 ) subjects watched a stream of stimuli comprised of red and green shapes. The task was to detect when a shape of the attended color appeared twice in a row. Unbeknownst to the subjects, certain statistical regularities existed in the stream such that there were triplets of shapes likely to occur close together. When shown two sets of three shapes—one an actual co-occurring triplet and another a random selection of shapes of the same color—subjects recognized the real triplet as more familiar, but only if the triplets were from the attended color. The statistical regularities of the unattended shapes were not learned.

Yet some learning can occur even without conscious attention. For example, in Watanabe ( 2003 ) patients engaged in a letter detection task located centrally in their visual field while random dot motion was shown in the background at sub-threshold contrast. The motion had 10% coherence in a direction that was correlated with the currently-presented letter. Before and after learning this task, subjects performed an above-threshold direction classification task. After learning the task, direction classification improved only for the direction associated with the targeted letters. This suggests a reward-related signal activated by the target led to learning about a non-attended component of the stimulus.

Many behavioral studies have explored the extent to which attention is needed for memory retrieval. For example, by asking subjects to simultaneously recall a list of previously-memorized words and engage in a secondary task like card sorting, researchers can determine if memory retrieval pulls from the same limited pool of attentional resources as the task. Some such studies have found that retrieval is impaired by the co-occurrence of an attention-demanding task, suggesting it is an attention-dependent process. The exact findings, however, depend on the details of the memory and non-memory tasks used (Lozito and Mulligan, 2006 ).

Even if memory retrieval does not pull from shared attentional resources, it is still clear that some memories are selected for more vivid retrieval at any given moment than others. Therefore, a selection process must occur. An examination of neuroimaging results suggests that the same parietal brain regions responsible for the top-down allocation and bottom-up capture of attention may play analogous roles during memory retrieval (Wagner et al., 2005 ; Ciaramelli et al., 2008 ).

Studies of memory retrieval usually look at medium to long-term memory but a mechanism for attention to items in working memory has also been proposed (Manohar et al., 2019 ). It relies on two different mechanisms of working memory: synaptic traces for non-attended items and sustained activity for the attended one.

Some forms of memory occur automatically and within the sensory processing stream itself. Priming is a well-known phenomenon in psychology wherein the presence of a stimulus at one point in time impacts how later stimuli are processed or interpreted. For example, the word “doctor” may be recognized more quickly following the word “hospital” than the word “school.” In this way, priming requires a form of implicit memory to allow previous stimuli to impact current ones. Several studies on conceptual or semantic priming indicate that attention to the first stimulus is required for priming effects to occur (Ballesteros and Mayas, 2015 ); this mirrors findings that attention is required for memory encoding more generally.

Most priming is positive, meaning that the presence of a stimulus at one time makes the detection and processing of it or a related stimulus more likely at a later time. In this way, priming can be thought of as biasing bottom-up attention. However, top-down attention can also create negative priming. In negative priming, when stimuli that functioned as a distractor on the previous trial serve as the target of attention on the current trial, performance suffers (Frings et al., 2015 ). This may stem from a holdover effect wherein the mechanisms of distractor suppression are still activated for the now-target stimulus.

Adaptation can also be considered a form of implicit memory. Here, neural responses decrease after repeated exposure to the same stimulus. By reducing the response to repetition, changes in the stimulus become more salient. Attention—by increasing the neural response to attended stimuli—counters the effects of adaptation (Pestilli et al., 2007 ; Anton-Erxleben et al., 2013 ). Thus, both with priming and adaptation, top-down attention can overcome automatic processes that occur at lower levels which may be guiding bottom-up attention.

3. Attention in Machine Learning

While the concept of artificial attention has come up prior to the current resurgence of artificial neural networks, many of its popular uses today center on ANNs (Mancas et al., 2016 ). The use of attention mechanisms in artificial neural networks came about—much like the apparent need for attention in the brain—as a means of making neural systems more flexible. Attention mechanisms in machine learning allow a single trained artificial neural network to perform well on multiple tasks or tasks with inputs of variable length, size, or structure. While the spirit of attention in machine learning is certainly inspired by psychology, its implementations do not always track with what is known about biological attention, as will be noted below.

In the form of attention originally developed for ANNs, attention mechanisms worked within an encoder-decoder framework and in the context of sequence models (Cho et al., 2015 ; Chaudhari et al., 2019 ). Specifically, an input sequence will be passed through an encoder (likely a recurrent neural network) and the job of the decoder (also likely a recurrent neural network) will be to output another sequence. Connecting the encoder and decoder is an attention mechanism.

Commonly, the output of the encoder is a set of a vectors, one for each element in the input sequence. Attention helps determine which of these vectors should be used to generate the output. Because the output sequence is dynamically generated one element at a time, attention can dynamically highlight different encoded vectors at each time point. This allows the decoder to flexibly utilize the most relevant parts of the input sequence.

The specific job of the attention mechanism is to produce a set of scalar weightings, α t i , one for each of the encoded vectors ( v i ). At each step t , the attention mechanism (ϕ) will take in information about the decoder's previous hidden state ( h t −1 ) and the encoded vectors to produce unnormalized weightings:

Because attention is a limited resource, these weightings need to represent relative importance. To ensure that the α values sum to one, the unnormalized weightings are passed through a softmax:

These attention values scale the encoded vectors to create a single context vector on which the decoder can be conditioned:

This form of attention can be made entirely differentiable and so the whole network can be trained end-to-end with simple gradient descent.

This type of artificial attention is thus a form of iterative re-weighting. Specifically, it dynamically highlights different components of a pre-processed input as they are needed for output generation. This makes it flexible and context dependent, like biological attention. As such it is also inherently dynamic. While sequence modeling already has an implied temporal component, this form of attention can also be applied to static inputs and outputs (as will be discussed below in the context of image processing) and will thus introduce dynamics into the model.

In the traditional encoder-decoder framework without attention, the encoder produced a fixed-length vector that was independent of the length or features of the input and static during the course of decoding. This forced long sequences or sequences with complex structure to be represented with the same dimensionality as shorter or simpler ones and didn't allow the decoder to interrogate different parts of the input during the decoding process. But encoding the input as a set of vectors equal in length to the input sequence makes it possible for the decoder to selectively attend to the portion of the input sequence relevant at each time point of the decoding. Again, as in interpretations of attention in the brain, attention in artificial systems is helpful as a way to flexibly wield limited resources. The decoder can't reasonably be conditioned on the entirety of the input so at some point a bottleneck must be introduced. In the system without attention, the fixed-length encoding vector was a bottleneck. When an attention mechanism is added, the encoding can be larger because the bottleneck (in the form of the context vector) will be produced dynamically as the decoder determines which part of the input to attend to.

The motivation for adding such attention mechanisms to artificial systems is of course to improve their performance. But another claimed benefit of attention is interpretability. By identifying on which portions of the input attention is placed (that is, which α i values are high) during the decoding process, it may be possible to gain an understanding of why the decoder produced the output that it did. However, caution should be applied when interpreting the outputs of attention as they may not always explain the behavior of the model as expected (Jain and Wallace, 2019 ; Wiegreffe and Pinter, 2019 ).

In the following subsections, specific applications of this general attention concept will be discussed, along with some that don't fit neatly into this framework. Further analogies to the biology will also be highlighted.

3.1. Attention for Natural Language Processing

As described above, attention mechanisms have frequently been added to models charged with processing sequences. Natural language processing (NLP) is one of the most common areas of application for sequence modeling. And, though it was not the original domain of attention in machine learning—nor does it have the most in common with biology—NLP is also one of the most common areas of application for attention (Galassi et al., 2019 ).

An early application of the this form of attention in artificial neural networks was to the task of translation (Bahdanau et al., 2014 ) ( Figure 3 ). In this work, a recurrent neural network encodes the input sentence as a set of “annotation” vectors, one for each word in the sentence. The output, a sentence in the target language, is generated one word at a time by a recurrent neural network. The probability of each generated word is a function of the previously generated word, the hidden state of the recurrent neural network and a context vector generated by the attention mechanism. Here, the attention mechanism is a small feedforward neural network that takes in the hidden state of the output network as well as the current annotation vector to create the weighting over all annotation vectors.

Attention for neural machine translation. The to-be-translated sentence is encoded to a series of vectors ( v ) via a recurrent neural network. The attention mechanism (ϕ) uses the hidden state of the decoder ( h ) and these vectors to determine how the encoded vectors should be combined to produce a context vector ( c ), which influences the next hidden state of the decoder and thus the next word in the translated sentence.

Blending information from all the words in the sentence this way allows the network to pull from earlier or later parts when generating an output word. This can be especially useful for translating between languages with different standard word orders. By visualizing the locations in the input sentence to which attention was applied the authors observed attention helping with this problem.

Since this initial application, many variants of attention networks for language translation have been developed. In Firat et al. ( 2016 ), the attention mechanism was adapted so it could be used to translate between multiple pairs of languages rather than just one. In Luong et al. ( 2015 ), the authors explore different structures of attention to determine if the ability to access all input words at once is necessary. And in Cheng et al. ( 2016 ), attention mechanisms were added to the recurrent neural networks that perform the sentence encoding and decoding in order to more flexibly create sentence representations.

In 2017, the influential “Attention is All You Need” paper utilized a very different style of architecture for machine translation (Vaswani et al., 2017 ). This model doesn't have any recurrence, making it simpler to train. Instead, words in the sentence are encoded in parallel and these encodings generate key and query representations that are combined to create attention weightings. These weightings scale the word encodings themselves to create the next layer in the model, a process known as “self-attention.” This process repeats, and eventually interacts with the autoregressive decoder which also has attention mechanisms that allow it to flexibly focus on the encoded input (as in the standard form of attention) and on the previously generated output. The Transformer—the name given to this new attention architecture—outperformed many previous models and quickly became the standard for machine translation as well as other tasks (Devlin et al., 2018 ).

Interestingly, self-attention has less in common with biological attention than the recurrent attention models originally used for machine translation. First, it reduces the role of recurrence and dynamics, whereas the brain necessarily relies on recurrence in sequential processing tasks, including language processing and attentional selection. Second, self-attention provides a form of horizontal interaction between words—which allows for words in the encoded sentence to be processed in the context of those around them—but this mechanism does not include an obvious top-down component driven by the needs of the decoder. In fact, self-attention has been shown under certain circumstances to simply implement a convolution, a standard feedforward computation frequently used in image processing (Andreoli, 2019 ; Cordonnier et al., 2019 ). In this way, self-attention is more about creating a good encoding than performing a task-specific attention-like selection based on limited resources. In the context of a temporal task, its closest analogue in psychology may be priming because priming alters the encoding of subsequent stimuli based on those that came before. It is of course not the direct goal of machine learning engineers to replicate the brain, but rather to create networks that can be easily trained to perform well on tasks. These different constraints mean that even large advances in machine learning do not necessarily create more brain-like models.

While the study of attention in human language processing is not as large as other areas of neuroscience research, some work has been done to track eye movements while reading (Myachykov and Posner, 2005 ). They find that people will look back at previous sections of text in order to clarify what they are currently reading, particularly in the context of finding the antecedent of a pronoun. Such shifts in overt attention indicate what previous information is most relevant for the current processing demands.

3.2. Attention for Visual Tasks

As in neuroscience and psychology, a large portion of studies in machine learning are done on visual tasks. One of the original attention-inspired tools of computer vision is the saliency map, which identifies which regions in an image are most salient based on a set of low-level visual features such as edges, color, or depth and how they differ from their surround (Itti and Koch, 2001 ). In this way, saliency maps indicate which regions would be captured by “bottom-up” attention in humans and animals. Computer scientists have used saliency maps as part of their image processing pipeline to identify regions for further processing.

In more recent years, computer vision models have been dominated by deep learning. And since their success in the 2012 ImageNet Challenge (Russakovsky et al., 2015 ), convolutional neural networks have become the default architecture for visual tasks in machine learning.

The architecture of convolutional neural networks is loosely based on the mammalian visual system (Lindsay, 2020 ). At each layer, a bank of filters is applied to the activity of the layer below (in the first layer this is the image). This creates a H × W × C tensor of neural activity with the number of channels, C equal to the number of filters applied and H and W representing the height and width of the 2-D feature maps that result from the application of a filter.

Attention in convolutional neural networks has been used to enhance performance on a variety of tasks including classification, segmentation, and image-inspired natural language processing. Also, as in the neuroscience literature, these attentional processes can be divided into spatial and feature-based attention.

3.2.1. Spatial Attention

Building off of the structures used for attention in NLP tasks, visual attention has been applied to image captioning. In Xu et al. ( 2015 ), the encoding model is a convolutional neural network. The attention mechanism works over the activity at the fourth convolutional layer. As each word of the caption is generated, a different pattern of weighting across spatial locations of the image representation is created. In this way, attention for caption generation replaces the set of encoded word vectors in a translation task with a set of encoded image locations. Visualizing the locations with high weights, the model appears to attend to the object most relevant to the current word being generated for the caption.

This style of attention is referred to as “soft” because it produces a weighted combination of the visual features over spatial locations ( Figure 4B ). “Hard” attention is an alternative form that chooses a single spatial location to be passed into the decoder at the expense of all others ( Figure 4A ). In Xu et al. ( 2015 ), to decide which location should receive this hard attention, the attention weights generated for each spatial location were treated as probabilities. One location is chosen according to these probabilities. Adding this stochastic element to the network makes training more difficult, yet it was found to perform somewhat better than soft attention.

Hard vs. soft visual attention in artificial neural networks. (A) In hard attention, the network only gets input from a small portion of the whole image. This portion is iteratively chosen by the network through an attention selection mechanism. If the input is foveated, the network can use the lower resolution periphery to guide this selection. (B) Feature maps in convolutional neural networks are 2-D grids of activation created by the application of a filter to the layer below. In soft spatial attention, different locations on these grids are weighted differently. In soft feature attention, different feature maps are weighted differently.

A 2014 study used reinforcement learning to train a hard attention network to perform object recognition in challenging conditions (Mnih et al., 2014 ). The core of this model is a recurrent neural network that both keeps track of information taken in over multiple “glimpses” made by the network and outputs the location of the next glimpse. For each glimpse, the network receives a fovea-like input (central areas are represented with high resolution and peripheral with lower) from a small patch of the image. The network has to integrate the information gained from these glimpses to find and classify the object in the image. This is similar to the hard attention described above, except the selection of a location here determines which part of the image is sampled next (whereas in the case above it determined which of the already-processed image locations would be passed to the decoder). With the use of these glimpses, the network is not required to process all of the image, saving computational resources. It can also help when multiple objects are present in the image and the network must classify each (Ba et al., 2014 ). Recent work has shown that adding a pre-training step enhances the performance of hard attention applied to complex images (Elsayed et al., 2019 ).

In many ways, the correspondence between biological and artificial attention is strongest when it comes to visual spatial attention. For example, this form of hard attention—where different locations of the image are sequentially-sampled for further processing—replicates the process of saccading and is therefore akin to overt visual attention in the neuroscience and psychology literature. Insofar as soft attention dynamically re-weights different regions of the network's representation of the image without any change in the input to the network, it is akin to covert spatial attention. Also, as the mode of application for soft attention involves multiplicative scaling of the activity of all units at a specific location, it replicates neural findings about covert spatial attention.

Soft spatial attention has been used for other tasks, including visual question and answering (Chen et al., 2015 ; Xu and Saenko, 2016 ; Yang et al., 2016 ) and action recognition in videos (Sharma et al., 2015 ). Hard attention has also been used for instance segmentation (Ren and Zemel, 2017 ) and for fine-grained classification when applied using different levels of image resolution (Fu et al., 2017 ).

3.2.2. Feature Attention

In the case of soft spatial attention, weights are different in different spatial locations of the image representation yet they are the same across all feature channels at that location. That is, the activity of units in the network representing different visual features will all be modified the same way if they represent the same location in image space. Feature attention makes it possible to dynamically re-weight individual feature maps, creating a spatially global change in feature processing.

In Stollenga et al. ( 2014 ), a convolutional neural network is equipped with a feature-based attention mechanism. After an image is passed through the standard feedforward architecture, the activity of the network is passed into a policy that determines how the different feature maps at different layers should be weighted. This re-weighting leads to different network activity which leads to different re-weightings. After the network has run for several timesteps the activity at the final layer is used to classify the object in the image. The policy that determines the weighting values is learned through reinforcement learning, and can be added to any pre-trained convolutional neural network.

The model in Chen et al. ( 2017 ) combines feature and spatial attention to aid in image captioning. The activity of the feedforward pass of the convolutional network is passed into the attention mechanism along with the previously generated word to create attention weightings for different channels at each layer in the CNN. These weights are used to scale activity and then a separate attention mechanism does the same procedure for generating spatial weightings. Both spatial and feature attention weights are generated and applied to the network at each time point.

In the model in De Vries et al. ( 2017 ), the content of a question is used to control how a CNN processes an image for the task of visual question and answering. Specifically, the activity of a language embedding network is passed through a multi-layer perceptron to produce the additive and multiplicative parameters for batch normalization of each channel in the CNN. This procedure, termed conditional batch normalization, functions as a form of question-dependent feature attention.

A different form of dynamic feature re-weighting appears in “squeeze-and-excitation” networks (Hu et al., 2018 ). In this architecture, the weightings applied to different channels are a nonlinear function of the activity of the other channels at the same layer. As with “self-attention” described above, this differs in spirit from more “top-down” approaches where weightings are a function of activity later in the network and/or biased by the needs of the output generator. Biologically speaking, this form of interaction is most similar to horizontal connections within a visual area, which are known to carry out computations such as divisive normalization (Carandini and Heeger, 2012 ).

In the study of the biology of feature-based attention, subjects are usually cued to attend to or search for specific visual features. In this way, the to-be-attended features are known in advance and relate to the specific sub-task at hand (e.g., detection of a specific shape on a given trial of a general shape detection task). This differs from the above instances of artificial feature attention, wherein no external cue biases the network processing before knowledge about the specific image is available. Rather, the feature re-weighting is a function of the image itself and meant to enhance the performance of the network on a constant task (note this was also the case for the forms of artificial spatial attention described).

The reason for using a cueing paradigm in studies of biological attention is that it allows the experimenter to control (and thus know) where attention is placed. Yet, it is clear that even without explicit cueing, our brains make decisions about where to place attention constantly; these are likely mediated by local and long-range feedback connections to the visual system (Wyatte et al., 2014 ). Therefore, while the task structure differs between the study of biological feature attention and its use in artificial systems, this difference may only be superficial. Essentially, the artificial systems are using feedforward image information to internally generate top-down attentional signals rather than being given the top-down information in the form of a cue.

That being said, some artificial systems do allow for externally-cued feature attention. For example setting a prior over categories in the network in Cao et al. ( 2015 ) makes it better at localizing the specific category. The network in Wang et al. ( 2014 ), though not convolutional, has a means of biasing the detection of specific object categories as well. And in Lindsay and Miller ( 2018 ), several performance and neural aspects of biological feature attention during a cued object detection task were replicated using a CNN. In Luo et al. ( 2020 ), the costs and benefits of using a form of cued attention in CNNs were explored.

As mentioned above, the use of multiplicative scaling of activity is in line with certain findings from biological visual attention. Furthermore, modulating entire feature maps by the same scalar value is aligned with the finding mentioned above that feature attention acts in a spatially global way in the visual system.

3.3. Multi-Task Attention

Multi-task learning is a challenging topic in machine learning. When one network is asked to perform several different tasks—for example, a CNN that must classify objects, detect edges, and identify salient regions—training can be difficult as the weights needed to do each individual task may contradict each other. One option is have a set of task-specific parameters that modulate the activity of the shared network differently for each task. While not always called it, this can reasonably be considered a form of attention, as it flexibly alters the functioning of the network.

In Maninis et al. ( 2019 ), a shared feedforward network is trained on all of multiple tasks, while task specific skip connections and squeeze-and-excitation blocks are trained to modulate this activity only on their specific task. This lets the network benefit from sharing processing that is common to all tasks while still specializing somewhat to each.

A similar procedure was used in Rebuffi et al. ( 2017 ) to create a network that performs classification on multiple different image domains. There, the domain could be identified from the input image making it possible to select the set of task-specific parameters automatically at run-time.

In Zhao et al. ( 2018 ), the same image can be passed into the network and be classified along different dimensions (e.g. whether the person in the picture is smiling or not, young or old). Task-specific re-weighting of feature channels is used to execute these different classifications.

The model in Strezoski et al. ( 2019 ) uses what could be interpreted as a form of hard feature attention to route information differently in different tasks. Binary masks over feature channels are chosen randomly for each task. These masks are applied in a task-specific way during training on all tasks and at run-time. Note that in this network no task-specific attentional parameters are learned, as these masks are pre-determined and fixed during training. Instead, the network learns to use the different resulting information pathways to perform different tasks.

In a recent work, the notion of task-specific parameters was done away with entirely (Levi and Ullman, 2020 ). Instead, the activations of a feedforward CNN are combined with a task input and passed through a second CNN to generate a full set of modulatory weights. These weights then scale the activity of the original network in a unit-specific way (thus implementing both spatial and feature attention). The result is a single set of feedforward weights capable of flexibly engaging in multiple visual tasks.

When the same input is processed differently according to many different tasks, these networks are essentially implementing a form of within-modality task switching that relies on feature attention. In this way, it is perhaps most similar to the Stroop test described previously.

3.4. Attention to Memory

Deep neural networks tend not to have explicit memory, and therefore attention to memory is not studied. Neural Turing Machines, however, are a hybrid neural architecture that includes external memory stores (Graves et al., 2014 ). The network, through training, learns how to effectively interact with these stores to perform tasks such as sorting and repetition of stored sequences. Facilitating this interaction is a form of attention. Memories are stored as a set of vectors. To retrieve information from this store, the network generates a weight for each vector and calculates a weighted sum of the memories. To determine these weights, a recurrent neural network (which receives external and task-relevant input) outputs a vector and memories are weighted in accordance to their similarity to this vector. Thus, at each point in time, the network is able to access context-relevant memories.

As described previously, how the brain chooses what memories to attend to and then attends to them is not entirely clear. The use of a similarity metric in this model means that memories are retrieved based on their overlap with a produced activity vector, similar to associative memory models in the neuroscience literature. This offers a mechanism for the latter question—that is, how attention to memory could be implemented in the brain. The activity vector that the model produces controls what memories get attended and the relationship with biology is less clear here.

4. Ideas for Future Interaction Between Artificial and Biological Attention

As has been shown, some amount of inspiration from biology has already led to several instances of attention in artificial neural networks (summarized in Figure 5 ). While the addition of such attention mechanisms has led to appreciable increases in performance in these systems, there are clearly still many ways in which they fall short and additional opportunities for further inspiration exist. In the near term, this inspiration will likely be in the form of incremental improvements to specialized artificial systems as exist now. However, the true promise of brain-inspired AI should deliver a more integrated, multiple-purpose agent that can engage flexibly in many tasks.

An incomplete summary of the different types of attention studied in neuroscience/psychology and machine learning and how they relate. On the left are divisions of attention studied biologically, on the right are those developed for artificial intelligence and machine learning. Topics at the same horizontal location are to some extent analogous, with the distance between them indicating how close the analogy is. Forms of visual attention, for example, have the most overlap and are the most directly comparable across biology and machine learning. Some forms of attention, such as overall arousal, don't have an obvious artificial analogue.

4.1. How to Enhance Performance

There are two components to the study of how attention works in the brain that can be considered flip sides of the same coin. The first is the question of how attention enhances performance in the way that it does—that is, how do the neural changes associated with attention make the brain better at performing tasks. The second is how and why attention is deployed in the way that it is—what factors lead to the selection of certain items or tasks for attention and not others.

Neuroscientists have spent a lot of time investigating the former question. In large part, the applicability of these findings to artificial neural systems, however, may not be straightforward. Multiplicative scaling of activity appears in both biological and artificial systems and is an effective means of implementing attention. However, many of the observed effects of attention in the brain make sense mainly as a means of increasing the signal carried by noisy, spiking neurons. This includes increased synchronization across neurons and decreased firing variability. Without analogs for these changes in deep neural networks, it is hard to take inspiration from them. What's more, the training procedures for neural networks can automatically determine the changes in activity needed to enhance performance on a well-defined task and so lessons from biological changes may not be as relevant.

On the other hand, the observation that attention can impact spiking-specific features such as action potential height, burstiness, and precise spike times may indicate the usefulness of spiking networks. Specifically, spiking models offer more degrees of freedom for attention to control and thus allow attention to possibly have larger and/or more nuanced impacts.

Looking at the anatomy of attention may provide usable insights to people designing architectures for artificial systems. For example, visual attention appears to modulate activity more strongly in later visual areas like V4 (Noudoost et al., 2010 ), whereas auditory attention can modulate activity much earlier in the processing stream. The level at which attention should act could thus be a relevant architectural variable. In this vein, recent work has shown that removing self-attention from the early layers of a Transformer model enhances its performance on certain natural language processing tasks and also makes the model a better predictor of human fMRI signals during language processing (Toneva and Wehbe, 2019 ).

The existence of cross-modal cueing—wherein attention cued in one sensory modality can cause attention to be deployed to the same object or location in another modality—indicates some amount of direct interaction between different sensory systems. Whereas many multi-modal models in machine learning use entirely separate processing streams that are only combined at the end, allowing some horizontal connections between different input streams may help coordinate their processing.

Attention also interacts with the kind of adaptation that normally occurs in sensory processing. Generally, neural network models do not have mechanisms for adaptation—that is, neurons have no means of reducing their activity if given the same input for multiple time steps. Given that adaptation helps make changes and anomalies stand out, it may be useful to include. In a model with adaption, attention mechanisms should work to reactivate adapted neurons if the repeated stimulus is deemed important.

Finally, some forms of attention appear to act in multiple ways on the same system. For example, visual attention is believed to both: (1) enhance the sensitivity of visual neurons in the cortex by modulating their activity and (2) change subcortical activity such that sensory information is readout differently (Birman and Gardner, 2019 ; Sreenivasan and Sridharan, 2019 ). In this way, attention uses two different mechanisms, in different parts of the brain, to create its effect. Allowing attention to modulate multiple components of a model architecture in complementary ways may allow it to have more robust and effective impacts.

4.2. How to Deploy Attention

The question of how to deploy attention is likely the more relevant challenge for producing complex and integrated artificial intelligence. Choosing the relevant information in a stream of incoming stimuli, picking the best task to engage in, or deciding whether to engage in anything at all requires that an agent have an integrative understanding of its state, environment, and needs.

The most direct way to take influence from biological attention is to mimic it directly. Scanpath models, for example, have existed in the study of saliency for many years. They attempt to predict the series of fixations that humans make while viewing images (Borji and Itti, 2019 ). A more direct approach to training attention was used in Linsley et al. ( 2018 ). Here, a large dataset of human top-down attention was collected by having subjects label the regions of images most relevant for object classification. The task-specific saliency maps created through this method were used to train attention in a deep convolutional neural network whose main task was object recognition. They found that influencing the activity of intermediate layers with this method could increase performance. Another way of learning a teacher's saliency map was given in Zagoruyko and Komodakis ( 2016 ).

Combined training on tasks and neural data collected from human visual areas has also helped the performance of CNNs (Fong et al., 2018 ). Using neural data collected during attention tasks in particular could help train attention models. Such transfer could also be done for other tasks. For example, tracking eye movements during reading could inform NLP models; thus far, eye movements have been used to help train a part-of-speech tagging model (Barrett et al., 2016 ). Interestingly, infants may learn from attending to what adults around them attend to and the coordination of attention more broadly across agents may be very helpful in a social species. Therefore, the attention of others should influence how attention is guided. Attempts to coordinate joint attention will need to be integrated into attention systems (Kaplan and Hafner, 2006 ; Klein et al., 2009 ).

Activities would likely need to flexibly decide which of several possible goals should be achieved at any time and therefore where attention should be placed. This problem clearly interacts closely with issues around reinforcement learning—particularly hierarchical reinforcement learning which involves the choosing of subtasks—as such decisions must be based on expected positive or negative outcomes. Indeed, there is a close relationship between attention and reward as previously rewarded stimuli attract attention even in contexts where they no longer provide reward (Camara et al., 2013 ). A better understanding of how humans choose which tasks to engage in and when should allow human behavior to inform the design of a multi-task AI.

To this end, the theory put forth in Shenhav et al. ( 2013 ), which says that allocation of the brain's limited ability to control different processes is based on the expected value of that control, may be of use. In this framework, the dorsal anterior cingulate cortex is responsible for integrating diverse information—including the cognitive costs of control—in order to calculate the expected value of control and thus direct processes like attention. Another approach for understanding human executive control in complex tasks is inverse reinforcement learning. This method was recently applied to a dataset of eye movements during visual search in order to determine the reward functions and policies used by humans (Zelinsky et al., 2020 ).

An additional factor that drives biological attention but is perhaps underrepresented in artificial attention systems is curiosity (Gottlieb et al., 2013 ). In biology, novel, confusing, and surprising stimuli can grab attention, and inferotemporal and perirhinal cortex are believed to signal novel visual situations via an adaptation mechanism that reduces responses to familiar inputs. Reinforcement learning algorithms that include novelty as part of the estimate of the value of a state can encourage this kind of exploration (Jaegle et al., 2019 ). How exactly to calculate surprise or novelty in different circumstances is not always clear, however. Previous work on biological attention has understood attention selection in Bayesian terms of surprise or information gathering and these framings may be useful for artificial systems (Itti and Baldi, 2006 ; Mirza et al., 2019 ).

A final issue in the selection of attention is how conflicts are resolved. Given the brain's multiple forms of attention—arousal, bottom-up, top-down, etc.—how do conflicts regarding the appropriate locus of attention get settled? Looking at the visual system, it seems that the local circuits that these multiple systems target are burdened with this task. These circuits receive neuromodulatory input along with top-down signals which they must integrate with the bottom-up input driving their activity. Horizontal connections mediate this competition, potentially using winner-take-all mechanisms. This can be mimicked in the architecture of artificial systems.

4.3. Attention and Learning

Attention, through its role in determining what enters memory, guides learning. Most artificial systems with attention include the attention mechanism throughout training. In this way, the attention mechanism is trained along with the base architecture; however, with the exception of the Neural Turing Machine, the model does not continue learning once the functioning attention system is in place. Therefore, the ability of attention to control learning and memory is still not explicitly considered in these systems.

Attention could help make efficient use of data by directing learning to the relevant components and relationships in the input. For example, saliency maps have been used as part of the pre-processing for various computer vision tasks (Lee et al., 2004 ; Wolf et al., 2007 ; Bai and Wang, 2014 ). Focusing subsequent processing only on regions that are intrinsically salient can prevent wasteful processing on irrelevant regions and, in the context of network training, could also prevent overfitting to these regions. Using saliency maps in this way, however, requires a definition of saliency that works for the problem at hand. Using the features of images that capture bottom-up attention in humans has worked for some computer vision problems; looking at human data in other modalities may be useful as well.

In a related vein, studies on infants suggest that they have priors that guide their attention to relevant stimuli such as faces. Using such priors could bootstrap learning both of how to process important stimuli and how to better attend to their relevant features (Johnson, 2001 ).

In addition to deciding which portions of the data to process, top-down attention can also be thought of as selecting which elements of the network should be most engaged during processing. Insofar as learning will occur most strongly in the parts of the network that are most engaged, this is another means by which attention guides learning. Constraining the number of parameters that will be updated in response to any given input is an effective form of regularization, as can be seen in the use of dropout and batch normalization. Attention—rather than randomly choosing which units to engage and disengage—is constrained to choose units that will also help performance on this task. It is therefore a more task-specific form of regularization.

In this way, attention may be particularly helpful for continual learning where the aim is to update a network to perform better on a specific task while not disrupting performance on the other tasks the network has already learned to do. A related concept, conditional computation, has recently been applied to the problem of continual learning (Lin et al., 2019 ). In conditional computation, the parameters of a network are a function of the current input (it can thus be thought of as an extreme form of the type of modulation done by attention); optimizing the network for efficient continual learning involves controlling the amount of interference between different inputs. More generically, it may be helpful to think of attention, in part, as a means of guarding against undesirable synaptic changes.

Attention and learning also work in a loop. Specifically, attention guides what is learned about the world and internal world models are used to guide attention. This inter-dependency has recently been formalized in terms of a reinforcement learning framework that also incorporates cognitive Bayesian inference models that have succeeded in explaining human learning and decision making (Radulescu et al., 2019 ). Interconnections between basal ganglia and prefrontal cortex are believed to support the interplay between reinforcement learning and attention selection.

At a more abstract level, the mere presence of attention in the brain's architecture can influence representation learning. The global workspace theory of consciousness says that at any moment a limited amount of information selected from the brain's activity can enter working memory and be available for further joint processing (Baars, 2005 ). Inspired by this, the ‘consciousness prior' in machine learning emphasizes a neural network architecture with a low-dimensional representation that arises from attention applied to an underlying high-dimensional state representation (Bengio, 2017 ). This low-D representation should efficiently represent the world at an abstract level such that it can be used to summarize and make predictions about future states. The presence of this attention-mediated bottleneck has a trickle-down effect that encourages disentangled representations at all levels such that they can be flexibly combined to guide actions and make predictions.

Conscious attention is required for the learning of many complex skills such as playing a musical instrument. However once fully learned, these processes can become automatic, possibly freeing attention up to focus on other things (Treisman et al., 1992 ). The mechanisms of this transformation are not entirely clear but insofar as they seem to rely on moving the burden of the task to different, possibly lower/more reflexive brain areas, it may benefit artificial systems to have multiple redundant pathways that can be engaged differently by attention (Poldrack et al., 2005 ).

4.4. Limitations of Attention: Bugs or Features?

Biological attention does not work perfectly. As mentioned above, performance can suffer when switching between different kinds of attention, arousal levels need be just right in order to reach peak performance, and top-down attention can be interrupted by irrelevant but salient stimuli. A question when transferring attention to artificial systems is are these limitations bugs to be avoided or features to be incorporated?

Distractability, in general, seems like a feature of attention rather than a bug. Even when attempting to focus on a task it is beneficial to still be aware of—and distractable by—potentially life-threatening changes in the environment. The problem comes only when an agent is overly distractable to inputs that do not pose a threat or provide relevant information. Thus, artificial systems should balance the strength of top down attention such that it still allows for the processing of unexpected but informative stimuli. For example, attentional blink refers to the phenomenon wherein a subject misses a second target in a stream of targets and distractors if it occurs quickly after a first target (Shapiro et al., 1997 ). While this makes performance worse, it may be necessary to give the brain time to process and act on the first target. In this way, it prevents distractability to ensure follow through.

Any agent, artificial or biological, will have some limitations on its energy resources. Therefore, prudent decisions about when to engage in the world versus enter an energy-saving state such as sleep will always be of relevance. For many animals sleep occurs according to a schedule but, as was discussed, it can also be delayed or interrupted by attention-demanding situations. The decision about when to enter a sleep state must thus be made based on a cost-benefit analysis of what can be gained by staying awake. Because sleep is also known to consolidate memories and perform other vital tasks beyond just energy conservation, this decision may be a complex one. Artificial systems will need to have an integrative understanding of their current state and future demands to make this decision.

5. Conclusions

Attention is a large and complex topic that sprawls across psychology, neuroscience, and artificial intelligence. While many of the topics studied under this name are non-overlapping in their mechanisms, they do share a core theme of the flexible control of limited resources. General findings about flexibility and wise uses of resources can help guide the development of AI, as can specific findings about the best means of deploying attention to specific sensory modalities or tasks.

Author Contributions

GL conceived and wrote the article and generated the figures.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MR declared a past co-authorship with the author GL to the handling Editor.

Acknowledgments

The author would like to thank Jacqueline Gottlieb and the three reviewers for their insights and pointers to references.

Funding. This work was supported by a Marie Skłodowska-Curie Individual Fellowship (No. 844003) and a Sainsbury Wellcome Centre/Gatsby Computational Unit Fellowship.

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• Open access
• Published: 24 June 2024

Effects of memory and attention on the association between video game addiction and cognitive/learning skills in children: mediational analysis

• Amani Ali Kappi 1 ,
• Rania Rabie El-Etreby 2 ,
• Ghada Gamal Badawy 3 ,
• Gawhara Ebrahem 3 &
• Warda El Shahat Hamed 2  

BMC Psychology volume  12 , Article number:  364 ( 2024 ) Cite this article

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Metrics details

Video games have become a prevalent source of entertainment, especially among children. Furthermore, the amount of time spent playing video games has grown dramatically. The purpose of this research was to examine the mediation effects of attention and child memory on the relationship between video games addiction and cognitive and learning abilities in Egyptian children.

A cross-sectional research design was used in the current study in two schools affiliated with Dakahlia District, Egypt. The study included 169 children aged 9 to 13 who met the inclusion criteria, and their mothers provided the questionnaire responses. The data collection methods were performed over approximately four months from February to May. Data were collected using different tools: Socio-demographic Interview, Game Addiction Scale for Children (GASC), Children’s Memory Questionnaire (CMQ), Clinical Attention Problems Scale, Learning, Executive, and Attention Functioning (LEAF) Scale.

There was a significant indirect effect of video game addiction on cognitive and learning skills through attention, but not child memory. Video game addiction has a significant impact on children’s attention and memory. Both attention and memory have a significant impact on a child’s cognitive and learning skills.

Conclusions

These results revealed the significant effect of video game addiction on cognitive and learning abilities in the presence of mediators. It also suggested that attention-focused therapies might play an important role in minimizing the harmful effects of video game addiction on cognitive and learning abilities.

Peer Review reports

Introduction

The use of video games has increased significantly in recent years. Historically, such games are used more often by children. Despite the positive impacts of video games on socialization and enjoyment, empirical and clinical research has consistently demonstrated that many children can become addicted due to excessive use. Among Arab children and adolescents, the prevalence of video game addiction is 62% of 393 adolescents in Saudi Arabia, 5% in Jordan, 6% in Syria, and 7.8% in Kuwait [ 1 , 2 ]. The varying incidence rates can be attributable to variations in the research population, cultural determinants, and evaluation or diagnostic standards.

In addition, video games, the internet, and other new technologies have become children’s top leisure pursuits. Today, they comprise a virtual environment in which thousands of gamers simultaneously participate worldwide; rather than being a personal or lonely leisure activity, they are often a group activity that establishes new social networks [ 3 ]. Although playing video games in moderation can have many positive effects, their exploitation may lead to addictions and societal issues [ 4 ]. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), identifies repetitive and persistent behavior related to online video games as the core element of addiction. This behavior should persist for at least 12 months and result in significant impairment. Additionally, addiction should be accompanied by psychological and social symptoms, as well as tolerance and withdrawal symptoms [ 5 ].

Different studies have examined the impact of video games on children’s cognitive abilities and school performance [ 6 , 7 ]. The recent literature has shown how video games affect the brain and alter its functioning while being played. It demonstrates how specific cortical and subcortical structures are involved [ 8 , 9 , 10 ]. Research indicates that excessive play of the same typees of games might negatively impact school-age children’s cognitive and academic skills as well as their capacity to maintain and enhance memories [ 7 ]. Possible consequences of video game addiction may include memory and attention-related difficulties [ 4 , 6 , 11 ]. For instance, children’s memory scores negatively correlated with greater levels of video game addiction in Lebanon [ 6 ]. Furthermore, studies show that action-game players are more likely to succeed at short-term concentration tests while they perform below average in long-term, less exciting activities. At the point of game addiction, difficulties with focus are likely to become much more apparent [ 12 ]. Studies show a substantial association between gaming addiction and inattention, even after controlling other variables such as personality factors, anxiety and depression symptoms, and attention deficit hyperactivity disorder [ 13 , 14 ].

Prior studies have illustrated the association between video game addiction and psychiatric disorders, social phobia, mental well-being, and risky health behaviors [ 15 , 16 , 17 ]. Another study shows an association between video game addiction and memory, attention, cognitive, and learning abilities among Lebanese children [ 18 ]. However, all of these studies explain the association without controlling for any history of mental or behavioral disorders such as ADHD, anxiety, or depression. However, to the best of our knowledge, a few studies have specifically investigated the effect of attention and child memory on the relationship between video game addiction and cognitive and learning abilities in Egyptian children. Therefore, this study aimed to explore the mediation effect of attention and child memory on the association between addiction to video game and cognitive and learning abilities among Egyptian children. Our hypotheses were: (1) child attention mediates the relationship between video game addiction and cognitive and learning abilities among Egyptian children; and (2) child memory mediates the relationship between video game addiction and cognitive and learning abilities among Egyptian children.

Literature review

Video games have transformed into complex experiences that embody principles recognized by psychologists, neuroscientists, and educators as crucial for behavior, learning, and cognitive functions. While video games offer social and entertainment benefits, extensive research indicates that their excessive use can lead to adverse psychological consequences and even addiction in a minority of players. Symptoms like impaired control over gaming and prioritizing games over daily responsibilities may signify gaming addiction [ 19 ].

The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) acknowledged video game addiction as an internet gaming disorder in its fifth edition, highlighting the need for further research [ 20 ]. Similarly, the 11th edition of the International Classification of Diseases (ICD-11) classified gaming disorder as a recurrent pattern of gaming behavior that encompasses both online and offline gaming [ 21 ]. Scientific evidence indicates that addictions can develop due to a combination of genetic susceptibility and repeated exposure to specific stimuli [ 22 ].

Growing public concerns have emerged regarding the potential negative impacts of video games, notably on children’s memory [ 23 ]. Individuals with various behavioral disorders and those with addictive tendencies often find their memory, crucial for comprehension and cognitive abilities like memory updating and working memory, compromised [ 24 ]. Although some research delves into video games’ effects on cognitive functions and academic achievement in children [ 25 , 26 ], the impact on memory remains a contentious topic.

Despite being a leisure activity, video gaming can pose issues for certain children, impacting their ability to focus. Meta-analysis and systematic reviews by Ho et al. and Carli et al. indicated a link between inattention and addiction to the internet and gaming [ 27 ]. Additionally, numerous studies corroborated this connection, demonstrating a robust correlation between the severity of inattention in ADHD and addiction to the internet or gaming. This correlation persisted even after controlling for factors such as depression and anxiety symptoms, as well as personality traits [ 27 ].

Study design and sample

This study has a cross-sectional descriptive design. It was conducted in two convienient selected preparatory schools, Emam Mohamed Abdo Preparatory and Omar Ibn Elkhatab Preparatory School. The two schools are affiliated with xxx. The participants were selected at random from the list of school principals. The research was open to all students between the ages of 9 and 13 with no history of physical, mental, or cognitive disorders. Each student’s parents provided the questionnaire responses. Using the G-power software 3.1.9.2, the study’s sample size was determined. Based on an average effect size of f = 0.15, a 2-sides test at alpha = 0.05, a statistical power (1-β) of 0.95, and eight predictors (age, gender, educational level of the child and mother, video game addiction, memory, attention, and learning abilities), power analysis was performed. A minimum of 166 participants were required based on these criteria.

Ethical consideration

The study approved by the Research Ethics Committee (REC) of Mansoura University’s Faculty of Nursing (IRB P0506/9/8/2023). The study’s purpose, methodology, duration, and benefits were also explained to the directors of the two selected institutions. Mothers’ consents obtained after explaining the study’s objective and the data kept confidential. The participants were informed that they had their right to withdraw from the study at any time.

Data Collection

The following tools were utilized in the study:

Socio-demographic questionnaire

Child and mother’s information was collected, such as age, sex, number of children, and level of education.

• Video game addiction

We used the Game Addiction Scale for Children (GASC) to measure children’s video game addiction. The GASC developed by Yılmaz, Griffiths [ 28 ] according to DSM criteria to evaluate gaming addiction. It includes 21 self-reported items rated on five-point Likert scale (from 1 = never to 5 = very frequently), where higher score shows more hazardous online gaming usage. An individual’s total score can range from a minimum of 21 to a maximum of 105; a score above 90 may be a sign of a video game addiction. It is also emphasized that this is not a diagnostic tool, however, but merely an indicator that a child may have a gaming addiction. Such a diagnosis could only be made by a comprehensive clinical evaluation. Seven criteria for video game addiction are determined by the scale: salience, tolerance, mood modification, withdrawal, relapse, conflict, and issues. The scale shows an acceptable internal consistency reliability ( r  = 0.89, p  < 0.001) [ 19 ].

Children’s memory

We used the Children’s Memory questionnaire (CMQ) to assess children’s memory rated by their parents. The CMQ developed by Drysdale, Shores [ 29 ]. It included 34 items that rated on a five-point Likert scale ranging from 1 = never or almost never, to 5 = more than once a day. Higher scores indicate a more significant reduction in the cognitive domain. The scale is divided into three subscales: working memory and attention, visual memory, and episodic memory. The Cronbach alpha value for the episodic memory subscale was 0.88, the visual memory is 0.77, and the working memory is 0.84 [ 29 ].

Attention of children

The Clinical Attention Problems Scale was used to measure children’s attention level in the morning and afternoon. This scale was developed by Edelbrock and Rancurello [ 30 ] and includes 12 items. The possible responses are 0 = not true, 1 = somewhat or sometimes true, and 2 = very often or often true. The higher the scores, the more attention there is. The Cronbach alpha values for the clinical attention problem in the morning is 0.84 and for the afternoon is 0.83.

Cognitive and learning skills

We used the Learning, Executive, and Attention Functioning (LEAF) scale to measure children’s cognitive and learning skills. The LEAF scale is a self-reported 55 items scale developed by Castellanos, Kronenberger [ 31 ]. The scale assesses core cognitive abilities and related academic and learning abilities. The LEAF assesses cognitive skills such as attention, processing speed, working memory, sustained sequential processing to accomplish goals (such as planning and carrying out goal-directed tasks), and new problem-solving. Moreover, the LEAF approach takes into account academic functioning, declarative/factual memory, and understanding and concept formulation.

The LEAF includes 55 items, with 11 academic subscales that rate a person’s reading, writing, and mathematics proficiency. The LEAF is divided into subscales that measure comprehension and conceptual learning, factual memory, attention, processing speed, visual-spatial organization, sustained sequential processing, working memory, new problem-solving, mathematics, basic reading, and written expression skills. Each subscale has the same number of items. The responses were rated on a three-point scale ranging from 0 to 3. Higher scores indicate more significant issues with cognition. The five component items are added to provide the subscale score for each of the 11 subject areas. Three criterion-referenced ranges are established for the interpretation of LEAF subscale raw scores. Out of nine, a score of five to nine is classified as the “borderline problem range,” a score of less than five as the “no problem range,” and a score of nine or above as the “problem range.” The Cronbach alpha value for the LEAF scale is 0.96.

Validity and reliability

Study tools were translated into Arabic by the researchers. Five pediatric nursing and psychiatric and mental health nursing experts tested them for content validity. At first, the scales were translated into Arabic using a forward and backward translation method. The translated questionnaires were then adapted to fit Arabic cultural norms. Two highly proficient native Arabic speakers who are accomplished academics in the fields of psychiatry and mental health nursing, and hold the academic status of Full Professor translated the questionnaire from English to Arabic. An English-language expert who is fluent in Arabic back translated the Arabic version. Native Arabic speakers who were not involved in the translation process verified the final translation. The forward-to-back translation process was repeated until the comparative findings matched exactly. The questionnaires were then given to three Arabic psychiatric nursing professionals, who provided their opinions on its importance, relevance, and simplicity. The tools’ reliability was tested using Cronbach’s alpha test (tool I α = 0.86, tool II α = 0.81, tool III α = 0.95, and tool IV α = 0.95, respectively). Additionally, a confirmatory factor analysis were carried out to validate the content of the four scales after translation. The data collection methods were performed over approximately four months from February to May. Also, a pilot study was conducted to assess the study tools’ feasibility and determine the time required to complete the tools. 10% of the initial participants were randomly selected from the same schools. Minimal modifications were then made to the tools. Mothers of students who participated in the pilot study were excluded from the primary study. The data was collected for four months (February to May). An online Google form was created to collect data. The link was then shared with selected student parents through WhatsApp groups. The link outlined the study’s purpose and methods, and participants signed a consent form.

Data collection procedure

We obtained permission to translate the study scales into Arabic. We collected data from February to May using an online Google Form for four months. The Google Form included full details regarding the study’s aims and processes to ensure transparency and establish participants’ trust. An extensive description of the response process additionally supports the Attention Problems Scale. For instance, mothers are required to respond to the items and their relevance to their children in the morning and afternoon. We distributed the survey link to the selected students’ mothers through WhatsApp groups as it was convenient and widespread among the target demographic. Before proceeding to the survey questions, participants were required to read and sign this consent form to ensure that participants received information about the study and voluntarily consented.

Statistical analysis

We employed the Statistical Package for Social Science version 26 [ 23 ] to analyze the data. We analyzed the demographic data using descriptive statistics such as means, standard deviations, frequency, and percentages. In order to evaluate the mediator effects of memory and attention on the relationship between cognitive, academic, and learning skills and video gaming addiction, we ran the multiple regression PROCESS macro with 5,000 bootstraps in SPSS version 3.4 [ 24 ]. We also included confounding variables, such as the age of the child, gender, the age of the mother, education, and job status, as covariates in the mediation model.

Sample characteristics

There were 169 children their mothers responded to the study surveys. The children’s mean age was 13 (SD = 3.9), while the mothers’ mean age was 41 (SD = 7.1). According to mothers, the children were ranked third in their household. Most mothers (72%) said they lived in rural areas. About 61% of the families had at least three children. Half of the mothers had high school or less education, and more than half were unemployed. Most children were in middle school (72%), see Table  1 .

Study variables description

The mean scores for all scales are presented in Table  2 . The mean score of the video gaming addiction total scale was 61 ± 19.3, indicating a moderate level of addiction. The attention total scale mean was 9 ± 6.50, indicating moderate attention problems. The mean score on the total scale for child memory was 80 ± 31,4, indicating moderate memory issues. Eight subscales of the LEAF had mean scores of 5: factual memory, processing speed, visual-spatial organization, sustained sequential processing, working memory, novel problem-solving, mathematics skills, and written expression skills. These mean scores indicate that a borderline problem exists. However, the mean scores for the comprehension and conceptual learning subscale, attention subscale, and basic reading skills subscale were below five, indicating that there was no problem.

Mediating effect of memory, attention problem on the association between video gaming addiction and cognitive, learning, and academic skills

Video game addiction had a significant impact on attention problems (b = 0.34, p  < 0.001; a1), and child memory (b = 0.18, p  < 0.001; a2). In turn, both attention problems (b = 0.48, p  < 0.001; b1) and child memory (b = 0.38, p  < 0.001; b2) had significant impact on cognitive and learning skills. The results reveal a significant indirect effect of video game addiction on cognitive and learning skills through attention problems (b = 0.17, CI: 0.82, 0.25; c ’ 1). However, there was no significant indirect effect of video game addiction on cognitive and learning skills through child memory (b = 0.07, CI: -0.01, 0.16; c ’ 2). The analysis revealed that confounding variables had no significant effect on the direct or indirect pathways linking video game addiction to cognitive and learning skills. The direct effect of video game addiction on cognitive and learning skills in the presence of the mediators was also found to be significant (b = 0.11, CI: 0.008, 0.401; c ’ -c). Figure  1 displays the mediation analysis findings.

Mediation effect of attention problem and child memory on the association between video gaming addiction and cognitive and learning skills

Previous research has explored the relationship between video game addiction, attention, and memory. Some studies have focused on the relationship between video game addiction and cognitive and learning skills. Others have examined the association between video gaming addiction and all other variables (attention, memory, learning, and cognitive skills). However, no study has explicitly examined the direct and indirect effect of video gaming addiction on learning and cognitive skills through the mediation effect of attention and memory.

This study was done on a sample of Egyptian school children to evaluate the mediation effect of attention and memory on the relationship between video game addiction and cognitive and learning abilities in children. The present study reveals that a gaming addiction can significantly impact attention and memory. This result agrees with Farchakh, Haddad [ 6 ], who conducted a study on a group of Lebanese school children aged 9 to 13 to investigate the association between gaming addiction, attention, memory, cognitive, and learning skills. They found that a greater degree of addiction to video gaming was significantly associated with worse attention scores and worse memory scores. An earlier study suggests that the link between inattention and video game addiction could be described by game genres’ immediate response and reward system. Alrahili, Alreefi [ 2 ] suggest that this may alleviate the boredom typically reported by inattentive users while simultaneously introducing a lack of responsiveness to real-world rewards. Another study on Turkish schoolchildren aged 10 to 16 years old revealed that the total recall scores of the subject group (children who regularly play video games) are significantly lower than those of the control group (children who do not regularly play video games; [ 7 ]).

The current study demonstrates that attention and child memory significantly impacted cognitive and learning skills. This agrees with the opinion of, Gallen, Anguera [ 32 ], who argues that children and young people process information differently, affecting the performance of various cognitive tasks. Additionally, this result disagrees with the findings of Ellah, Achor, and Enemarie [ 26 ], who have stated that students’ working memory has no statistically significant correlation with learning and problem-solving skills. Moreover, their same study showed that different measures of working memory can be attributed to a small variation in low-ability students’ problem-solving skills.

The results revealed a significant indirect effect of video game addiction on cognitive and learning skills through attention. This could be related to the relationship between attention and learning skills. Attention is an essential factor in the learning process because it helps a person make efficient use of data by directing their learning to relevant components and relationships in the input material. If a student can pay attention, they may be able to better retain and understand this material; if not, a lack of attention may lead to difficulties in learning and academic performance. As video gaming addiction affects students’ attention, it may directly affect learning skills [ 33 ]. Another study agrees with the current result, revealing that video game addiction negatively affects adolescents’ learning skills and grade point average [ 34 ].

A child’s memory has an effect on their cognitive and learning skills. Encoding, consolidating, and retrieving experiences and information are the foundation for learning new skills and knowledge [ 35 ]. Video game addiction affects children’s memory. Hence, the expectation is that video game addiction directly affects cognitive and learning skills. However, the present study reveals no significant indirect effect of video game addiction on cognitive and learning skills through child memory. For example, perceptual attention to the exterior world and reflective attention to interior memories need modification of shared representational components in the occipitotemporal cortex. This is shown in episodic memory by recovering an experience from memory, which includes reactivating some of the same sensory areas used during encoding. Furthermore, the prefrontal cortex involves continuous and reflecting attention [ 36 ]. The prefrontal cortex controls memory recall by choosing target memories and filtering or suppressing competing memories [ 36 ].

Another aspect that may be responsible for the absence of a mediating effect of memory on the association between video game addiction and cognitive and learning skills is the presence of the many factors that affect learning and cognitive skills besides memory alone. Life circumstances can affect learning skills rather than memory itself, for example. Problem solving (one of the learning skills) requires a brain that works effectively. Therefore, it is critical to address needs such as physical health, which is influenced by self-care needs such as diet, sleep, and relaxation, as well as children’s social and emotional needs. Furthermore, learning experiences that use all the senses, rather than only hearing or seeing information, result in effective and straightforward information retrieval from memory during problem-solving processes. Such abilities are supposed to be acquired by active participation in learning activities by children [ 37 ]. Finally, long-term focus on online gaming may eventually lead to neglect in learning, leading to a deterioration in learning performance [ 38 ].

Limitations

Our study has some limitations. First, we administered the Clinical Attention Problems Scale only once per student rather than conducting repeated measurements in the morning and afternoon. This approach overlooks potential daytime variations in attention levels, limiting our understanding of each child’s attentional profile. This choice was driven by practical considerations such as reducing the testing burden and participant fatigue. Future research could address this limitation by implementing repeated assessments to comprehend better daytime patterns in children’s attention levels and their implications for learning and behavior. Causality analysis was not possible due to the use of a cross-sectional sample. In addition, some results may be attributable to the small sample size. To fully understand the complex interplay between video game addiction and cognitive outcomes, longitudinal studies and controlled experiments are necessary to provide more conclusive insights into the relationship. It was difficult to include both parents in the study, as most of the fathers said they were too busy to participate. Hence, mothers were the subjects of the study. Certain differences (or lack thereof) are probably artifacts of the sample size. As a result, our findings must be validated by analyzing larger samples. Despite these limitations, this work has the potential to provide insights and open new research avenues.

Implications

Healthcare professionals should be aware of how much children participate in these games and be willing to engage in in-depth conversations with parents about the impact these games may have on children’s health. Therefore, periodical workshops should be held by pediatric and community mental health nurses to enhance student awareness of the effects of video games on their memory, attention, and academic performance. In addition, teaching programs should be held at schools to improve students’ attention, memory, learning, and cognitive skills.

Video game addiction has a significant impact on children’s attention and memory. Both attention and memory have a significant impact on a child’s cognitive and learning skills. These results reveal a significant indirect effect of video game addiction on cognitive and learning skills through attention. However, video game addiction had no significant indirect effect on cognitive and learning skills through child memory. In the presence of the mediators, the direct impact of video game addiction on cognitive and learning skills was also significant.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors extend their heartfelt appreciation and gratitude to all parents who willingly participated in the study.

The authors gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project Number: GSSRD-24.

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Amani Ali Kappi

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Amany Ali Kappi contributed to the project by designing the methodology, performing formal analysis, analyzing the data, and writing both the original draft and the manuscript. Rania Rabie El-Etreby contributed to conceptualizing, methodology, conducting, drafting, reviewing, and editing the manuscript. Ghada Gamal Badawy, was responsible for designing, executing, and documenting the investigation, including methodology, and manuscript preparation. Gawhara Ebrahem was responsible for designing, executing, and documenting the investigation, including methodology, and manuscript preparation Warda El Shahat Hamed conceptualized and prepared the methodology and investigation and contributed to writing the original draft. She also reviewed and edited the document. All authors read and approved the final manuscript.

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The researchers obtained approval for this study and data collection in this study from the Research Ethical Committee (REC) of Mansoura University’s Faculty of Nursing (IRB P0506/9/8/2023). All procedures were conducted in accordance with ethical standards outlined by the responsible committee on human experimentation and the Helsinki Declaration of 2008. Consent forms were obtained from all participants. Informed consent was obtained from all the participants in this study (from the mothers of the participant children).

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Kappi, A.A., El-Etreby, R.R., Badawy, G.G. et al. Effects of memory and attention on the association between video game addiction and cognitive/learning skills in children: mediational analysis. BMC Psychol 12 , 364 (2024). https://doi.org/10.1186/s40359-024-01849-9

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DOI : https://doi.org/10.1186/s40359-024-01849-9

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