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| Maximization Theory Redux: An Economic Account of Instrumental Reinforcement |
| Saturday, May 24, 2025 |
| 4:00 PM–4:50 PM |
| Marriott Marquis, M2 Level, Marquis Salon 1-5 |
| Area: SCI/EAB; Domain: Basic Research |
| Chair: Jonathan W. Pinkston (University of Kansas) |
| CE Instructor: Jonathan W. Pinkston, Ph.D. |
| Presenting Author: FEDERICO SANABRIA (Arizona State University) |
| Abstract: The Modular Maximization Theory (MMT) is introduced as a comprehensive framework for understanding instrumental behavior. Like earlier maximization theories, MMT posits that behavior is distributed across alternatives to maximize utility over time. This concept is structured through five foundational postulates that define alternatives (e.g., leisure and work) and choice rules as budget constraints and utility functions. A key innovation of MMT is its incorporation of reinforcer utilization—encompassing both consummatory and post-consummatory activities—into the budget-constraint function. A model of ratio-schedule performance is developed under the assumption that utilization is proportional to demand, with utility represented as an additive power function of reinforcer magnitude. This model effectively explains how reinforcer magnitude, response effort, non-contingent reinforcement, and income influence demand curves, behavior-output functions, dose-response relationships, and progressive-ratio breakpoints, while accounting for rate-dependent effects. It also offers novel insights into choice behavior, including concurrent-schedule performance, income dependency, and delay discounting, as well as post-reinforcement pauses and run rates. Variations in budget-constraint and utility functions are proposed as alternative models. Potential theoretical advancements and applications are explored. |
| Instruction Level: Intermediate |
| Target Audience: Masters and doctoral students acquainted with fundamental concepts in behavior analysis |
Learning Objectives: 1. List the premises of Modular Maximization Theory
2. Identify the key contributions of MMT to reinforcement theory
3. Explain how MMT may contribute to solving concrete problems in behavior analysis |
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| FEDERICO SANABRIA (Arizona State University) |
 Dr. Sanabria serves as Professor and Area Head (Behavioral Neuroscience and Comparative Psychology) at the Department of Psychology at Arizona State University (ASU). He previously served as its Director for Diversity, Equity, Inclusion, and Belonging. Dr. Sanabria obtained his Ph.D. from Stony Brook University under the mentorship of the late Howard Rachlin, and was a postdoctoral researcher in ASU under the supervision of Peter Killeen and Janet Neisewander. He currently serves as President of the Society for the Quantitative Analysis of Behavior (SQAB), serving previously in the Executive Boards of the Society for the Experimental Analysis of Behavior (SEAB) and of Division 25 (Behavior Analysis) of the American Psychological Association (APA). Dr. Sanabria serves in multiple Editorial Boards in the field, including the Journal of Experimental Psychology: Animal Learning and Cognition; Journal of Neuroscience, Psychology, and Economics; Behavioral Neuroscience; among others. He was Associate Editor of the Journal of the Experimental Analysis of Behavior and of Learning & Behavior. Dr. Sanabria’s research focuses on fundamental and highly conserved cognitive and behavioral processes governing animal learning and motivation, their involvement in various psychopathologies, and their representation in computational models. His work is reflected in over 70 empirical and theoretical papers and chapters on basic behavioral processes. Dr. Sanabria’s research has been funded by NIH and NSF. |
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