behavior analysis research topics

5 Research Topics for Applied Behavior Analysis Students

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Whether you are in an ABA program right now or would like to be soon, it may be time to start thinking about research topics for your thesis or dissertation. All higher-level ABA courses will require students to have substantial independent research experience, which includes setting up a research experiment or trial, taking data, analyzing data, and suggesting next steps. And this also includes writing a professional paper either to turn in or submit to a scientific journal.

Overall, there will be quite a bit of research and writing that occurs in an ABA program.

If you’re currently in a program, read about these five research topic examples that might pique your curiosity.

1. Industrial Safety

In one classic study from 1987 , researchers examined how creating a token economy might increase safety at dangerous industrial sites. The study rewarded pit-mine workers when they and their colleagues avoided incidents that resulted in personal injury or equipment damage. They also rewarded workers who took extra steps to ensure the safety of others and report incidents. By using applied behavior analysis to incentivize self-motivated conduct modification, the researchers created improvements that persisted for years.

Behavior-Based Safety (BBS) “is an approach to occupational risk management that uses the science of behavior to increase safe behavior and reduce workplace injuries.”

Successful applications of BBS programs adhere to the following key principles ( Geller, 2005) :

Focus interventions on specific, observable behaviors.
Look for external factors to understand and improve behavior.
Use signals to direct behaviors, and use consequences to motivate workers.
Focus on positive consequences (not a punishment) to motivate behavior.
Use a science-based approach to test and improve BBS interventions.
Don’t let scientific theory limit the possibilities for improving BBS interventions.
Design interventions while considering the feelings and attitudes of workers within the organization.

The field of BBS can always improve, and your contribution to it through research can help. Consider choosing industrial safety and ABA as one of your research topics.

2. Autism Spectrum

Advocates also note that there remains a small but significant portion of autism sufferers who don’t respond to conventional techniques. There’s an ongoing need to study alternative methods and explore why certain approaches don’t work with some individuals. ABA techniques and their relation to autism-spectrum disorders will continue to pose important research questions for some time.

Not only can you conduct your own research (legally and ethically), and study other works of scientific literature, but you can be in the middle of it all like the professionals at the Marcus Autism Center do.

The center at Marcus is one of the most highly-regarded in the field of autism in the United States. They have a behavioral analysis research lab where clinician-researchers with expertise in applied behavior analysis.

According to their site:

“Although this work continues, the Behavior Analysis Research Lab recently expanded its research focus to include randomized clinical trials of behavioral interventions for core symptoms of autism, as well as co-occurring conditions or behaviors, such as elopement (e.g., wandering or running away) and encopresis (e.g., toileting concerns). Our goal is to disseminate the types of interventions and outcomes that can be achieved using ABA-based interventions to broader audiences by studying them in larger group designs.”

Depending on where you live, there may be experiential research opportunities for you as a student to dive into, such as the positions they have open at Marcus.

3. Animal and Human Intelligence

For example, researchers note that in 2010, dogs bit 4.5 million Americans annually, with 20 percent of bites needing medical intervention. They further suggest that ABA can provide a valid framework for understanding why such bites occur and preventing them. Similarly, studies that examine why rats may be able to detect tuberculosis or how service dogs help people involve learning about these creatures’ behaviors.

AAB, or Applied Animal Behavior , is an example of an organization that conducts research, supports animal behaviorists, and promotes the well-being of all animals that work in an applied setting.

The Animal Behavior Society is another example, which is the leading professional organization in the United States that studies animal behavior. They say that animal behaviorists can be educated in a variety of disciplines, including psychology biology, zoology, or animal science.

There is definitely room for more research in the field of animal behavior and its impact on humans.

4. Criminology

One study showed a potential correlation between allowing high-risk students to choose their schools and their likelihood of criminal involvement. While school choice didn’t affect academic achievement, it generally lowered the risk that people would commit crimes later in life.

Criminologists, behavioral psychologists, and forensic psychologists are all hired to work with local law enforcement and even the FBI to determine the motives of criminals along with the societal impacts, generational changes and other trends that might help be more proactive in the future. Mostly, they investigate why people commit crimes.

If you have ever watched a forensics TV show like Crime Scene Investigation (CSI) then you have a good idea of what their job entails. Between criminal profiling, working directly with a team, and investigating and solving cases is what it’s all about.

Experts on applied behavior analysis state:

“Its value to law enforcement investigations and criminal rehabilitation efforts make it an essential tool for any forensic psychologist. Research shows that successful application of applied forensic behavior analysis can lead to lower recidivism rates in convicts and a higher success rate in apprehending criminal suspects.”

Applied behavior analysis students who research these fields could play big roles in advancing societal knowledge.

5. Education

ABA is all around in education––you just cannot escape it! Everything, even academics, revolves around behavior . Whether it is on the county level or the classroom, there are FBAs, BIPs, data collection, positive reinforcement, consequences, token economies, trial and error, behavioral interventions, and much more.

And teachers aren’t the only staff privy to ABA. School social workers, school counselors, behavioral specialists, and paraprofessionals all have access to ABA and can implement strategies based on individual student needs.

Education techniques rely heavily on applied behavior analysis. Instructors may be tasked with giving consequences to students or devising custom lessons, and these tasks often involve understanding how to incentivize appropriate behavior while motivating learners.

Like other kinds of ABA, applied education research also provides the opportunity for internships and postgraduate residency programs. Because many of this field’s modern foundations lie in education, classroom-based research is a natural fit for students who want to apply their discoveries.

Research Topics for Applied Behavior Analysis Students: Conclusion

Applied behavior analysis is complex, but studying it is extremely rewarding. This field provides students at all educational levels with ample opportunities to contribute to scientific knowledge and better people’s lives in the process. There are almost too many fields to choose from in terms of where you want to lean. Think about your interests, what you have access to in your surrounding area (unless you are willing to move), and consider what type of research will help you move forward in your educational career and beyond. There are ABA programs and careers out there waiting for each of you!

Brittany Cerny

Master of Education (M.Ed.) | Northeastern State University

Behavior and Learning Disorders | Georgia State University

Updated December 2021

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Applied Behavior Analysis Research Paper Topics

This page provides a comprehensive list of applied behavior analysis research paper topics , designed to serve as an invaluable resource for students delving into the intricacies of behavior analysis. With a focus on both foundational theories and practical applications, this collection spans a wide array of subjects within the field, from the fundamental principles of behavior analysis to cutting-edge research in autism spectrum disorders, ethical considerations, and beyond. Whether you are a novice in the field of applied behavior analysis or seeking to deepen your expertise with advanced research topics, this curated list aims to inspire and guide your academic inquiries, ensuring you find a topic that not only meets your educational requirements but also sparks your intellectual curiosity.

100 Applied Behavior Analysis Research Paper Topics

Choosing the right topic for your applied behavior analysis (ABA) research is pivotal, as it not only determines the direction of your study but also impacts its relevance and potential contribution to the field. The diversity within applied behavior analysis research paper topics is vast, reflecting the field’s broad applicability to various behavioral issues and settings. From foundational theories to innovative interventions, the range of topics available offers students the opportunity to explore areas of personal interest or professional relevance, fostering a deeper understanding and engagement with the subject matter.

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The History and Evolution of Applied Behavior Analysis
Key Principles of Behavior Analysis
Behaviorism: The Theoretical Underpinning
The Role of Reinforcement and Punishment
Comparing ABA with Other Behavioral Therapies
The Science of Behavior Change: Core Concepts
Understanding Behavioral Assessments
The Significance of Operational Definitions in ABA
Generalization and Maintenance of Behavioral Changes
Ethical Foundations in Applied Behavior Analysis
Functional Behavior Assessments (FBA)
Behavior Intervention Plans (BIPs)
Single-Subject Design in ABA Research
Data Collection Methods in ABA
Interpreting and Using ABA Data for Decision Making
The Role of Indirect and Descriptive Assessments
Reliability and Validity in Behavioral Assessments
Use of Technology in Behavioral Assessment
Comparative Analysis of Assessment Tools
Behavioral Skills Training
Discrete Trial Training (DTT)
Natural Environment Training (NET)
Pivotal Response Treatment (PRT)
Early Intensive Behavioral Intervention (EIBI)
Token Economies in ABA
The Use of Visual Supports in ABA
Response Interruption and Redirection (RIRD)
Functional Communication Training (FCT)
Behavioral Activation Techniques
Crisis Intervention Strategies in ABA
Digital Data Collection Tools
The Use of Virtual Reality in Behavior Analysis
Mobile Applications for Behavioral Interventions
Telehealth and Remote ABA Services
Augmented Reality for Social Skills Development
Wearable Technology in Behavioral Monitoring
Gamification of Behavioral Interventions
Online Training Programs for ABA Practitioners
Enhancing Parental Engagement through Technology
Data Analytics and Machine Learning in ABA
Early Detection and Intervention Strategies
Social Skills Training for Children with ASD
Addressing Challenging Behaviors in ASD
Parent-Implemented Interventions for Autism
The Impact of ABA on Language Development
Integrating ABA with Other Therapeutic Approaches
School-Based Interventions for ASD
Transition Planning for Adolescents with ASD
Adult Outcomes and Interventions for Autism Spectrum Disorder
Novel Therapeutic Technologies for ASD
Informed Consent in Behavioral Research
Privacy and Confidentiality Issues
Cultural Competency and Sensitivity
Ethical Decision-Making Models
Balancing Efficacy and Ethics in Intervention Planning
Rights of Participants in Behavioral Studies
Addressing Professional Conflicts of Interest
The Use of Restraint and Seclusion
Ethical Issues in Telehealth Services
Maintaining Professional Boundaries
Classroom Management Strategies
Inclusive Education Practices
Peer-Mediated Interventions
Curriculum Modifications and Accommodations
Functional Literacy in Special Education
Behavioral Strategies for Academic Engagement
Addressing Bullying in Schools
Teacher Training in Behavioral Techniques
Assessment and Intervention in School Settings
Promoting Generalization Across Educational Contexts
Assessing Social Competence
Group Interventions for Social Skills Training
Modeling and Role-Playing Techniques
Enhancing Emotional Intelligence through ABA
Social Narratives and Story-Based Interventions
Peer Feedback and Social Interaction Strategies
Technology-Assisted Social Skills Training
Social Skills in Early Childhood Education
Strategies for Adolescents and Adults
Measuring Outcomes in Social Skills Interventions
Principles of Effective Parent Training Programs
Implementing Behavioral Strategies at Home
Supporting Families of Children with Special Needs
Culturally Responsive Parent Training
Training for Caregivers of Adults with Disabilities
Stress Management for Parents and Caregivers
Community-Based Support Programs
Enhancing Communication between Parents and Professionals
Online and Remote Training Options
Evaluating the Impact of Parent Training
Precision Teaching and Personalized Learning
The Intersection of ABA and Neuroscience
Behavioral Economics and Decision Making
Sustainability and Environmental Behavior Change
The Future of ABA Certification and Training
Integrative Approaches to Health and Wellness
The Role of ABA in Public Policy Making
Advances in Behavioral Gerontology
ABA and Technology: The Next Frontier
Global Perspectives and International Applications of ABA

The breadth and depth of applied behavior analysis research paper topics reflect the dynamic and evolving nature of the field. By exploring these diverse areas, students can contribute valuable insights and advancements to the practice and theory of ABA. We encourage students to delve into these topics, pursuing research that not only fulfills academic requirements but also propels the field forward, enhancing the efficacy and reach of applied behavior analysis in improving lives.

What is Applied Behavior Analysis

Introduction to Applied Behavior Analysis

Applied Behavior Analysis Research Paper Topics

Historical Context and Evolution of ABA

The roots of Applied Behavior Analysis trace back to the early 20th century, with the work of pioneers like B.F. Skinner, who laid the groundwork with his studies on operant conditioning. Skinner’s research highlighted the influence of environmental factors on behavior, setting the stage for the development of ABA. Over the decades, ABA has evolved from a focus on basic research to a broader application that addresses a wide range of human behaviors and challenges. This evolution reflects a growing recognition of the versatility and effectiveness of ABA methods in bringing about positive behavior change.

Key Principles and Techniques in ABA

Central to ABA are principles such as reinforcement, punishment, and extinction, which are used to increase or decrease targeted behaviors. Techniques such as discrete trial training, pivotal response training, and functional behavior assessments are instrumental in implementing these principles. ABA interventions are characterized by their individualized approach, where strategies are tailored based on the assessment of each person’s behavior and environment. This customization ensures that interventions are not only effective but also respectful of the individual’s unique circumstances and needs.

The Scope of ABA in Various Settings

Applied behavior analysis research paper topics often explore the diverse applications of ABA across settings such as schools, clinics, workplaces, and homes. In educational environments, ABA strategies support students with learning disabilities, behavioral challenges, and autism spectrum disorders, enhancing their educational outcomes and social integration. Clinically, ABA is pivotal in treating various psychological disorders, including phobias, OCD, and ADHD, by helping individuals develop new skills and reduce problematic behaviors. At home, ABA techniques empower families and caregivers with strategies to support their loved ones, promoting independence and improving quality of life.

Exploration of ABA’s Impact on Autism and Developmental Disorders

One of the most profound applications of Applied Behavior Analysis is in the treatment of autism spectrum disorders (ASD) and other developmental disorders. ABA’s impact on autism is significant, with a multitude of studies demonstrating its efficacy in improving communication, social skills, and adaptive behaviors among individuals with ASD. Through individualized assessment and intervention plans, ABA facilitates meaningful progress, helping individuals with autism lead more fulfilling lives. The emphasis on early intervention and the adaptability of ABA techniques have made it a cornerstone in autism therapy.

Ethical Considerations and Future Directions in ABA Research

As the field of ABA continues to grow, so too does the attention to its ethical considerations. Ensuring the dignity and rights of those receiving ABA services is paramount. This includes obtaining informed consent, ensuring privacy, and maintaining a commitment to do no harm. Looking to the future, ABA research is poised to explore new technologies, interdisciplinary approaches, and global applications. The integration of ABA with fields such as neuroscience and technology promises to enhance the understanding and treatment of behavior further.

The Significance of ABA in Advancing Understanding and Interventions for Behavior Modification

The significance of Applied Behavior Analysis in advancing our understanding and interventions for behavior modification cannot be overstated. Its evidence-based approach has transformed the lives of many, offering hope and practical solutions to individuals and families navigating behavioral challenges. As ABA continues to evolve, its capacity to foster positive change remains its most enduring legacy, underscoring the importance of continued research, ethical practice, and innovation in the field. Through the dedicated efforts of researchers, practitioners, and educators, ABA will continue to illuminate the path toward understanding human behavior and creating meaningful change.

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APA Handbook of Behavior Analysis

Editor-in-Chief: Gregory J. Madden, PhD

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Table of contents
Contributor bios
Book details
Additional Resources

This two-volume handbook continues the inductive translational approach to the science of behavior analysis by providing overview and in-depth chapters spanning the breadth of behavior analysis.

Behavior analysis emerged from the nonhuman laboratories of B. F. Skinner, Fred Keller, Nate Schoenfeld, Murray Sidman, James Dinsmoor, Richard Herrnstein, Nate Azrin, and others who pioneered experimental preparations designed to do one thing—find orderly relations between environment and behavior. This bottom-up approach to a natural science of behavior yielded a set of behavioral principles that proved orderly and replicable across subjects, laboratories, and species.

By the 1960s, behavior analysts began translating these principles into interventions for institutionalized humans characterized by impoverished repertoires of adaptive behavior. When these interventions proved successful in replacing problem- with adaptive-behavior, the field of Applied Behavior Analysis was born.

Over the last 50 years the field of behavior analysis has grown substantially both in the number of practicing behavior analysts and the range of behavior to which behavioral principles have been applied. Today the laboratory study of basic principles of behavior continues to expand our understanding of behavior and to inform the treatment of disorders ranging from autism to substance abuse.

The present volumes continue this inductive translational approach to the science of behavior analysis by providing overview and in-depth chapters spanning the breadth of behavior analysis.

Volume I provides comprehensive coverage of the logic, clinical utility, and methods of single-case research designs. Chapters walk the reader through the design, data collection, and data analysis phases and are appropriate for students, researchers, and clinicians concerned with best practice. Volume I also provides an overview of the experimental analysis of behavior, and chapters reviewing some of the most important areas of contemporary laboratory research in behavior analysis. Topics covered include memory, attention, choice, behavioral neuroscience, and behavioral pharmacology.

Volume II includes 10 chapters illustrating how principles of behavior discovered in basic-science laboratories have provided insights on socially important human behavior ranging from the complex discriminations that underlie human language to disorders treated by clinical psychologists. The second section of Volume II includes 12 chapters, each devoted to a particular behavioral/developmental disorder (e.g., behavioral treatments of ADHD, autism) or to behavior of societal importance (e.g., effective college teaching, effective treatment of substance abuse). Each of these chapters provides a review of what works and where additional research is needed.

Volume 1: Methods and Principles

Editorial Board

About the Editor-in-Chief

Contributors

Series Preface

Introduction

I. Overview

Single-Case Research Methods: An Overview Iver H. Iversen
The Five Pillars of the Experimental Analysis of Behavior Kennon A. Lattal
Translational Research in Behavior Analysis William V. Dube
Applied Behavior Analysis Dorothea C. Lerman, Brian A. Iwata, and Gregory P. Hanley

II. Single-Case Research Designs

Single-Case Experimental Designs Michael Perone and Daniel E. Hursh
Observation and Measurement in Behavior Analysis Raymond G. Miltenberger and Timothy M. Weil
Generality and Generalization of Research Findings Marc N. Branch and Henry S. Pennypacker
Single-Case Research Designs and the Scientist-Practitioner Ideal in Applied Psychology Neville M. Blampied
Visual Analysis in Single-Case Research Jason C. Bourret and Cynthia J. Pietras
Quantitative Description of Environment–Behavior Relations Jesse Dallery and Paul L. Soto
Time-Series Statistical Analysis of Single-Case Data Jeffrey J. Borckardt, Michael R. Nash, Wendy Balliet, Sarah Galloway, and Alok Madan
New Methods for Sequential Behavior Analysis Peter C. M. Molenaar and Tamara Goode

III. The Experimental Analysis of Behavior

Pavlovian Conditioning K. Matthew Lattal
The Allocation of Operant Behavior Randolph C. Grace and Andrew D. Hucks
Behavioral Neuroscience David W. Schaal
Stimulus Control and Stimulus Class Formation Peter J. Urcuioli
Attention and Conditioned Reinforcement Timothy A. Shahan
Remembering and Forgetting K. Geoffrey White
The Logic and Illogic of Human Reasoning Edmund Fantino and Stephanie Stolarz-Fantino
Self-Control and Altruism Matthew L. Locey, Bryan A. Jones, and Howard Rachlin
Behavior in Relation to Aversive Events: Punishment and Negative Reinforcement Philip N. Hineline and Jesús Rosales-Ruiz
Operant Variability Allen Neuringer and Greg Jensen
Behavioral Pharmacology Gail Winger and James H. Woods

Volume 2: Translating Principles Into Practice

I. translational research in behavior analysis.

From Behavioral Research to Clinical Therapy Paul M. Guinther and Michael J. Dougher
Translational Applied Behavior Analysis and Neuroscience Travis Thompson
Arranging Reinforcement Contingencies in Applied Settings: Fundamentals and Implications of Recent Basic and Applied Research Iser G. DeLeon, Christopher E. Bullock, and A. Charles Catania
Operant Extinction: Elimination and Generation of Behavior Kennon A. Lattal, Claire St. Peter, and Rogelio Escobar
Response Strength and Persistence John A. Nevin and David P. Wacker
Simple and Complex Discrimination Learning William J. McIlvane
Translational Applications of Quantitative Choice Models Eric A. Jacobs, John C. Borrero, and Timothy R. Vollmer
The Translational Utility of Behavioral Economics: The Experimental Analysis of Consumption and Choice Steven R. Hursh, Gregory J. Madden, Ralph Spiga, Iser G. DeLeon, and Monica T. Francisco
Environmental Health and Behavior Analysis: Contributions and Interactions M. Christopher Newland
Toward Prosocial Behavior and Environments: Behavioral and Cultural Contingencies in a Public Health Framework Anthony Biglan and Sigrid S. Glenn

II. Applied/Clinical Issues

Behavioral Approaches to Treatment of Intellectual and Developmental Disabilities Patricia F. Kurtz and Michael A. Lind
Behavioral Approaches to the Treatment of Autism William H. Ahearn and Jeffrey H. Tiger
The Analysis of Verbal Behavior and Its Therapeutic Applications James E. Carr and Caio F. Miguel
Assessment and Treatment of Severe Problem Behavior Louis P. Hagopian, Claudia L. Dozier, Griffin W. Rooker, and Brooke A. Jones
Understanding and Treating Attention-Deficit/Hyperactivity Disorder Nancy A. Neef, Christopher J. Perrin, and Gregory J. Madden
Teaching Reading Edward J. Daly III and Sara Kupzyk
Sleep: A Behavioral Account Neville M. Blampied and Richard R. Bootzin
Acceptance and Commitment Therapy: Applying an Iterative Translational Research Strategy in Behavior Analysis Michael E. Levin, Steven C. Hayes, and Roger Vilardaga
Voucher-Based Contingency Management in the Treatment of Substance Use Disorders Stephen T. Higgins, Sarah H. Heil, and Stacey C. Sigmon
Behavioral Approaches to Business and Industrial Problems: Organizational Behavior Management William B. Abernathy
Contributions of Behavior Analysis to Higher Education Dan Bernstein and Philip N. Chase
Behavioral Gerontology Jane Turner and R. Mark Mathews

Gregory J. Madden, PhD , is a professor in the department of psychology at Utah State University, Logan. He earned a master's degree in behavior analysis from the University of North Texas is 1992 and a doctorate in psychology from West Virginia University in 1995. After completing a 3-year National Institutes of Health-funded postdoctoral research fellowship at the University of Vermont, Dr. Madden held faculty appointments at the University of Wisconsin–Eau Claire and the University of Kansas before joining the faculty at Utah State University.

Dr. Madden's research falls under the umbrella of behavioral economics, with an emphasis on impulsive choice and health decision making. He is the author or coauthor of many of the seminal scientific articles in the study of delayed-reward discounting and its relation to addictions. His research in this area is supported by grants from the National Institutes of Health (National Institute on Drug Abuse). He also holds grants from the U.S. Department of Agriculture. The latter grants support behavioral economic approaches to influencing dietary choices made by children in school cafeterias.

Dr. Madden coedited, with Warren K. Bickel, Impulsivity: The Behavioral and Neurological Science of Discounting (APA) and currently serves as the editor of the Journal of the Experimental Analysis of Behavior , established in 1958 and the flagship journal of basic research in behavior analysis. Dr. Madden has served on a number of important decision-making bodies (e.g., the Executive Council of the Association for Behavior Analysis International) and is the recipient of several teaching honors, including being selected as the 2011 G. Stanley Hall lecturer for APA Division 2 (Society for the Teaching of Psychology).

Include handbook chapters in customized course books with APA Custom Course Books .

Applied Behavior Analysis Theses and Dissertations

Theses/dissertations from 2009 2009.

It is Time to Play! Peer Implemented Pivotal Response Training with a Child with Autism during Recess , Leigh Anne Sams

Theses/Dissertations from 2008 2008

The Evaluation of a Commercially-Available Abduction Prevention Program , Kimberly V. Beck

Expert Video Modeling with Video Feedback to Enhance Gymnastics Skills , Eva Boyer

Behavior Contracting with Dependent Runaway Youth , Jessica Colon

Can Using One Trainer Solely to Deliver Prompts and Feedback During Role Plays Increase Correct Performance of Parenting Skills in a Behavioral Parent Training Program? , Michael M. Cripe

Evaluation of a Functional Treatment for Binge Eating Associated with Bulimia Nervosa , Tamela Cheri DeWeese-Giddings

Teaching Functional Skills to Individuals with Developmental Disabilities Using Video Prompting , Julie A. Horn

Evaluation of a Standardized Protocol for Parent Training in Positive Behavior Support Using a Multiple Baseline Design , Robin Lane

Publicly Posted Feedback with Goal Setting to Improve Tennis Performance , Gretchen Mathews

Improving Staff Performance by Enhancing Staff Training Procedures and Organizational Behavior Management Procedures , Dennis Martin McClelland Jr.

Supporting Teachers and Children During In-Class Transitions: The Power of Prevention , Sarah M. Mele

Effects of Supervisor’s Presence on Staff Response to Tactile Prompts and Self-Monitoring in a Group Home Setting , Judy M. Mowery

Social Skills Training with Typically Developing Adolescents: Measurement of Skill Acquisition , Jessica Anne Thompson

Theses/Dissertations from 2007 2007

Evaluating the effects of a reinforcement system for students participating in the Fast Forword language program , Catherine C. Wilcox

Theses/Dissertations from 2006 2006

The Acquisition of Functional Sign Language by Non-Hearing Impaired Infants , Kerri Haley-Garrett

Response Cards in the Elementary School Classroom: Effects on Student and Teacher Behavior , Shannon McKallip-Moss

The Effects of a Parent Training Course on Coercive Interactions Between Parents and Children , Lezlee Powell

The Effects of Role-Playing on the Development of Adaptive Skills in a Parent Training Program , Chantell A. Rodriguez-Del Valle

Archival evaluation of a proactive school wide discipline plan , Beth Rutz-Beynart

Effects of a multi-component interdependent group contingency game on the classroom behavior of typically developing elementary school children , Stacey D. Simonds

Establishing a Functional Analysis Protocol for Examining Behavioral Deficits using Social Withdrawal as an Exemplar , Melissa Penaranda Walters

Theses/Dissertations from 2005 2005

The Role of Choice Versus Preference: An Analysis of Why Choice Interventions Work , John D. Adelinis

The Effect of Direct Instruction Math Curriculum on Higher-Order Problem Solving , Pamela Christofori

The Effects of Response Cards on the Performance and Generalization of Parenting Skills , Bennie L. Colbert

A Comparison of Two Prompting Procedures on Tacting Behavior , Kelley N. Gardner

The Effects Of The Presence Of A Dog On The Social Interactions Of Children With Developmental Disabilities , Stephanie Walters

The Effects of Fluency Training on Performance, Maintenance, and Generalization of Parenting Skills , Gertie Williams

Theses/Dissertations from 2004 2004

Use of the Power Card Strategy as an Intervention with an Elementary School Student with Asperger Syndrome: Increasing On-Task Behavior in the General Education Setting , Jane M. Devenport

The Importance of Program-Delivered Differential Reinforcement in the Development of Classical Music Auditory Discrimination , Gudmundur Torfi Heimisson

The Impact of a Goal Setting Procedure on the Work Performance of Young Adults with Behavioral/Emotional/Learning Challenges , Robin Wagner Hogsholm

The Effects of Graphic Display and Training in Visual Inspection on Teachers' Detection of Behavior Change , Allana Duncan Luquette

Imitation and its Reciprocity in the Treatment of Autism , Roxana I. Nedelcu

Utility of Positive Peer Reporting to Improve Interactions Among Children in Foster Care , Jenny L. Van Horn

Behavioral Analysis of Interactions Between Teachers and Children with Selective Mutism , Jason D. Wallace

Theses/Dissertations from 2003 2003

Creating Positive Experiences: Increasing Parent Participation In A Low Income Elementary School , Krista Stinson Cayer

Evaluation Of A Presentation And Measurement Method For Assessing Activity Preference , Tara L. Lieblein

Using The ABLLS with English Language Learners: Implications for Students and Teachers , Lorie G. Schultz

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I’ve been able to work on several studies and trials with my mentor, Dr. Rick Kubina, that I talk about in this episode. In 2005, I coauthored a peer reviewed journal article, Using Transfer Procedures to Teach Tacts to a Child with Autism. This study was born out of work done with my son Lucas to correct a tact error with greetings. I never published this study because of the mixed procedures but I did present, and all 4 of the subjects learned equally as well with this method and Lucas only learned this way. Just a few years later, I was able to meet a Doctor who did his dissertation on transfer procedures and actually quoted my work on that study. The need for studies and for information is there.

Dr. Valentino is the Chief Clinical Officer for Trumpet Behavioral Health, she is very passionate about advocating for research with practitioners. Her book Applied Behavior Analysis Research Made Easy: A Handbook for Practitioners Conducting Research Post-Certification, is a great read for professionals who want to contribute research to the field, break barriers, and get started. You can find out more about her on the TBH website as well as her personal blog, Behavior-Mom.

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Published: 27 January 2022

The future of human behaviour research

Janet M. Box-Steffensmeier 1 ,
Jean Burgess 2 , 3 ,
Maurizio Corbetta 4 , 5 ,
Kate Crawford 6 , 7 , 8 ,
Esther Duflo 9 ,
Laurel Fogarty 10 ,
Alison Gopnik 11 ,
Sari Hanafi 12 ,
Mario Herrero 13 ,
Ying-yi Hong 14 ,
Yasuko Kameyama 15 ,
Tatia M. C. Lee 16 ,
Gabriel M. Leung 17 , 18 ,
Daniel S. Nagin 19 ,
Anna C. Nobre 20 , 21 ,
Merete Nordentoft 22 , 23 ,
Aysu Okbay 24 ,
Andrew Perfors 25 ,
Laura M. Rival 26 ,
Cassidy R. Sugimoto 27 ,
Bertil Tungodden 28 &
Claudia Wagner 29 , 30 , 31

Nature Human Behaviour volume 6 , pages 15–24 ( 2022 ) Cite this article

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Human behaviour is complex and multifaceted, and is studied by a broad range of disciplines across the social and natural sciences. To mark our 5th anniversary, we asked leading scientists in some of the key disciplines that we cover to share their vision of the future of research in their disciplines. Our contributors underscore how important it is to broaden the scope of their disciplines to increase ecological validity and diversity of representation, in order to address pressing societal challenges that range from new technologies, modes of interaction and sociopolitical upheaval to disease, poverty, hunger, inequality and climate change. Taken together, these contributions highlight how achieving progress in each discipline will require incorporating insights and methods from others, breaking down disciplinary silos.

Genuine progress in understanding human behaviour can only be achieved through a multidisciplinary community effort. Five years after the launch of Nature Human Behaviour , twenty-two leading experts in some of the core disciplines within the journal’s scope share their views on pressing open questions and new directions in their disciplines. Their visions provide rich insight into the future of research on human behaviour.

Artificial intelligence

Kate Crawford

Much has changed in artificial intelligence since a small group of mathematicians and scientists gathered at Dartmouth in 1956 to brainstorm how machines could simulate cognition. Many of the domains that those men discussed — such as neural networks and natural language processing — remain core elements of the field today. But what they did not address was the far-reaching social, political, legal and ecological effects of building these systems into everyday life: it was outside their disciplinary view.

Since the mid-2000s, artificial intelligence (AI) has rapidly expanded as a field in academia and as an industry, and now a handful of powerful technology corporations deploy these systems at a planetary scale. There have been extraordinary technical innovations, from real-time language translation to predicting the 3D structures of proteins 1 , 2 . But the biggest challenges remain fundamentally social and political: how AI is widening power asymmetries and wealth inequality, and creating forms of harm that need to be prioritized, remedied and regulated.

The most urgent work facing the field today is to research and remediate the costs and consequences of AI. This requires a deeper sociotechnical approach that can contend with the complex effect of AI on societies and ecologies. Although there has been important work done on algorithmic fairness in recent years 3 , 4 , not enough has been done to address how training data fundamentally skew how AI models interpret the world from the outset. Second, we need to address the human costs of AI, which range from discrimination and misinformation to the widespread reliance on underpaid labourers (such as the crowd-workers who train AI systems for as little as US $2 per hour) 5 . Third, there must be a commitment to reversing the environmental costs of AI, including the exceptionally high energy consumption of the current large computational models, and the carbon footprint of building and operating modern tensor processing hardware 6 . Finally, we need strong regulatory and policy frameworks, expanding on the EU’s draft AI Act of 2021.

By building a more interdisciplinary and inclusive AI field, and developing a more rigorous account of the full impacts of AI, we give engineers and regulators alike the tools that they need to make these systems more sustainable, equitable and just.

Kate Crawford is Research Professor at the Annenberg School, University of Southern California, Los Angeles, CA, USA; Senior Principal Researcher at Microsoft Research New York, New York, NY, USA; and the Inaugural Visiting Chair of AI and Justice at the École Normale Supérieure, Paris, France.

Anthropology

Laura M. Rival

The field of anthropology faces fundamental questions about its capacity to intervene more effectively in political debates. How can we use the knowledge that we already have to heal the imagined whole while keeping people in synchrony with each other and with the world they aspire to create for themselves and others?

The economic systems that sustain modern life have produced pernicious waste cultures. Globalization has accelerated planetary degradation and global warming through the continuous release of toxic waste. Every day, like millions of others, I dutifully clean and prepare my waste for recycling. I know it is no more than a transitory measure geared to grant manufacturers time to adjust and adapt. Reports that most waste will not be recycled, but dumped or burned, upset me deeply. How can anthropology remain a critical project in the face of such orchestrated cynicism, bad faith and indifference? How should anthropologists deploy their skills and bring a sense of shared responsibility to the task of replenishing the collective will?

To help to find answers to these questions, anthropologists need to radically rethink the ways in which we describe the processes and relations that tie communities to their environments. The extinction of experience (loss of direct contact with nature) that humankind currently suffers is massive, but not irreversible. New forms of storytelling have successfully challenged modernist myths, particularly their homophonic promises 7 . But there remain persistent challenges, such as the seductive and rampant power of one-size-fits-all progress, and the actions of elites, who thrive on emulation, and in doing so fuel run-away consumerism.

To combat these challenges, I simply reassert that ‘nature’ is far from having outlasted its historical utility. Anthropologists must join forces and reanimate their common exploration of the immense possibilities contained in human bodies and minds. No matter how overlooked or marginalized, these natural potentials hold the key to what keeps life going.

Laura M. Rival is Professor of Anthropology of Nature, Society and Development, ODID and SAME, University of Oxford, Oxford, UK .

Communication and media studies

Jean Burgess

The communication and media studies field has historically been animated by technological change. In the process, it has needed to navigate fundamental tensions: communication can be understood as both transmission (of information), and as (social) ritual 8 ; relatedly, media can be understood as both technology and as culture 9 .

The most important technological change over the past decade has been the ‘platformization’ 10 of the media environment. Large digital platforms owned by the world’s most powerful technology companies have come to have an outsized and transformative role in the transmission (distribution) of information, and in mediating social practices (whether major events or intimate daily routines). In response, digital methods have transformed the field. For example, advances in computational techniques enabled researchers to study patterns of communication on social media, leading to disciplinary trends such as the quantitative description of ‘hashtag publics’ in the mid-2010s 11 .

Platforms’ uses of data, algorithms and automation for personalization, content moderation and governance constitute a further major shift, giving rise to new methods (such as algorithmic audits) that go well beyond quantitative description 12 . But platform companies have had a patchy — at times hostile — relationship to independent research into their societal role, leading to data lockouts and even public attacks on researchers. It is important in the interests of public oversight and open science that we coordinate responses to such attempts to suppress research 13 , 14 .

As these processes of digital transformation continue, new connections between the humanities and technical disciplines will be necessary, giving rise to a new wave of methodological innovation. This next phase will also require more hybrid (qualitative and quantitative; computational and critical) methods 15 , not only to get around platform lockouts but also to ensure more careful attention is paid to how the new media technologies are used and experienced in everyday life. Here, innovative approaches such as the use of data donations can both aid the ‘platform observability’ 16 that is essential to accountability, and ensure that our research involves the perspectives of diverse audiences.

Jean Burgess is Professor of Digital Media at the School of Communication and Digital Media Research Centre (DMRC), Queensland University of Technology, Brisbane, Queensland Australia; and Associate Director at the Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADM+S), Melbourne, Victoria, Australia .

Computational social science

Claudia Wagner

Computational social science has emerged as a discipline that leverages computational methods and new technologies to collect, model and analyse digital behavioural data in natural environments or in large-scale designed experiments, and combine them with other data sources (such as survey data).

While the community made critical progress in enhancing our understanding about empirical phenomena such as the spread of misinformation 17 and the role of algorithms in curating misinformation 18 , it has focused less on questions about the quality and accessibility of data, the validity, reliability and reusability of measurements, the potential consequences of measurements and the connection between data, measurement and theory.

I see the following opportunities to address these issues.

First, we need to establish privacy-preserving, shared data infrastructures that collect and triangulate survey data with scientifically motivated organic or designed observational data from diverse populations 19 . For example, longitudinal online panels in which participants allow researchers to track their web browsing behaviour and link these traces to their survey answers will not only facilitate substantive research on societal questions but also enable methodological research (for example, on the quality of different data sources and measurement models), and contribute to the reproducibility of computational social science research.

Second, best practices and scientific infrastructures are needed for supporting the development, evaluation and re-use of measurements and the critical reflection on potentially harmful consequences of measurements 20 . Social scientists have developed such best practices and infrastructural support for survey measurements to avoid using instruments for which the validity is unclear or even questionable, and to support the re-usability of survey scales. I believe that practices from survey methodology and other domains, such as the medical industry, can inform our thinking here.

Finally, the fusion of algorithmic and human behaviour invites us to rethink the various ways in which data, measurements and social theories can be connected 20 . For example, product recommendations that users receive are based on measurements of users’ interests and needs: however, users and measurements are not only influenced by those recommendations, but also influence them in turn. As a community we need to develop research designs and environments that help us to systematically enhance our understanding of those feedback loops.

Claudia Wagner is Head of Computational Social Science Department at GESIS – Leibniz Institute for the Social Sciences, Köln, Germany; Professor for Applied Computational Social Sciences at RWTH Aachen University, Aachen, Germany; and External Faculty Member of the Complexity Science Hub, Vienna, Austria .

Criminology

Daniel S. Nagin

Disciplinary silos in path-breaking science are disappearing. Criminology has had a longstanding tradition of interdisciplinarity, but mostly in the form of an uneasy truce of research from different disciplines appearing side-by-side in leading journals — a scholarly form of parallel play. In the future, this must change because the big unsolved challenges in criminology will require cooperation among all of the social and behavioural sciences.

These challenges include formally merging the macro-level themes emphasized by sociologists with the micro-, individual-level themes emphasized by psychologists and economists. Initial steps have been made by economists who apply game theory to model crime-relevant social interactions, but much remains to be done in building models that explain the formation and destruction of social trust, collective efficacy and norms, as they relate to legal definitions of criminal behaviour.

A second opportunity concerns the longstanding focus of criminology on crimes involving the physical taking of property and interpersonal physical violence. These crimes are still with us, but — as the daily news regularly reports — the internet has opened up broad new frontiers for crime that allow for thefts of property and identities at a distance, forms of extortion and human trafficking at a massive scale (often involving untraceable transactions using financial vehicles such as bitcoin) and interpersonal violence without physical contact. This is a new and largely unexplored frontier for criminological research that criminologists should dive into in collaboration with computer scientists who already are beginning to troll these virgin scholarly waters.

The final opportunity I will note also involves drawing from computer science, the primary home of what has come to be called machine learning. It is important that new generations of criminologists become proficient with machine learning methods and also collaborate with its creators. Machine learning and related statistical methods have wide applicability in both the traditional domains of criminological research and new frontiers. These include the use of prediction tools in criminal justice decision-making, which can aid in crime detection, and the prevention and measuring of crime both online and offline, but also have important implications for equity and fairness due to their consequential nature.

Daniel S. Nagin is Teresa and H. John Heinz III University Professor of Public Policy and Statistics at the Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA .

Behavioural economics

Bertil Tungodden

Behavioural and experimental economics have transformed the field of economics by integrating irrationality and nonselfish motivation in the study of human behaviour and social interaction. A richer foundation of human behaviour has opened many new exciting research avenues, and I here highlight three that I find particularly promising.

Economists have typically assumed that preferences are fixed and stable, but a growing literature, combining field and laboratory experimental approaches, has provided novel evidence on how the social environment shapes our moral and selfish preferences. It has been shown that prosocial role models make people less selfish 21 , that early-childhood education affects the fairness views of children 22 and that grit can be fostered in the correct classroom environment 23 . Such insights are important for understanding how exposure to different institutions and socialization processes influence the intergenerational transmission of preferences, but much more work is needed to gain systematic and robust evidence on the malleability of the many dimensions that shape human behaviour.

The moral mind is an important determinant of human behaviour, but our understanding of the complexity of moral motivation is still in its infancy. A growing literature, using an impartial spectator design in which study participants make consequential choices for others, has shown that people often disagree on what is morally acceptable. An important example is how people differ in their view of what is a fair inequality, ranging from the libertarian fairness view to the strict egalitarian fairness view 24 , 25 . An exciting question for future research is whether such moral differences reflect a concern for other moral values, such as freedom, or irrational considerations.

A third exciting development in behavioural and experimental economics is the growing set of global studies on the foundations of human behaviour 26 , 27 . It speaks to the major concern in the social sciences that our evidence is unrepresentative and largely based on studies with samples from Western, educated, industrialized, rich and democratic societies 28 . The increased availability of infrastructure for implementing large-scale experimental data collections and methodological advances carry promise that behavioural and experimental economic research will broaden our understanding of the foundations of human behaviour in the coming years.

Bertil Tungodden is Professor and Scientific Director of the Centre of Excellence FAIR at NHH Norwegian School of Economics, Bergen, Norway .

Development economics

Esther Duflo

The past three decades have been a wonderful time for development economics. The number of scholars, the number of publications and the visibility of the work has dramatically increased. Development economists think about education, health, firm growth, mental health, climate, democratic rules and much more. No topic seems off limits!

This progress is intimately connected with the explosion of the use of randomized controlled trials (RCTs) and, more generally, with the embrace of careful causal identification. RCTs have markedly transformed development economics and made it the field that it is today.

The past three decades (until the COVID-19 crisis) have also been very good for improving the circumstances of low-income people around the world: poverty rates have fallen; school enrolment has increased; and maternal and infant mortality has been halved. Although I would not dare imply that the two trends are causally related, one of the reasons for these improvements in the quality of life — even in countries where economic growth has been slow — is the greater focus on pragmatic solutions to the fundamental problems faced by people with few resources. In many countries, development economics researchers (particularly those working with RCTs) have been closely involved with policy-makers, helping them to develop, implement and test these solutions. In turn, this involvement has been a fertile ground for new questions, which have enriched the field.

I imagine future change will, once again, come from an unexpected place. One possible driver of innovation will come from this meeting between the requirements of policy and the intellectual ambition of researchers. This means that the new challenges of our planet must (and will) become the new challenges of development economics. Those challenges are, I believe, quite clear: rethinking social protection to be better prepared to face risks such as the COVID-19 pandemic; mitigating, but unfortunately also adapting to, climate changes; curbing pollution; and addressing gender, racial and ethnic inequality.

To address these critical issues, I believe the field will continue to rely on RCTs, but also start using more creatively (descriptively or in combination with RCTs) the huge amount of data that is increasingly available as governments, even in poor countries, digitize their operations. I cannot wait to be surprised by what comes next.

Esther Duflo is The Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics at the Department of Economics, Massachusetts Institute of Technology, Cambridge MA, USA; and cofounder and codirector of the Abdul Latif Jameel Poverty Action Lab (J-PAL) .

Political science

Janet M. Box-Steffensmeier

Political science remains one of the most pluralistic disciplines and we are on the move towards engaged pluralism. This takes us beyond mere tolerance to true, sincere engagement across methods, methodologies, theories and even disciplinary boundaries. Engaged pluralism means doing the hard work of understanding our own research from the multiple perspectives of others.

More data are being collected on human behaviour than ever before and our advances in methods better address the inherent interdependencies of the data across time, space and context. There are new ways to measure human behaviour via text, image and video. Data creation can even go back in time. All these advancements bode well for the potential to better understand and predict behaviour. This ‘data century’ and ‘golden age of methods’ also hold the promise to bridge, not divide, political science, provided that there is engaged methodological pluralism. Qualitative methods provide unique insights and perspectives when joined with quantitative methods, as does a broader conception of the methodologies underlying and launching our research.

I remain a strong proponent of leveraging dynamics and focusing on heterogeneity in our research questions to advance our disciplines. Doing so brings in an explicit perspective of comparison around similarity and difference. Our questions, hypotheses and theories are often made more compelling when considering the dynamics and heterogeneity that emerges when thinking about time and change.

Striving for a better understanding of gender, race and ethnicity is driving deeper and fuller understandings of central questions in the social sciences. The diversity of the research teams themselves across gender, sex, race, ethnicity, first-generation status, religion, ideology, partisanship and cultures also pushes advancement. One area that we need to better support is career diversity. Supporting careers in government, non-profit organizations and industry, as well as academia, for graduate students will enhance our disciplines and accelerate the production of knowledge that changes the world.

Engaged pluralism remains a foundational key to advancement in political science. Engaged pluralism supports critical diversity, equity and inclusion work, strengthens political scientists’ commitment to democratic principles, and encourages civic engagement more broadly. It is an exciting time to be a social scientist.

Janet M. Box-Steffensmeier is Vernal Riffe Professor of Political Science, Professor of Sociology (courtesy) and Distinguished University Professor at the Department of Political Science, Ohio State University, Columbus OH, USA; and immediate past President of the American Political Science Association .

Cognitive psychology

Andrew Perfors

Cognitive psychology excels at understanding questions whose problem-space is well-defined, with precisely specified theories that transparently map onto thoroughly explored experimental paradigms. That means there is a vast gulf between the current state of the art and the richness and complexity of cognition in the real world. The most exciting open questions are about how to bridge that gap without sacrificing rigour and precision. This requires at least three changes.

First, we must move beyond typical experiments. Stimuli must become less artificial, with a naturalistic structure and distribution. Similarly, tasks must become more ecologically valid: less isolated, with more uncertainty, embedded in natural situations and over different time-scales.

Second, we must move beyond considering individuals in isolation. We live in a rich social world and an environment that is heavily shaped by other humans. How we think, learn and act is deeply affected by how other people think and interact with us; cognitive science needs to engage with this more.

Third, we must move beyond the metaphor of humans as computers. Our cognition is deeply intertwined with our emotions, motivations and senses. These are more than just parameters in our minds; they have a complexity and logic of their own, and interact in nontrivial ways with each other and more typical cognitive domains such as learning, reasoning and acting.

How do we make progress on these steps? We need reliable real-world data that are comparable across people and situations, reflect the cognitive processes involved and are not changed by measurement. Technology may help us with this, but challenges surrounding privacy and data quality are huge. Our models and analytic approaches must also grow in complexity — commensurate with the growth in problem and data complexity — without becoming intractable or losing their explanatory power.

Success in this endeavour calls for a different kind of science that is not centred around individual laboratories or small stand-alone projects. The biggest advances will be achieved on the basis of large, rich, real-world datasets from different populations, created and analysed in collaborative teams that span multiple domains, fields and approaches. This requires incentive structures that reward team-focused, slower science and prioritize the systematic construction of reliable knowledge over splashy findings.

Andrew Perfors is Associate Professor and Deputy Director of the Complex Human Data Hub, University of Melbourne, Melbourne, Victoria, Australia .

Cultural and social psychology

Ying-yi Hong

I am writing this at an exceptional moment in human history. For two years, the world has faced the COVID-19 pandemic and there is no end in sight. Cultural and social psychology are uniquely equipped to understand the COVID-19 pandemic, specifically examining how people, communities and countries are dealing with this extreme global crisis — especially at a time when many parts of the world are already experiencing geopolitical upheaval.

During the pandemic, and across different nations and regions, a diverse set of strategies (and subsequent levels of effectiveness) were used to curb the spread of the disease. In the first year of the pandemic, research revealed that some cultural worldviews — such as collectivism (versus individualism) and tight (versus loose) norms — were positively associated with compliance with COVID-19 preventive measures as well as with fewer infections and deaths 29 , 30 . These worldview differences arguably stem from different perspectives on abiding to social norms and prioritizing the collective welfare over an individual’s autonomy and liberty. Although in the short term it seems that a collectivist or tight worldview has been advantageous, it is unclear whether this will remain the case in the long term. Cultural worldviews are ‘tools’ that individuals use to decipher the meaning of their environment, and are dynamic rather than static 31 . Future research can examine how cultural worldviews and global threats co-evolve.

The pandemic has also amplified the demarcation of national, political and other major social categories. On the one hand, identification with some groups (for example, national identity) was found to increase in-group care and thus a greater willingness to sacrifice personal autonomy to comply with COVID-19 measures 32 . On the other hand, identification with other groups (for example, political parties) widened the ideological divide between groups and drove opposing behaviours towards COVID-19 measures and health outcomes 33 . As we are facing climate change and other pressing global challenges, understanding the role of social identities and how they affect worldviews, cognition and behaviour will be vital. How can we foster more inclusive (versus exclusive) identities that can unite rather than divide people and nations?

Ying-yi Hong is Choh-Ming Li Professor of Management and Associate Dean (Research) at the Department of Management, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China .

Developmental psychology

Alison Gopnik

Developmental psychology is similar to the kind of book or band that, paradoxically, everyone agrees is underrated. On the one hand, children and the people who care for them are often undervalued and overlooked. On the other, since Piaget, developmental research has tackled some of the most profound philosophical questions about every kind of human behaviour. This will only continue into the future.

Psychologists increasingly recognize that the minds of children are not just a waystation or an incomplete version of adult minds. Instead, childhood is a distinct evolutionarily adaptive phase of an organism, with its own characteristic cognitions, emotions and motivations. These characteristics of childhood reflect a different agenda than those of the adult mind — a drive to explore rather than exploit. This drive comes with motivations such as curiosity, emotions such as wonder and surprise and remarkable cognitive learning capacities. A new flood of research on curiosity, for example, shows that children actively seek out the information that will help them to learn the most.

The example of curiosity also reflects the exciting prospects for interdisciplinary developmental science. Machine learning is increasingly using children’s learning as a model, and developmental psychologists are developing more precise models as a result. Curiosity-based AI can illuminate both human and machine intelligence. Collaborations with biology are also exciting: for example, in work on evolutionary ‘life history’ explanations of the effects of adverse experiences on later life, and new research on plasticity and sensitive periods in neuroscience. Finally, children are at the cutting edge of culture, and developmental psychologists increasingly conduct a much wider range of cross-cultural studies.

But perhaps the most important development is that policy-makers are finally starting to realize just how crucial children are to important social issues. Developmental science has shown that providing children with the care that they need can decrease poverty, inequality, disease and violence. But that care has been largely invisible to policy-makers and politicians. Understanding scientifically how caregiving works and how to support it more effectively will be the most important challenge for developmental psychology in the next century.

Alison Gopnik is Professor of Psychology and Affiliate Professor of Philosophy at the Department of Psychology, University of California at Berkeley, Berkeley, CA, USA .

Science of science

Cassidy R. Sugimoto

Why study science? The goal of science is to advance knowledge to improve the human condition. It is, therefore, essential that we understand how science operates to maximize efficiency and social good. The metasciences are fields that are devoted to understanding the scientific enterprise. These fields are distinguished by differing epistemologies embedded in their names: the philosophy, history and sociology of science represent canonical metasciences that use theories and methods from their mother disciplines. The ‘science of science’ uses empirical approaches to understand the mechanisms of science. As mid-twentieth-century science historian Derek de Solla Price observed, science of science allows us to “turn the tools of science on science itself” 34 .

Contemporary questions in the science of science investigate, inter alia, catalysts of discovery and innovation, consequences of increased access to scientific information, role of teams in knowledge creation and the implications of social stratification on the scientific enterprise. Investigation of these issues require triangulation of data and integration across the metasciences, to generate robust theories, model on valid assumptions and interpret results appropriately. Community-owned infrastructure and collective venues for communication are essential to achieve these goals. The construction of large-scale science observatories, for example, would provide an opportunity to capture the rapidly expanding dataverse, collaborate and share data, and provide nimble translations of data into information for policy-makers and the scientific community.

The topical foci of the field are also undergoing rapid transformation. The expansion of datasets enables researchers to analyse a fuller population, rather than a narrow sample that favours particular communities. The field has moved from an elitist focus on ‘success’ and ‘impact’ to a more-inclusive and prosopographical perspective. Conversations have shifted from citations, impact factors and h -indices towards responsible indicators, diversity and broader impacts. Instead of asking ‘how can we predict the next Nobel prize winner?’, we can ask ‘what are the consequences of attrition in the scientific workforce?’. The turn towards contextualized measurements that use more inclusive datasets to understand the entire system of science places the science of science in a ripe position to inform policy and propel us towards a more innovative and equitable future.

Cassidy R. Sugimoto is Professor and Tom and Marie Patton School Chair, School of Public Policy, Georgia Institute of Technology, Atlanta, GA, USA .

Sari Hanafi

In the past few years, we have been living through times in which reasonable debate has become impossible. Demagogical times are driven by the vertiginous rise of populism and authoritarianism, which we saw in the triumph of Donald Trump in the USA and numerous other populist or authoritarian leaders in many places around the globe. There are some pressing tasks for sociology that can be, in brief, reduced to three.

First, fostering democracy and the democratization process requires disentangling the constitutive values that compose the liberal political project (personal liberty, equality, moral autonomy and multiculturalism) to address the question of social justice and to accommodate the surge in people’s religiosity in many parts in the globe.

Second, the struggle for the environment is inseparable from our choice of political economy, and from the nature of our desired economic system — and these connections between human beings and nature have never been as intimate as they are now. Past decades saw rapid growth that was based on assumptions of the long-term stability of the fixed costs of raw materials and energy. But this is no longer the case. More recently, financial speculation intensified and profits shrunk, generating distributional conflicts between workers, management, owners and tax authorities. The nature of our economic system is now in acute crisis.

The answer lies in a consciously slow-growing new economy that incorporates the biophysical foundations of economics into its functioning mechanisms. Society and nature cannot continue to be perceived each as differentiated into separate compartments. The spheres of nature, culture, politics, social, economy and religion are indeed traversed by common logics that allow a given society to be encompassed in its totality, exactly as Marcel Mauss 35 did. The logic of power and interests embodied in ‘ Homo economicus ’ prevents us from being able to see the potentiality of human beings to cultivate gift-giving practices as an anthropological foundation innate within social relationships.

Third, there are serious social effects of digitalized forms of labour and the trend of replacing labour with an automaton. Even if digital labour partially reduces the unemployment rate, the lack of social protection for digital labourers would have tremendous effects on future generations.

In brief, it is time to connect sociology to moral and political philosophy to address fundamentally post-COVID-19 challenges.

Sari Hanafi is Professor of Sociology at the American University of Beirut, Beirut, Lebanon; and President of the International Sociological Association .

Environmental studies (climate change)

Yasuko Kameyama

Climate change has been discussed for more than 40 years as a multilateral issue that poses a great threat to humankind and ecosystems. Unfortunately, we are still talking about the same issue today. Why can’t we solve this problem, even though scientists pointed out its importance and urgency so many years ago?

These past years have been spent trying to prove the causal relationship between an increase in greenhouse gas concentrations, global temperature rise and various extreme weather events, as well as developing and disseminating technologies needed to reduce emissions. All of these tasks have been handled by experts in the field. At the same time, the general public invested little time in this movement, probably expecting that the problem would be solved by experts and policy-makers. But that has not been the case. No matter how much scientists have emphasized the crisis of climate change or how many clean energy technologies engineers have developed, society has resisted making the necessary changes. Now, the chances of keeping the temperature rise within 1.5 °C of pre-industrial levels — the goal necessary to minimize the effects of climate change — are diminishing.

We seem to finally be realizing the importance of social scientific knowledge. People need to take scientific information seriously for clean technology to be quickly diffused. Companies are more interested in investing in newer technology and product development when they know that their products will sell. Because environmental problems are caused by human activity, research on human behaviour is indispensable in solving these problems.

Reports by the Intergovernmental Panel on Climate Change (IPCC) have not devoted many pages to the areas of human awareness and behaviour ( https://www.ipcc.ch/ ). The IPCC’s Third Working Group, which deals with mitigation measures, has partially spotlighted research on institutions, as well as on concepts such as fairness. People’s perception of climate change and the relationship between perception and behavioural change differ depending on the country, societal structure and culture. Additional studies in these areas are required and, for that purpose, more studies from regions such as Asia, Africa and South America, which are underrepresented in terms of the number of academic publications, are particularly needed.

Yasuko Kameyama is Director, Social Systems Division, National Institute for Environmental Studies, Tsukuba, Japan .

Sustainability (food systems)

Mario Herrero

The food system is in dire straits. Food demand is unprecedented, while malnutrition in all its forms (obesity, undernutrition and micronutrient deficiencies) is rampant. Environmental degradation is pervasive and increasing, and if it continues, the comfort zone for humanity and ecosystems to thrive will be seriously compromised. From bruises and shapes to sell-by dates, we tend to find many reasons to exclude perfectly edible food from our plates, whereas in other cases not enough food reaches hungry mouths owing to farming methods, pests and lack of adequate storage. These types of inequalities are common and — together with inherent perverse incentives that maintain the status quo of how we produce, consume and waste increasingly cheap and processed food — they are launching us towards a disaster.

We are banking on a substantial transformation of the food system to solve this conundrum. Modifying food consumption and waste patterns are central to the plan for achieving healthier diets, while increasing the sustainability of our food system. This is also an attractive policy proposition, as it could lead to gains in several sectors. Noncommunicable diseases such as obesity, diabetes and heart disease could decline, while reducing the effects of climate change, deforestation, excessive water withdrawals and biodiversity loss, and their enormous associated — and largely unaccounted — costs.

Modifying our food consumption and waste patterns is very hard, and unfortunately we know very little about how to change them at scale. Yes, many pilots and small examples exist on pricing, procurement, food environments and others, but the evidence is scarce, and the magnitude of the change required demands an unprecedented transdisciplinary research agenda. The problem is at the centre of human agency and behaviour, embodying culture, habits, values, social status, economics and all aspects of agri-food systems. Certainly, one of the big research areas for the next decade if we are to reach the Sustainable Development Goals leaving no one behind.

Mario Herrero is Professor, Cornell Atkinson scholar and Nancy and Peter Meinig Family Investigator in the Life Sciences at the Department of Global Development, College of Agriculture and Life Sciences and Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, USA .

Cultural evolution

Laurel Fogarty

Humans are the ultimate ‘cultural animals’. We are innovative, pass our cultures to one another across generations and build vast self-constructed environments that reflect our cultural biases. We achieve things using our cultural capacities that are unimaginable for any other species on earth. And yet we have only begun to understand the dynamics of cultural change, the drivers of cultural complexity or the ways that we adapt culturally to changing environments. Scholars — anthropologists, archaeologists and sociologists — have long studied culture, aiming to describe and understand its staggering diversity. The relatively new field of cultural evolution has different aims, one of the most important of which is to understand the mechanics in the background — what general principles, if any, govern human cultural change?

Although the analogy of culture as an evolutionary process has been made since at least the time of Darwin 36 , 37 , cultural evolution as a robust field of study is much younger. From its beginnings with the pioneering work of Cavalli-Sforza & Feldman 38 , 39 , 40 and Boyd & Richerson 41 , 42 , the field of cultural evolution has been heavily theoretical. It has drawn on models from genetic evolution 40 , 43 , 44 , 45 , ecology 46 , 47 and epidemiology 40 , 48 , extending and adapting them to account for unique and important aspects of cultural transmission. Indeed, in its short life, the field of cultural evolution has largely been dominated by a growing body of theory that ensured that the fledgling field started out on solid foundations. Because it underpins and makes possible novel applications of cultural evolutionary ideas, theoretical cultural evolution’s continued development is not only crucial to the field’s growth but also represents some of its most exciting future work.

One of the most urgent tasks for cultural evolution researchers in the next five years is to develop, alongside its theoretical foundations, robust principles of application 49 , 50 , 51 . In other words, it is vital to develop our understanding of what we can — and, crucially, cannot — infer from different types of cultural data. Where do we draw those boundaries and how can we apply cultural evolutionary theory to cultural datasets in a principled way? The tandem development of robust theory and principled application has the potential to strengthen cultural evolution as a robust, useful and ground-breaking inferential science of human behaviour.

Laurel Fogarty is Senior Scientist at the Department of Human Behaviour, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany .

Over the past decade, research using molecular genetic data has confirmed one of the main conclusions of twin studies: all human behaviour is partly heritable 52 , 53 . Attempts at examining the link between genetics and behaviour have been met with concerns that the findings can be abused to justify discrimination — and there are good historical grounds for these concerns. However, these findings also show that ignoring the contribution of genes to variation in human behaviour could be detrimental to a complete understanding of social phenomena, given the complex ways that genes and environment interact.

Uncovering these complex pathways has become feasible only recently thanks to rapid technological progress reducing the costs of genotyping. Sample sizes in genome-wide association studies (GWAS) have risen from tens of thousands to millions in the past decade, reporting thousands of genetic variants associated with different behaviours 54 , 55 , 56 , 57 . New ways to use GWAS results have emerged, the most important one arguably being a method to aggregate the additive effects of many genetic variants into a ‘polygenic index’ (PGI) (also known as a ‘polygenic score’) that summarizes an individual’s genetic propensity towards a trait or behaviour 58 , 59 . Being aggregate measures, PGIs capture a much larger share of the variance in the trait of interest compared to individual genetic variants 60 . Thus, they have paved the way for follow-up studies with smaller sample sizes but deeper phenotyping compared to the original GWAS, allowing researchers to, for example, analyse the channels through which genes operate 61 , 62 , how they interact with the environment 63 , 64 , and account for confounding bias and boost statistical power by controlling for genetic effects 65 , 66 .

Useful as they are, PGIs and the GWAS that they are based on can suffer from confounding due to environmental factors that correlate with genotypes, such as population stratification, indirect effect from relatives or assortative mating 67 . Now that the availability of genetic data enables large-scale within-family GWAS, the next big thing in behaviour genetic research will be disentangling these sources 68 . While carrying the progress further, it is important that the field prioritizes moving away from its currently predominant Eurocentric bias by extending data collection and analyses to individuals of non-European ancestries, as the exclusion of non-European ancestries from genetic research has the potential to exacerbate health disparities 69 . Researchers should also be careful to communicate their findings clearly and responsibly to the public and guard against their misappropriation by attempts to fuel discriminatory action and discourse 70 .

Aysu Okbay is Assistant Professor at the Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands .

Cognitive neuroscience

Anna C. Nobre

Since the ‘decade of the brain’ in the 1990s, ingenuity in cognitive neuroscience has focused on measuring and analysing brain signals. Adapting tools from statistics, engineering, computer science, physics and other disciplines, we studied activity, states, connectivity, interactions, time courses and dynamics in brain regions and networks. Unexpected findings about the brain yielded important insights about the mind.

Now is a propitious time to upgrade the brain–mind duumvirate to a brain–mind–behaviour triumvirate. Brain and mind are embodied, and their workings are expressed through various effectors. Yet, experimental tasks typically use simple responses to capture complex psychological functions. Often, a button press — with its limited dimensions of latency and accuracy — measures anticipating, focusing, evaluating, choosing, reflecting or remembering. Researchers venturing beyond such simple responses are uncovering how the contents of mind can be studied using various continuous measures, such as pupil diameter, gaze shifts and movement trajectories.

Most tasks also restrict participants’ movements to ensure experimental control. However, we are learning that principles of cognition derived in artificial laboratory contexts can fail to generalize to natural behaviour. Virtual reality should prove a powerful methodology. Participants can behave naturally, and experimenters can control stimulation and obtain quality measures of gaze, hand and body movements. Noninvasive neurophysiology methods are becoming increasingly portable. Exciting immersive brain–mind–behaviour studies are just ahead.

The next necessary step is out of the academic bubble. Today the richest data on human behaviour belong to the information and technology industries. In our routines, we contribute data streams through telephones, keyboards, watches, vehicles and countless smart devices in the internet of things. These expose properties such as processing speed, fluency, attention, dexterity, navigation and social context. We supplement these by broadcasting feelings, attitudes and opinions through social media and other forums. The richness and scale of the resulting big data offer unprecedented opportunities for deriving predictive patterns that are relevant to understanding human cognition (and its disorders). The outcomes can then guide further hypothesis-driven experimentation. Cognitive neuroscience is intrinsically collaborative, combining a broad spectrum of disciplines to study the mind. Its challenge now is to move from a multidisciplinary to a multi-enterprise science.

Anna C. Nobre is Chair in Translational Cognitive Neuroscience at the Department of Experimental Psychology, University of Oxford, UK; and Director of Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, UK .

Social and affective neuroscience

Tatia M. C. Lee

Social and affective neuroscience is a relatively new, but rapidly developing, field of neuroscience. Social and affective neuroscience research takes a multilevel approach to make sense of socioaffective processes, focusing on macro- (for example, social environments and structures), meso- (for example, social interactions) and micro (for example, socio-affective neural processes and perceptions)-level interactions. Because the products of these interactions are person-specific, the conventional application of group-averaged mechanisms to understand the brain in a socioemotional context has been reconsidered. Researchers turn to ecologically valid stimuli (for example, dynamic and virtual reality instead of static stimuli) and experimental settings (for example, real-time social interaction) 71 to address interindividual differences in social and affective responses. At the neural level, there has been a shift of research focus from local neural activations to large-scale synchronized interactions across neural networks. Network science contributes to the understanding of dynamic changes of neural processes that reflect the interactions and interconnection of neural structures that underpin social and affective processes.

We are living in an ever-changing socioaffective world, full of unexpected challenges. The ageing population and an increasing prevalence of depression are social phenomena on a global scale. Social isolation and loneliness caused by measures to tackle the current pandemic affect physical and psychological well-being of people from all walks of life. These global issues require timely research efforts to generate potential solutions. In this regard, social and affective neuroscience research using computational modelling, longitudinal research designs and multimodal data integration will create knowledge about the basis of adaptive and maladaptive social and affective neurobehavioural processes and responses 72 , 73 , 74 . Such knowledge offers important insights into the precise delineation of brain–symptom relationships, and hence the development of prediction models of cognitive and socioaffective functioning (for example, refs. 75 , 76 ). Therefore, screening tools for identifying potential vulnerabilities can be developed, and timely and precise interventions can be tailored to meet individual situations and needs. The translational application of social and affective neuroscience research to precision medicine (and policy) is experiencing unprecedented demand, and such demand is met with unprecedented clinical and research capabilities.

Tatia M. C. Lee is Chair Professor of Psychology at the State Key Laboratory of Brain and Cognitive Sciences and Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong Special Administrative Region, China .

Maurizio Corbetta

Focal brain disorders, including stroke, trauma and epilepsy, are the main causes of disability and loss of productivity in the world, and carry a cumulative cost in Europe of about € 500 billion per year 77 . The disease process affects a specific circuit in the brain by turning it off (as in stroke) or pathologically turning it on (as in epilepsy). The cause of the disabling symptoms is typically local circuit damage. However, there is now overwhelming evidence that symptoms reflect not only local pathology but also widespread (network) functional abnormalities. For instance, in stroke, an average lesion — the size of a golf ball — typically alters the activity of on average 25% of all brain connections. Furthermore, normalization of these abnormalities correlates with optimal recovery of function 78 , 79 .

One exciting treatment opportunity is ‘circuit-based’ stimulation: an ensemble of methods (optogenetic, photoacoustic, electrochemical, magnetic and electrical) that have the potential to normalize activity. Presently, this type of therapy is limited by numerous factors, including a lack of knowledge about the circuits, the difficulty of mapping these circuits in single patients and, most importantly, a principled understanding of where and how to stimulate to produce functional recovery.

A possible solution lies in a strategy (developed with G. Deco, M. Massimini and M. Sanchez-Vivez) that starts with an in-depth assessment of behaviour and physiological studies of brain activity to characterize the affected circuits and associated patterns of functional abnormalities. Such a multi-dimensional physiological map of a lesioned brain can be then fed to biologically realistic in silico models 80 . A model of a lesioned brain affords the opportunity to explore, in an exhaustive way, different kinds of stimulation to normalize faulty activity. Once a suitable protocol is found it can be exported first to animal models, and then to humans. Stimulation alone will not be enough. Pairing with behavioural training (rehabilitation) will stabilize learning and normalize connections.

The ability to interface therapy (stimulation, rehabilitation and drugs) with brain signals or other kinds of behavioural sensor offers another exciting opportunity, to open the ‘brain’s black box’. Most current treatments in neuroscience are given with no regard to their effect on the underlying brain signals or behaviour. Giving patients conscious access to their own brain signals may substantially enhance recovery, as the brain is now in the position to use its own powerful connections and learning mechanisms to cure itself.

Maurizio Corbetta is Professor and Chair of Neurology at the Department of Neuroscience and Director of the Padova Neuroscience Center (PNC), University of Padova, Italy; and Principal Investigator at the Venetian Institute of Molecular Medicine (VIMM), Padova, Italy .

Merete Nordentoft

Schizophrenia and related psychotic disorders are among the costliest and most debilitating disorders in terms of personal sufferings for those affected, for relatives and for society 81 . These disorders often require long-term treatment and, for a substantial proportion of the patients, the outcomes are poor. This has motivated efforts to prevent long-lasting illness by early intervention. The time around the onset of psychotic disorders is associated with an increased risk of suicide, of loss of affiliation with the labour market, and social isolation and exclusion. Therefore, prevention and treatment of first-episode psychosis will be a key challenge for the future.

There is now solid evidence proving that early intervention services can improve clinical outcomes 82 . This was first demonstrated in the large Danish OPUS trial, in which OPUS treatment — consisting of assertive outreach, case management and family involvement, provided by multidisciplinary teams over a two-year period — was shown to improve clinical outcomes 83 . Moreover, it was also cost-effective 84 . Although the positive effects on clinical outcomes were not sustainable after five and ten years, there was a long-lasting effect on use of supported housing facilities (indicating improved ability to live independently) 85 . Later trials proved that it is possible to maintain the positive clinical outcomes by extending the services to five years or by offering a stepped care model with continued intensive care for the patients who are most impaired 86 . However, even though both clinical and functional outcomes (such as labour market affiliation) can be improved by evidence-based treatments 82 , a large group of patients with first-episode psychosis still have psychotic symptoms after ten years. Thus, there is still an urgent need for identification of new and better options for treatment.

Most probably, some of the disease processes start long before first onset of a psychotic disorder. Thus, identifying disease mechanisms and possibilities for intervention before onset of psychosis will be extremely valuable. Evidence for effective preventive interventions is very limited, and the most burning question — of how to prevent psychosis — is still open.

The early intervention approach is also promising also for other disorders, including bipolar affective disorder, depression, anxiety, eating disorders, personality disorders, autism and attention-deficient hyperactivity disorder.

Merete Nordentoft is Clinical Professor at the Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; and Principal Investigator, CORE - Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark .

Epidemiology

Gabriel M. Leung

In a widely anthologized article from the business field of marketing, Levitt 87 pointed out that often industries failed to grow because they suffered from a limited market view. For example, Kodak went bust because it narrowly defined itself as a film camera company for still photography rather than one that should have been about imaging writ large. If it had had that strategic insight, it would have exploited and invested in digital technologies aggressively and perhaps gone down the rather more successful path of Fujifilm — or even developed into territory now cornered by Netflix.

The raison d’être of epidemiology has been to provide a set of robust scientific methods that underpin public health practice. In turn, the field of public health has expanded to fulfil the much-wider and more-intensive demands of protecting, maintaining and promoting the health of local and global populations, intergenerationally. At its broadest, the mission of public health should be to advance social justice towards a complete state of health.

Therefore, epidemiologists should continue to recruit and embrace relevant methodology sets that could answer public health questions, better and more efficiently. For instance, Davey Smith and Ebrahim 88 described how epidemiology adapted instrumental variable analysis that had been widely deployed in econometrics to fundamentally improve causal inference in observational epidemiology. Conversely, economists have not been shy in adopting the randomized controlled trial design to answer questions of development, and have recognized it with a Nobel prize 89 . COVID-19 has brought mathematical epidemiology or modelling to the fore. The foundations of the field borrowed heavily from population dynamics and ecological theory.

In future, classical epidemiology, which has mostly focused on studying how the exposome associates with the phenome, needs to take into simultaneous account the other layers of the multiomics universe — from the genome to the metabolome to the microbiome 90 . Another area requiring innovative thinking concerns how to harness big data to better understand human behaviour 91 . Finally, we must consider key questions that are amenable to epidemiologic investigation arising from the major global health challenges: climate change, harmful addictions and mental wellness. What new methodological tools do we need to answer these questions?

Epidemiologists must keep trying on new lenses that correct our own siloed myopia.

Gabriel M. Leung is Helen and Francis Zimmern Professor in Population Health at WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong; Chief Scientific Officer at Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park; and Dean of Medicine at the University of Hong Kong, Hong Kong Special Administrative Region, China .

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Correspondence to Janet M. Box-Steffensmeier , Jean Burgess , Maurizio Corbetta , Kate Crawford , Esther Duflo , Laurel Fogarty , Alison Gopnik , Sari Hanafi , Mario Herrero , Ying-yi Hong , Yasuko Kameyama , Tatia M. C. Lee , Gabriel M. Leung , Daniel S. Nagin , Anna C. Nobre , Merete Nordentoft , Aysu Okbay , Andrew Perfors , Laura M. Rival , Cassidy R. Sugimoto , Bertil Tungodden or Claudia Wagner .

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Box-Steffensmeier, J.M., Burgess, J., Corbetta, M. et al. The future of human behaviour research. Nat Hum Behav 6 , 15–24 (2022). https://doi.org/10.1038/s41562-021-01275-6

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Scientific support for applied behavior analysis from the neurobehavioral unit (nbu).

Over the past 40 years, an extensive body of literature has documented the successful use of ABA-based procedures to reduce problem behavior and increase appropriate skills for individuals with intellectual disabilities (ID), autism, and related disorders. The literature consists of numerous controlled studies employing single-case experimental designs, consecutive controlled case-series studies, controlled group studies, and some randomized controlled trials.

Types of Research Designs:

A number of different research designs are used to evaluate treatments and answer other questions about treatment procedures. Each type of design has its own scientific and practical strengths and limitations, and each is ideally suited to answer particular types of questions. The designs are discussed further below.

Single-case experimental designs:

Many studies demonstrating the outcomes obtained with ABA-based procedures use single-case experimental designs (also termed “single-subject designs”; Kazdin, 2010 & 2013) because this type of design is ideal for examining how the behavior of an individual changes as a function of changes in the environment – which is the subject of interest in the field of ABA. These studies often include a small number of individuals (typically between one to four). It should be noted that published studies using single-case experimental designs are not the same as “case reports” (often seen in clinical journals), which are typically simply descriptive in nature. Rather, studies using single-case designs are controlled studies where treatment is applied in a manner that allows one to demonstrate that the treatment was responsible for the change in behavior. These studies are methodologically rigorous because they involve direct observation of behavior and objective data collection where behaviors are defined and counted (often using a computerized data collection system). A second observer also collects data independently to ensure reliable and accurate data collection.

The most common type of single-case design is a reversal design, which involves the following: a pre-treatment baseline level of behavior is obtained, then treatment is applied, and after a change is observed, the treatment is withdrawn, then reapplied to replicate the treatment effect (Kazdin, 2010; Kratochwill & Levin, 2010). The “replication” of the treatment effect illustrates that the treatment (and not some other event) is responsible for the change. This type of design has excellent “internal validity,” which refers to the extent to which the change in behavior can be attributed to the intervention and not some other variable. Single-case designs are limited, however, in that one cannot determine the extent to which the findings for one study are applicable to other individuals or situations (that is, it has weak “external validity”). It is possible that only cases for which treatment was successful were included in the published study (a concern termed “publication bias”). On the other hand, the ABA literature spans four decades and describes the efficacy of these treatments across a wide range of populations, settings, and problems. Collectively, this extensive body of literature provides strong evidence supporting the external validity of ABA-based interventions.

In the field of ABA, single-case experimental designs are not reserved for exclusive use in research studies. Rather, their use represents good clinical practice. During assessment, single-case designs permit one to identify what factors cause the behavior in question. These findings are then prescriptive for developing an individualized treatment. In addition, single-case designs enable one to determine whether a prescribed treatment (or what particular elements of a treatment) is responsible for behavior change. Isolating the active ingredients of treatment is crucial in saving time and resources.

Consecutive controlled case series designs:

Consecutive controlled case-series studies describe a series of cases where single-case experimental designs were used (see Rooker et al., 2013 for a recent example). These studies describe all individuals encountered who were treated with a certain procedure (regardless of whether the treatment was effective or not), and thus have better external validity than cases involving fewer participants. Because all the cases in the series evaluated treatment using single-case experimental designs, consecutive controlled case-series studies have excellent internal validity as well. Moreover, because a large number of individuals are included, they provide an opportunity to answer other questions, including determining what characteristics predict good outcomes. Several large scale consecutive controlled case series studies describing ABA-based assessment and treatment procedures have been published, and their findings nicely correspond to the broader body of single-case studies describing smaller numbers of individuals.

Group designs:

In contrast to single-case experimental designs where the individual’s behavior change during treatment is compared to his/her own behavior without treatment, group designs evaluate treatments based on a comparison of a group of individuals receiving one treatment relative to another similar group of individuals who received no treatment (or a different treatment; Kazdin, 2003). In contrast to single-case designs, where the behaviors of an individual are observed extensively and repeatedly (often for many hours or days) before and after treatment, group designs involve fewer observations of each individual in the group but obtain these measures across large numbers of individuals. Statistical analyses are used to determine whether overall differences between the groups are large enough to conclude that they are not due to normal variation or “chance” (Cohen, Cohen, West, & Aiken, 2003).

The most rigorous type of group design is a randomized controlled trial, which involves randomly assigning participants to a particular group (e.g., treatment or no treatment), and observers who evaluate the outcomes of the treatment do not know whether the participant received treatment or not (i.e., observers are “blinded”). When certain types of treatments, such as medications are being evaluated, the participant may also be “blind” to which group s/he is assigned through the administration of an inactive pill (a placebo). Several group studies describing comprehensive ABA-based interventions for individuals with autism have been published, including some that have used randomization (e.g., Sallows & Graupner, 2005; Smith, Groen, & Wynn, 2000). The most appropriate design to use in a particular situation depends on numerous factors, including the research question, consideration of the relative costs and benefits to participants, and the current state of knowledge about the topic of interest.

Findings from Controlled Studies Employing Single-Case Experimental Designs:

Small-n controlled studies:.

Over a thousand studies reporting on ABA-based assessment and treatment techniques have been published since the 1960’s. As discussed in the “types of research designs” section above, these controlled studies have strong internal validity as they use experimental designs that permit one to conclude that the intervention was responsible for the change in behavior. Studies on topics relevant to the use of ABA with persons with intellectual and developmental disabilities are most frequently published in journals such as: Behavioral Interventions, Journal of Applied Behavior Analysis, Journal of Autism and Developmental Disorders, Journal of Intellectual Disability Research, Research in Developmental Disabilities, Research in Autism Spectrum Disorders. Topics of these studies include communication training, social skills training, behavioral assessment and treatment of problem behavior (e.g., self-injury, aggression), educational instruction, early intensive behavioral intervention, etc. For further information, the reader is referred to these journals or to an on-line search engine (i.e., PsychINFO, Google Scholar).

Consecutive Case-Series Studies:

As discussed on the types of research designs section above, consecutive controlled case-series studies describe a series of cases where single-case experimental designs were used with all individuals encountered (regardless of whether the treatment was effective or not).

Functional Analysis of Problem Behavior:

Focused ABA interventions for problem behavior are designed for each individual based on an understanding of what antecedents may “trigger” problem behavior and what consequences may reinforce (reward) it. Functional behavioral assessment can be performed using a range of procedures, including interviews, questionnaires, direct observation in the individual’s natural setting, and / or systematically presenting situations that can function as potential triggers or rewards and observing and recording how behavior changes with these events. This latter type of procedure, called a functional analysis, is the most rigorous type of functional behavioral assessment. In most cases, the results can reveal why problem behavior occurs and persists – and thus provides a foundation for focused interventions targeting these behaviors.

Literature reviews by Hanley, Iwata, and McCord (2003) and Beavers, Iwata, and Lerman (2013) collectively identified 435 peer-reviewed articles where functional analysis of problem behavior was reported. Studies listed below represent a sample of the large-scale consecutive controlled case series studies involving functional analysis. These studies demonstrate that functional analysis is highly effective in identifying the controlling variables for problem behavior.

Functional analysis across a variety of settings (inpatient, residential) Participants: 154 cases Results: Conclusive assessment results in over 90% of cases Reference: Iwata BA, Pace GM, Dorsey MF, Zarcone JR, Vollmer TR, Smith RG, Rodgers TA, Lerman DC, Shore BA, Mazalesk JL, et al. (1994). The functions of self-injurious behavior: An experimental-epidemiological analysis . Journal of Applied Behavior Analysis, 27(2) , 215-240.

Functional analysis in school settings Participants: 69 cases Results: Conclusive assessment results in over 90% of cases Reference: Mueller MM, Nkosi A, Hine JF. (2011). Functional analysis in public schools: A summary of 90 functional analyses . Journal of Applied Behavior Analysis, 44(4) , 807-818.

Functional analysis of severe problem behavior Participants: 176 cases with severe problem behavior Results: Conclusive assessment results in over 90% of cases References: Hagopian LP, Rooker GW , Jessel J, DeLeon IG . (2013). Initial functional analysis outcomes and modifications in pursuit of differentiation: A summary of 176 inpatient cases . Journal of Applied Behavior Analysis, 46(1) , 88-100.

ABA-Based Focused Treatment for Problem Behavior:

Studies employing rigorous single-case experimental designs describing ABA focused interventions for problem behavior have been reported for four decades. The following sample of large-scale consecutive controlled case series studies provide further support for the effectiveness of these interventions. Findings from these studies parallel findings from reviews and meta-analysis of small-n studies.

Functional communication training for treatment of problem behavior Participants: 21 inpatient cases with IDD Results: 80% or greater reduction in problem behavior in 90% of cases Reference: Hagopian LP , Fisher WW, Sullivan MT, Acquisto J, LeBlanc LA. (1998). Effectiveness of functional communication training with and without extinction and punishment: A summary of 21 inpatient cases . Journal of Applied Behavior Analysis, 31(2) , 211-235.

Function-based treatment for severe problem behavior Participants: 138 inpatient cases with IDD Results: 90% or greater reduction in problem behavior in over 83% of cases Reference: Asmus JM, Ringdahl JE, Sellers JA, Call NA, Andelman MS, Wacker DP. (2004). Use of a short-term inpatient model to evaluate aberrant behavior: Outcome data summaries from 1996 to 2001 . Journal of Applied Behavior Analysis, 37(3) , 283-304.

Functional-based treatment delivered by care providers (mostly parents) for severe problem behavior Participants: 42 outpatient cases with IDD Results: 80% or greater reduction in problem behavior in 95% of cases Reference: Kurtz PF , Fodstad JC, Huete JM, Hagopian LP . (2013). Caregiver- and staff-conducted functional analysis outcomes: A summary of 52 cases . Journal of Applied Behavior Analysis, 46(4) , 738-749.

Functional communication training for treatment of severe problem behavior Participants: 50 inpatient and outpatient cases with IDD Results: 80% or greater reduction in problem behavior in 86% of cases Reference: Rooker GW , Jessel J, Kurtz PF, Hagopian LP . (2013). Functional communication training with and without alternative reinforcement and punishment: An analysis of 58 applications . Journal of Applied Behavior Analysis, 46(4) , 708-722.

Review Papers:

Broadly speaking, review papers summarize the published literature on a specific topic (e.g., diagnosis, type of assessment or treatment procedure). The reader is referred to recent reviews on comprehensive and focused ABA-based interventions for problems associated with autism:

Anderson C, Law JK , Daniels A, Rice C, Mandell DS, Hagopian L , Law PA. (2012). Occurrence and family impact of elopement in children with autism spectrum disorders . Pediatrics, 130(5) , 870-877.
Dawson G, Burner K. (2011). Behavioral interventions in children and adolescents with autism spectrum disorder: A review of recent findings . Current Opinion in Pediatrics, 23(6) , 616-620.
Doehring P, Reichow R, Palk T, Phillips C, Hagopian L . (2012). Behavioral approaches to managing severe problem behaviors in children with Autism Spectrum and Related Developmental Disorders: A descriptive analysis . Child and Adolescent Psychiatry Clinics of NA, 23(1), 25-40.
Lang R, Mahoney R, El Zein F, Delaune E, Amidon M. (2011). Evidence to practice: Treatment of anxiety in individuals with autism spectrum disorders . Neuropsychiatric Disease and Treatment, 7 , 27-30.
Myers SM, Johnson CP. (2007). Management of children with autism spectrum disorders . Pediatrics, 120(5) , 1162-1182.
Reichow B, Volkmar FR. (2010). Social skills interventions for individuals with autism: Evaluation for evidence-based practices within a best evidence synthesis framework . Journal of Autism and Developmental Disorders, 40(2), 149-166.

Recent reviews on ABA-based procedures for persons with intellectual and developmental disabilities (IDD):

Brosnan J, Healy O. (2011). A review of behavioral interventions for the treatment of aggression in individuals with developmental disabilities . Research in Developmental Disabilities, 32(2) , 437-446.
Hanley GP, Iwata BA, McCord BE. (2003). Functional analysis of problem behavior: A review . Journal of Applied Behavior Analysis, 36(2) , 147–185.
Kahng S, Iwata BA, Lewin AB. (2002). Behavioral treatment of self-injury, 1964 to 2000 . American Journal on Mental Retardation, 107(3) , 212-221.
Lang R, Rispoli M, Machalicek W, White PJ, Kang S, Pierce N, Mulloy A, Fragale T, O'Reilly M, Sigafoos J, Lancioni G. (2009). Treatment of elopement in individuals with developmental disabilities: A systematic review . Research in Developmental Disabilities, 30(4) , 670-681.
Lilienfeld SO. (2005). Scientifically unsupported and supported interventions for childhood psychopathology: A summary . Pediatrics, 115(3) , 761-764.
Sturmey P. (2002). Mental retardation and concurrent psychiatric disorder: Assessment and treatment . Current Opinion in Psychiatry, 15 , 489-495.
Tiger JH, Hanley GP, Bruzek J. (2008). Functional communication training: A review and practical guide . Behavior Analysis in Practice, 1(1) , 16-23.

Review articles indicating that treatments for autism and intellectual disability derived from ABA-based procedures are empirically supported treatments also have been published in non-behavioral journals. For example, the journal Current Opinion in Psychiatry is a journal designed to assist clinicians and researchers by synthesizing the psychiatric literature. An article that reviewed the assessment and treatment of individuals with intellectual disabilities and psychiatric disorders concluded that: "Interventions based on applied behavior analysis have the strongest empirical basis, although there is some evidence that other therapies have promise" (Sturmey, 2002, p. 489). Also, in the journal Pediatrics, the official journal of the American Academy of Pediatrics (AAP), an article offering guidelines on scientifically supported treatments for childhood psychiatric disorders concluded: "The most efficacious psychosocial treatment for autism is applied behavior analysis" (Lilienfeld, 2005, p. 762). The AAP issued a Clinical Report in Pediatrics regarding the management of children with autism, and the authors noted: “Children who receive early intensive behavioral treatment have been shown to make substantial, sustained gains in IQ, language, academic performance, and adaptive behavior as well as some measures of social behavior, and their outcomes have been significantly better than those of children in control groups” (Myers, & Johnson, 2007, p. 1164). In the Archives of Pediatric and Adolescent Medicine, Barbaresi et al. (2006) concluded, “ABA should be viewed as the optimal, comprehensive treatment approach in young children with ASD.”

Review papers finding support for ABA can be found in the following non-behavioral journals:

Current Opinion in Psychiatry (Grey & Hastings, 2005; Sturmey, 2002)
Scientific Review of Mental Health Practice (Herbert, Sharp, & Gaudiano, 2002)
American Journal on Mental Retardation (Kahng, Iwata, & Lewin, 2002)
Psychiatric Times (Erickson, Swiezy, Stigler, McDougle, & Posey, 2005)
Archives of Pediatric and Adolescent Medicine (Barbaresi, Katusic, & Voigt, 2006)
Child and Adolescent Psychiatric Clinics of North America (Doehring, Reichow, Palka, Phillips, & Hagopian, 2014)

Meta-Analyses:

In general, meta-analysis involves quantitative re-analysis of data reported in published studies. This requires standardizing treatment outcomes by statistically calculating “effect sizes” obtained within each study, for the purpose of evaluating data obtained across a group of studies on a particular treatment.

Similarly, seven meta-analyses (Campbell, 2003; Didden, Duker, & Korzilius, 1997; Harvey, Boer, Meyer, & Evans, 2009; Heyvaert, Maes, Van den Noortgate, Kuppens, & Onghena, 2012; Lundervold & Bourland, 1988; Ma, 2009; Weisz, Weiss, Han, Granger, & Morton, 1995) that collectively analyzed hundreds of studies concluded that ABA-based procedures were more effective for reducing problem behavior displayed by individuals with ID (as well as typically-developing individuals) than were alternative treatments. The large body of literature reviewed in these studies provides empirical evidence indicating that focused ABA interventions are effective at assessing and treating a variety of socially important behaviors emitted by individuals with a variety of diagnoses.

Furthermore, several meta-analytic studies also have found comprehensive ABA-based approaches for educating children with autism result in favorable outcomes (Eldevik, Hastings, Hughes, Jahr, Eikeseth, & Cross, 2010; Makrygianni & Reed, 2010; Reichow, 2012; Reichow, Barton, Boyd, & Hume, 2012; Virues-Ortega, 2010). In a recent meta-analytic study involving 22 studies, Virues-Ortega (2010) concluded: “Results suggest that long-term, comprehensive ABA intervention leads to (positive) medium to large effects in terms of intellectual functioning, language development, and adaptive behavior of individuals with autism” (p. 397).

Systematic Evaluative Reviews:

Systematic approaches for formally evaluating a body of research have been developed to determine if a particular intervention can be characterized as “empirically supported” or “established” based on the number, quality, and outcomes of published treatment studies. These efforts have been undertaken for the purpose of guiding clinical practice, influencing regulations and standards, providing priorities for funding (for both research and treatment), and guiding professional training (see Mesibov & Shea, 2011). For example, the American Psychological Association (Task Force Promoting Dissemination of Psychological Procedures, 1995) described a process to identify “empirically supported treatments.” Those interventions with the highest level of support are characterized as “well-established” (Chambless, et al, 1996).

Evaluations of the most commonly used focused ABA-based interventions (functional communication training and noncontingent reinforcement) indicated that these interventions meet criteria as “well-established” empiricially supported treatments (Carr, Severtson, & Lepper, 2009; Kurtz, Boelter, Jarmolowicz, Chin, & Hagopian, 2011). ABA-based treatments for pica (Hagopian, Rooker, & Rolider, 2011), and for treatment of phobic avoidance (Jennett & Hagopian, 2008) displayed by individuals with intellectual disabilities also have been characterized as “well-established.”

The National Standard Project of the National Autism Center developed a similar model to evaluate interventions for problems associated with autism (2009), which used the term “established” to describe interventions with the highest level of support. Using their evaluative method, the National Autism Center (2009) characterized comprehensive ABA-based interventions as being “established” treatments for autism.

Wong and colleagues (2013), as part of the Autism Evidence-Based Practice Review Group, describe a process for the identification of clinical practices that have sufficient empirical support to be termed “evidence-based.” The group stated in regards to the strength of evidence of ABA “Twenty-seven practices met the criteria for being evidence-based (see table 7, page 20)….evidence-based practices consist of interventions that are fundamental applied behavior analysis techniques (e.g., reinforcement, extinction, prompting), assessment and analytic techniques that are the basis for intervention (e.g., functional behavior assessment, task analysis), and combinations of primarily behavioral practices…”

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301 research topics from Skinner's book verbal behavior

Skinner's (1957) analysis of verbal behavior addresses some of the most important issues in human behavior. However, relatively few of the analyses presented by Skinner in Verbal Behavior have been subjected to an experimental analysis. The current list of topics was assembled in an effort to stimulate empirical research on verbal behavior. The list contains thirty research areas with ten topics suggested for each area. A final topic, education, is presented as a challenge to behavior analysts.

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Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling

Published: 17 September 2024

Cite this article

Wanli Xing 1 ,
Taehyun Kim 1 ,
Wangda Zhu 1 &
Yukyeong Song 1

Although researchers recognize the importance of discussing support for math learning within online learning communities, there is a lack of relevant network classifying methods and analyses at the group level to understand the behavioral differences between groups with varying levels of activity, including their mathematical literacies. In this research, we investigated different groups within a large asynchronous online discussion community for middle school students, focusing on their interaction patterns and the quality of their mathematical engagement. First, we employed an extended Surprise detection algorithm that evaluates interaction quality to classify users into core, periphery, and extra-periphery groups. Following this classification, we performed social network analysis to understand the interaction patterns among these groups. For discourse analysis, we used topic modeling methods to analyze the socio-semantic network structure of the discussions. To assess differences in math literacy and discussion success rates among the groups, we applied the Mann-Whitney U test. Findings indicate that each group is more responsive to its members, with the core group demonstrating a balanced response pattern. X-periphery students primarily engage in casual chats and open queries, indicating a more focused participation aimed at immediate learning needs. Notably, the X-periphery group exhibits the highest math literacy and discussion success rates, suggesting that lower activity levels do not hinder communication efficiency. These findings highlight the importance of considering group dynamics and roles in designing online math learning activities to foster effective communication and support, offering practical insights for sustaining online learning communities through tailored discussion activities.

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The data that support the findings of this study are available from Math Nation, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of Math Nation.

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Xing, W., Li, H., Kim, T. et al. Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13038-7

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