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

Event Details


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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
 

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