|
| Cluster 4: Computational Modeling |
| Tuesday, October 29, 2024 |
| 1:50 PM–4:50 PM |
| The Drake Hotel; Lobby Level; Grand Ballroom |
| Area: PCH; Domain: Theory |
| Chair: Timothy D. Hackenberg (Reed College) |
| CE Instructor: Timothy D. Hackenberg, Ph.D. |
| Abstract: Computational Modeling |
| Instruction Level: Intermediate |
| Target Audience: Behavior Analysts |
| Learning Objectives: At the conclusion of the presentation, participants will be able to: (1) Describe some technological advances in data collection and computational modeling that allow researchers to directly observe and measure verbal communities in real-time as they evolve; (2) Explain how new approaches to investigating the verbal community can move our understanding of its impact from the theoretical to the empirical; (3) Describe the nature of a complex systems theory; (4) Explain how the Evolutionary Theory of Behavior Dynamics [ETBD] illustrates the advantages of a complex systems approach. |
| |
| Verbal Frontiers: Combining Words in the Wild, Computational Modeling, and Behavior Analysis to Explore Verbal Communities |
| DAVID J. COX (RethinkFirst; Endicott College) |
| Abstract: The verbal community plays a critical role in the analysis of verbal behavior because it selects conventional verbal forms; shapes listeners' behavior that mediates the consequences of speakers' behavior; and specifies the conditions under which specific verbal forms will contact consequences. However, despite the importance of the verbal community, past authors typically describe “the verbal community” theoretically instead of observing, measuring, or describing verbal communities directly. In this presentation, we review recent technological advances in data collection and computational modeling that allow researchers to directly observe and measure verbal communities in real-time as they evolve. Further, because data is often collected at the individual level, researchers can directly observe, measure, and model the influence of a verbal community on individual speaker and listener behavior. We show how this can be done through two examples where two distinct verbal communities were directly observed, measured, described, and modeled. In doing so, previously theoretical questions about what constitutes a verbal community and how it influences speaker and listener behavior can be answered with data. Researchers able to sort out compatible and incompatible assumptions from this methodological integration might be poised to ask and answer questions novel to the analysis of verbal behavior. |
 Dr. David J. Cox, Ph.D., M.S.B., BCBA-D has been a behavior science junky since 2004. Scratching that itch led to a PhD in Behavior Analysis from the University of Florida and Post-Doctoral Training in Behavioral Pharmacology and Behavioral Economics from Johns Hopkins University School of Medicine. David gets into random things and so has also picked up a M.S. in Bioethics from Union Graduate College and Post-Doctoral Training in Data Science through the Insight! Data Science Fellows program. David's interest in computational modeling originated after watching The Matrix as a kid, however, it took a more serious, academic turn after seeing Ex Machina in 2014 and realizing the conceptual similarities between artificial intelligence and behavior analysis. Since then, his research and applied work has focused on leveraging technology, quantitative modeling, and artificial intelligence to understand the behavioral processes of decision-making so as to ethically optimize behavioral health outcomes and clinical decision-making. Based on individual and collaborative work, Dr. Cox has published 50+ peer-reviewed articles, four books, and 165+ presentations at scientific conferences. |
| |
| Complex Systems Theory in Behavior Analysis |
| JACK J MCDOWELL (Emory University) |
| Abstract: Traditional scientific theories typically abstract simplified variables from phenomena and enter them into mathematical expressions to test empirically. In behavior analysis, for example, behavioral and environmental phenomena are often reduced to simple rates of target responding and reinforcement, which are then entered into mathematical expressions such as the matching law. A modern version of theory development instead treats observable phenomena as the result of the operation of a complex system. The operation of the system is stated in the form of low level rules, which constitute the theory. A system that follows the rules produces higher level emergent outcomes that can be compared to data. One advantage of complex systems theory over traditional theory in science is that it naturally produces a wider range of phenomena, both steady-state and dynamic, that can be compared with experimental findings. An example of a complex systems theory in behavior analysis is the evolutionary theory of behavior dynamics (ETBD), which is stated in the form of low-level Darwinian rules that can be used to animate artificial organisms (AOs). The behavior of the AOs is a form of artificial intelligence that can be studied empirically and compared to the behavior of live organisms. The ETBD has been shown to accurately describe the behavior of live organisms, both qualitatively and quantitatively, in a wide variety of environments. The theory has also been successfully applied to the study and treatment of clinically significant behavior problems. |
 J. J McDowell received an A. B. from Yale University in 1972 and a Ph.D. from the State University of New York at Stony Brook in 1979. After completing his clinical internship, he joined the faculty of Emory University, where he is currently Emeritus Professor of Psychology. Dr. McDowell is also a licensed clinical psychologist, and maintains a private practice of behavior therapy in Atlanta. Dr. McDowell's research has focused on the quantitative analysis of behavior. He has conducted tests of matching theory in experiments with humans, rats, and pigeons, has made formal mathematical contributions to the matching theory literature, and has proposed a computational theory of behavior dynamics. He has also written on the relevance of mathematical and computational accounts of behavior for the treatment of clinical problems. Dr. McDowell's current research is focused on his computational theory of selection by consequences, including studies of behavior generated by the theory's genetic algorithm, and possible implementations of the theory in neural circuitry. His work, including collaborations with students and former students, has been funded by NIMH, NSF, and NIDA. |
| |
Discussant: Perspectives on Behavioral Complexity From Computational Modeling |
| PETER R. KILLEEN (Arizona State University) |
| Abstract: Dr. Killeen’s presentation will offer: 1) analysis of each contribution to this cluster; 2) consideration of the commonalities and differences represented; and 3) discussion of the implications of these for furthering understanding of behavioral complexity via computation modeling. |
 Peter received his doctorate in 1969 under the perplexed gazes of Howie Rachlin, Dick Herrnstein, and Fred Skinner. His first and last known position was at Arizona State University (witnessing the fall of the Department-Previously-Known-As Fort Skinner in the Desert). He has studied choice behavior, schedule-induced responses like polydipsia (which he devoutly practices), reinforcement schedules, interval timing, and delay discounting. His reinforcers include the Poetry in Science Award; the APA Div. 25 Med Outstanding Researcher Award; Banco de Santander Research Prize; the Hilgard Award for the Best Theoretical Paper on Hypnosis (!); the F. J. McGuigan Lecture on Understanding the Human Mind (!!); Presidents of the Society of Experimental Psychologists, the Society for the Quantitative Analysis of Behavior, and the 3rd International Seminar on Behavior (SINCA). A year at the Institute for Advanced Studies in Oslo birthed a paper that received The Faculty of 1000’s “Must Read” for its behavioral energetics theory of ADHD. His statistic prep was an Emerging Research Front Feature on Thomson Reuters Sciencewatch, before it was quashed by jealous others. (All of these reinforcers were seriously delayed from the behavior that instigated them, fwiw.) He has written oodles of screeds on choice and on timing; his first, now receiving paltry social security, showed that pigeons were indifferent between free food and schedules where they had to work for food (now disproved by many wayside signs indicating work as a preference, I am compelled to add); his latest were deep dives into the perception of sequential stimuli in the context of timing, an omnium gatherum on reinforcement schedule models, and an article on discounting which the editor blinked and let fly with a title that included “Portfolio of Desires”; [what are in yours, if I may rudely inquire?]. His portfolio includes the well-being of friends, family, and our field; and that of the many others now in dire straits. It includes the hope for your joy in research and the helping of others. And also suds with good music at a local speakeasy |
|
| |