Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.

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49th Annual Convention; Denver, CO; 2023

Program by Invited Tutorials: Saturday, May 27, 2023


 

Invited Tutorial #168
CE Offered: BACB
SQAB Tutorial:Three Laws of Behavior: Allocation, Induction, and Covariance
Saturday, May 27, 2023
11:00 AM–11:50 AM
Convention Center Four Seasons Ballroom 4
Area: SCI; Domain: Theory
BACB CE Offered. CE Instructor: William M. Baum, Ph.D.
Chair: Leonard Green (Washington University in St. Louis)
Presenting Authors: : WILLIAM M. BAUM (University of California, Davis)
Abstract:

Like any science, a science of behavior seeks to measure its phenomena and explain them. Measurement entails ontological commitments, and from an ontological viewpoint, behavior is process, measured by the time it takes up. Since time is limited, activities must compete with one another for time. The Law of Allocation states that the relative time taken up by an activity equals the activity’s relative competitive weight. Explaining behavioral allocation means finding the determiners of competitive weight. The two basic determiners are induction and covariance. The Law of Induction states that behavior depends on environmental events that affect reproductive success—phylogenetically important events (PIEs). PIEs induce both adjunctive behavior and operant behavior: adjunctive behavior because of phylogenetic contingencies; operant behavior because of ontogenetic contingencies. The law of covariance applies to ontogenetic contingencies. A PIE induces an operant activity when the rate of that PIE covaries with the rate of the operant activity. Otherwise neutral events also induce operant activities and adjunctive activities when such events covary with PIEs. Such inducers have commonly been called discriminative stimuli and conditional stimuli. Induction far exceeds reinforcement in explanatory power. The three laws of allocation, induction, and covariance explain most known behavioral phenomena.

Instruction Level: Basic
Target Audience:

Behavior analysts of all stripes

Learning Objectives: At the conclusion of the presentation, participants will be able to: (1) State the Law of Allocation in words; (2) Define a phylogenetically important event (PIE); and (3) Define behavior-PIE (B-PIE) covariance.
 
WILLIAM M. BAUM (University of California, Davis)
Dr. Baum received his BA in psychology from Harvard College in 1961. Originally a biology major, he switched to psychology after taking courses from B. F. Skinner and R. J. Herrnstein in his freshman and sophomore years. He attended Harvard University for graduate study in 1962, where he was supervised by Herrnstein and received his Ph.D. in 1966. He spent the year 1965–66 at Cambridge University, studying ethology at the Sub-Department of Animal Behavior. From 1966 to 1975, he held appointments as post-doctoral fellow, research associate, and assistant professor at Harvard University. He spent two years at the National Institutes of Health Laboratory for Brain, Evolution, and Behavior and then accepted an appointment in psychology at the University of New Hampshire in 1977. He retired from there in 1999. He currently has an appointment as associate researcher at the University of California, Davis and lives in Walnut Creek. His research concerns choice, molar behavior/environment relations, foraging, cultural evolution, and behaviorism. He is the author of two books, Understanding Behaviorism: Behavior, Culture, and Evolution (3rd ed.) and Science and Philosophy of Behavior: Selected Papers. • William M Baum, University of California, Davis, and University of New Hampshire • Three Laws of Behavior: Allocation, Induction, and Covariance
 
 
Invited Tutorial #182
CE Offered: BACB
SQAB Tutorial: A Practical Introduction to Information Theory in Experimental Design and Model Comparison
Saturday, May 27, 2023
3:00 PM–3:50 PM
Convention Center Four Seasons Ballroom 4
Area: SCI; Domain: Theory
BACB CE Offered. CE Instructor: Greg Jensen, Ph.D.
Chair: Ryan D Ward (University of Otago)
Presenting Authors: : GREG JENSEN (Columbia University)
Abstract:

As a branch of applied mathematics, information theory provides a set of tools and axioms for describing the patterning and structure of streams of events. Such streams are an indispensable form of data for learning theory. While measurements of disorder, or “entropy,” are now widely familiar to academics and laypeople, these only represent the tip of information theory’s iceberg. In concert with probability theory, its tools make possible the measure of how surprising an event is (contingent on some probabilistic expectation), how interrelated streams of events are, and how costly it is to translate between different sets of expectations. In this tutorial, I will give an overview of a practical toolkit of information-theoretic measures that can be used to make normative predictions about experimental designs. I will also introduce a strategy for how to measure the degree of correspondence between a model and data in a way that allows entirely different classes of statistical model to be compared with one another. Throughout, my emphasis will be on making these calculations practical for working experimentalists, so that these tools can be put to work in service of advancing learning theory.

Instruction Level: Intermediate
Target Audience:

Graduate students, experimental psychologists, and quantitative behavior analysts

Learning Objectives: At the conclusion of the presentations, participants will be able to: (1) Understand the motivation behind foundational measures of information and entropy, (2) Apply information theory to experimental designs in order to make normative predictions, and (3) Apply information theory to models of learning in order to assess how well they explain available data.
 
GREG JENSEN (Columbia University)
Greg Jensen received a B.A. from Reed college in 2003, where he remained doing post-baccalaureate research on operant variability and matching under concurrent schedules involving three or more simultaneous response alternatives. This work continued during graduate school, eventually resulting in a Ph.D. from Columbia University in 2014. While remaining at Columbia to do post-doctoral work, Dr. Jensen also taught as a lecturer in discipline. He remains affiliated with Columbia as an adjunct associate research scientist at the Zuckerman Institute, while also teaching at Reed College as a visiting assistant professor. Dr. Jensen's current focus is the comparative study of the mechanisms underlying transitive inferences. This work depends in part on the use of Bayesian statistical modeling to estimate latent variables that best describe behavior under various experimental conditions, and partly (in collaboration with system neuroscientists) through analysis of in vivo electrophysiological recordings made during task performance
 
 
Invited Tutorial #219
CE Offered: BACB
SQAB Tutorial: The Organization of Behavior in Bouts of Responses
Saturday, May 27, 2023
4:00 PM–4:50 PM
Convention Center Four Seasons Ballroom 4
Area: SCI; Domain: Basic Research
BACB CE Offered. CE Instructor: Federico Sanabria, Ph.D.
Chair: Suzanne H. Mitchell (Oregon Health & Science University)
Presenting Authors: : FEDERICO SANABRIA (Arizona State University)
Abstract:

Behavior analysts often rely on aggregate measures of behavior to conduct functional analyses and assess the efficacy of their treatments. These aggregate measures, nonetheless, obscure informative features of behavior. A more fine-grained analysis of how motivated behavior unfolds over time, for instance, suggests that, even in a relatively constant environment, motivated behavior is organized in bouts of engagement. Although this is likely in part an artifact of how behavior is measured, it reveals that (a) motivational, cognitive, and sensorimotor processes that underlie observable actions are, in principle, separable, and (b) the most important functional impact of reinforcement is not on conditioned responses or operants, but on behavioral states that give rise to those responses. This tutorial will discuss key insights on behavior that emerge from its temporal organization, the relation of this organization to theories of motivated behavior, and the analytical and procedural approaches to the estimation of organizational parameters.

Instruction Level: Intermediate
Target Audience:

Board certified behavior analysts and graduate students.

Learning Objectives: At the conclusion of the presentation, participants will be able to (1) indicate the importance and implications of the organization of behavior in bouts of engagement, (2) formulate the link between the organization of behavior and other theories of behavior, (3) estimate parameters of the organization of behavioral data
 
FEDERICO SANABRIA (Arizona State University)
Prof. Sanabria obtained his Ph.D. from Stony Brook University under the mentorship of the late Howard Rachlin, and was a postdoctoral researcher in Arizona State University (ASU) under the supervision of Peter Killeen and Janet Neisewander. He is the Director of Diversity, Equity, Inclusion and Belonging at the Department of Psychology at ASU. Prof. Sanabria was Associate Editor of the Journal of the Experimental Analysis of Behavior and of Learning & Behavior. He was the Program Board Coordinator for the Annual Meeting of ABAI and currently serves in its Science Board. Prof. Sanabria also serves as Program Chair and President-Elect of the Society for the Quantitative Analysis of Behavior (SQAB), in the Executive Boards of SEAB and APA Division 25, in the Editorial Board of the Journal of Experimental Psychology: Animal Learning and Cognition; Journal of Neuroscience, Psychology, and Economics; Behavioral Neuroscience; among others. He has published over 70 empirical and theoretical papers and chapters on basic behavioral processes, as well as the entry on Pavlovian and instrumental conditioning for the Oxford Research Encyclopedia of Psychology and a textbook on learning and conditioning. Dr. Sanabria’s research has been funded by NIH and NSF.
 

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