Association for Behavior Analysis International

Behavior analysis is on the cusp of a major change in the way we think about our most fundamental process: Reinforcement. Whereas the law of effect stipulates that reinforcers control behavior because of their special function in increasing a behavior’s strength, an alternative approach casts reinforcers as stimuli with current value to the organism, but no unique function in changing behavior. Under this approach, behavior is controlled by relations between stimuli, depending on the affordances and dispositions of the organism. This tutorial explores some of the data that has led us to change the way we understand control by current environmental conditions. First, the tutorial examines some of the evidence for prospective control, when reinforcers are absent, or temporally distant, or when reinforcer effects are inconsistent with strengthening. Next, I explore how quantitative models can provide a testable explanation of control by the likely future, as extrapolated from the past. Finally, the tutorial considers the implications of a shift from understanding control in terms of retrospective response-reinforcer pairings to prospection on the basis of the perceived structure of the environment, and argues that in conjunction with quantitative models, prospective control need not invoke an inner organism.

This talk is aimed at behavior analysts interested in new ways to measure and describe apparently changes in behavior, and/or in new approaches to understanding how reinforcers affect behavior.

Sarah Cowie obtained her Ph.D. in 2014 at the University of Auckland, under the supervision of Professor Michael Davison and Dr. Douglas Elliffe. Since graduating with her Ph.D., Sarah’s research has explored how past experience translates into control by the present and the likely future.

Application of basic statistical measures (e.g., t-tests, ANOVA) to single-subject designs have been a source of conflict in Behavior Analysis because, in part, these tests aggregate behavioral variability across subjects and time, eliminating much of the data that behavior analysts find important. Multilevel modeling (MLM) is a statistical technique that addresses these concerns and is commonly used when data are naturally clustered (e.g., student clusters in classrooms, which are also clustered in various schools across a district). With MLM, the value of a statistical parameter for a specific case depends on the levels of the each cluster for that case. A single subject can serve as a cluster of data and, therefore, MLM can provide subject-by-subject predictions. In a single-subject or small-n design, statistical comparisons based on the IVs of interest are enhanced when the models have already accounted for intrasubject variability. In theoretical modeling of behavior, subject-by-subject model parameters can be obtained while simultaneously accounting for group-level patterns in the data. This tutorial will demonstrate using MLM to analyze experimental data from a single subject design and also to conduct subject level model fitting. The analyses will be conducted in R, a popular, free software package for statistical analyses.

Researchers, research-practitioners, students

Dr. DeHart received his B.A. and Ph.D. from Utah State University under the mentorship of Dr. Amy Odum. In July of 2017, he began his current position as a post-doctoral fellow with Dr. Warren Bickel at the Fralin Biomedical Research Institute at VTC. Dr. DeHart’s primary research interests include the behavioral economics of addiction and other health behaviors including cigarette smoking and obesity as well as the application of advanced statistical methods to behavioral data. His early research investigated novel methods of reducing impulsive choice using framing and financial education and his dissertation applied structural equation modeling to better understand the effects of delay length and outcome magnitude on delay discounting. His current research interests are twofold. First, he is interested in measuring the abuse liability of different risky products including tobacco cigarettes and e-cigarettes and how demand for those products can be changed using public-health narratives. Second, he is interested in understanding the relationship of delay discounting to various health behaviors. In this line, he has applied advanced statistical methods including structural equation modeling, machine learning algorithms, and mixed-effects modeling. Dr. DeHart’s work has been recognized by various popular media outlets including the Wall Street Journal and he currently serves on the editorial board for the Journal of the Experimental Analysis of Behavior.

Jonathan E. Friedel is a research psychologist in the Bioanalytics Branch at the National Institute for Occupational Safety and Health. As part of the Organizational and Behavioral Research Team, he works on several grant funded projects focused on worker safety in laboratory workers, distracted driving, and data analytics for organizations using behavior based safety. He is currently the primary investigator for a grant funded project designed to use behavioral economics to quantify the factors that affect safety-related decision making in small businesses. He obtained his PhD in experimental psychology from Utah State University where he focused on delay discounting and behavioral economics. He obtained a MS in Behavior Analysis from University of North Texas.  

This tutorial will provide a general introduction to some technologies available for manipulating gene expression in mice. Technologies for manipulating gene expression can be used to investigate the neurobiological contributors to behavior. Results obtained from studies in dopamine receptor knockout mice on the role of dopamine receptors in food’s reinforcing efficacy will be used as an example of use of a global knockout approach. Results obtained from studies in Alzheimer’s transgenic APPswe/PS1dE9 mice on the role of beta amyloid in cognitive decline will be used as an example of a transgene approach. Additionally, the tutorial will discuss emerging technologies that allow precise control over the timing and location of modification of genetic expression. These emerging technologies allow behavioral researchers to investigate the role of neurobiological variables on behavior from a developmental perspective and to address questions regarding the role of particular brain regions in behavior. Genetically modified organisms provide a promising avenue for fruitful collaborations between behavior analysts and geneticists, neuroscientists, and scientists in other complementary areas.

Board certified behavior analysts; licensed psychologists; graduate students.

Researchers and clinicians rely heavily on the matching-to-sample procedure (MTS) to establish conditional discriminations. In an MTS trial, a visual or auditory sample is presented, followed by several comparisons (pictures or objects). The selection of the correct comparison leads to reinforcement while selection of the incorrect one leads to some form of correction. Clinically, MTS is used for teaching a variety of skills, including listener behavior, categorization, math, and reading. An important characteristic of MTS is that samples and comparisons become substitutable for each other (i.e., equivalent). Understanding the variables responsible for the development of equivalence classes has been the topic of investigation in the field of behavior analysis for almost 50 years, generating an enormous (and complicated) body of research. This research has led to the development of at least three theoretical accounts to explain meaning and symbolic behavior, as well as has informed clinicians on how to take advantage of the MTS procedure to produce a multitude of generative/novel performances. This talk will serve as a first introduction to the concept of stimulus equivalence and its ramifications for both research and practice.

Basic researchers, students, board certified behavior analysts, and licensed psychologists.