Translating Behavior Analysis: a Spectrum Rather than a Road Map

Tactics of just, equitable, diverse, and inclusive scientific research

The principles of social justice, equity, diversity, inclusion (JEDI) have received increasing attention in behavior analysis circles, but the conversation has largely centered on implications for applied behavior analysis practice and research. It may be less clear to researchers who conduct basic and translational research how JEDI principles can inform and inspire their work. This article synthesizes publications from behavior analysis and other scientific fields about tactics of JEDI-informed research. We organized this scholarship across five stages of research from developing the research question to sharing findings and curated sources for an audience of behavioral science researchers. We discuss reflexive practice, representation, belongingness, participatory research, quantitative critical theory, and open science, among other topics. Some researchers may have already adopted some of the practices outlined, some may begin new practices, and some may choose to conduct experimental analyses of JEDI problems. Our hope is that those actions will be reinforced by the behavior analysis scientific community. We conclude by encouraging the leadership of this journal to continue to work toward the structural changes necessary to make the experimental analysis of behavior just, equitable, diverse, and inclusive.

Advancing and Integrating the Cusp Concept to Understand Behavioral Repertoire Dynamics

The behavioral repertoire grows and develops through a lifetime in a manner intricately dependent on bidirectional connections between its current form and the shaping environment. Behavior analysis has discovered many of the key relationships that occur between repertoire elements that govern this constant metamorphosis, including the behavioral cusp: an event that triggers contact with new behavioral contingencies. The current literature already suggests possible integration of the behavioral cusp and related concepts into a wider understanding of behavioural development and cumulative learning. Here we share an attempted step in that progression: an approach to an in-depth characterization of the features and connections underlying cusp variety. We sketch this approach on the basis of differential involvement of contingency terms; the relevance to the cusp of environmental context, accompanying repertoire, or response properties; the connections of particular cusps to other behavioral principles, processes, or concepts; the involvement of co-evolving social repertoires undergoing mutual influence; and the ability of cusps to direct the repertoire either toward desired contingencies or away from a growth-stifling repertoire. We discuss the implications of the schema for expanded applied considerations, the programming of unique cusps, and the need for incorporating cultural context into the cusp. We hope that this schema could be a starting point, subject to empirical refinement, leading to an expanded understanding of repertoire interconnectivity and ontogenetic evolution.

Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations

A core feature of behavior analysis is the single-subject design, in which each subject serves as its own control. This approach is powerful for identifying manipulations that are causal to behavioral changes but often fails to account for individual differences, particularly when coupled with a small sample size. It is more common for other subfields of psychology to use larger-N approaches; however, these designs also often fail to account for the individual by focusing on aggregate-level data only. Moving forward, it is important to study individual differences to identify subgroups of the population that may respond differently to interventions and to improve the generalizability and reproducibility of behavioral science. We propose that large-N datasets should be used in behavior analysis to better understand individual subject variability. First, we describe how individual differences have been historically treated and then outline practical reasons to study individual subject variability. Then, we describe various methods for analyzing large-N datasets while accounting for the individual, including correlational analyses, machine learning, mixed-effects models, clustering, and simulation. We provide relevant examples of these techniques from published behavioral literature and from a publicly available dataset compiled from five different rat experiments, which illustrates both group-level effects and heterogeneity across individual subjects. We encourage other behavior analysts to make use of the substantial advancements in online data sharing to compile large-N datasets and use statistical approaches to explore individual differences.

Promoting Reciprocal Relations across Subfields of Behavior Analysis via Collaborations

Several barriers may inhibit the growth of behavior analysis as a more integrated and collaborative field. Two such barriers are siloed environments that reinforce a basic-applied distinction, and a lack of translational research pathways. We describe the perils of silos, and elaborate on potential solutions to increase reciprocal relations among subfields in behavior analysis. We promote a five-tiered system to classify research in behavior analysis, and discuss literature on cultivating effective intra and cross-disciplinary collaborations, including using the framework of metacontingencies to understand collaborations. We also propose quantitative and qualitative measures to examine whether the potential solutions increase intra and interdisciplinary interactions. These measures include bibliometric (e.g., citations across fields), sociometric (e.g., social network analysis), and narrative analysis. We apply some of these measures to publications from 2011-2022 from the Journal of the Experimental Analysis of Behavior and Journal of Applied Behavior Analysis, and argue that behavior analysis overall may benefit from a more collaborative approach.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40614-023-00386-x.

Strengthening the research-practice loop in applied animal behavior: Introduction to the special issue

Though operant learning has been applied to socially significant animal behavior for many years, connections between these practical applications and the basic science that supports them have weakened over time. There is a need for replications and extensions of technologies derived from basic research to applied animal settings, and for practical questions to be taken back to the lab where they can be modeled and studied under controlled conditions before incorporating the results in applied behavior-change research and practice. This special issue highlights ways that behavior analysis can contribute to and support the development of evidence-based applications with animals. Articles in this issue provide context for the relationship between basic research and practice in animal behavior, apply basic principles to animal behavior practice, and investigate practical problems using basic research techniques. Each of these is important for a robust interchange between basic science and practice. Here we comment on the contributions of each article to the literature and identify directions for future research.

Balancing the scale

Traditional discussions involving 'basic' and 'applied' behavioral research often focus on the differences, or gaps, between these areas. They take place in different environments, use different methods, ask different questions, and have different objectives. Applied animal behavior is no exception. Focusing on the differences in these areas is to the detriment of a cohesive and complete understanding of animal behavior. This paper instead focuses on the similarities between these two sides, and presents them as a matter of scale. A series of real-life examples experienced by the authors is used to highlight how the skills and knowledge of both the applied and the basic sides are valuable and necessary to not only further both fields independently, but to develop a comprehensive understanding of animal behavior.

Nathan H. Azrin: A Case Study in Research Translation in Behavior Science

Nathan H. Azrin (1930-2013) contributed extensively to the fields of experimental and applied behavior analysis. His creative and prolific research programs covered a wide range of experimental and applied areas that resulted in 160 articles and several books published over a period of almost 6 decades. As a result, his career illustrates an unparalleled example of translational work in behavior analysis, which has had a major impact not only within our field, but across disciplines and outside academia. In the current article we present a summary of Azrin's wide ranging contributions in the areas of punishment, behavioral engineering, conditioned reinforcement and token economies, feeding disorders, toilet training, overcorrection, habit disorders, in-class behavior, job finding, marital therapy, and substance abuse. In addition, we use scientometric evidence to gain an insight on Azrin's general approach to treatment evaluation and programmatic research. The analysis of Azrin's approach to research, we believe, holds important lessons to behavior analysts today with an interest in the applied and translational sectors of our science.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40614-020-00278-4.