What We Can (And Can't) Learn About Behavior from Artificial Organisms becomes clinically important the moment a team has to turn good intentions into reliable action inside documentation workflows, supervision meetings, treatment planning, and quality review. In What We Can (And Can't) Learn About Behavior from Artificial Organisms, for this course, the practical stakes show up in faster workflow without clinical drift, privacy loss, or weak oversight, not in abstract discussion alone.
Provider: BehaviorLive — via Colorado Association for Behavior Analysis
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Join Free →Artificial organisms (AOs) are increasingly used by behavior scientists to describe, predict, and suggest pathways to controlling the behavior of biological organisms. At their core, the most successful AOs are animated using reinforcement learning (RL) approaches from the field of artificial intelligence (AI). However, not all RL approaches possess the same utility for behavior scientists interested primarily in the behavior of biological organisms. This presentation reviews recent empirical work in behavior analysis that aims to animate AOs using various RL algorithms, explaining how they work and their current capabilities. In particular, we discuss the role AOs might play in helping clarify conceptual questions, understand basic behavioral processes, and inform clinical or educational service delivery. Attendees should leave this presentation with a better understanding of what we can, and cannot, learn about the behavior of biological organisms by way of conducting research with AOs.
| Certification Body | Credits | Type |
|---|---|---|
| BACB® | 1 | General |
| COA | 1 | — |
Dr. David Cox can formally lay claim to being a bioethicist (master's degree from Union Graduate College), a board-certified behavior analyst at the doctoral level (PhD in behavior analysis from the University of Florida), a behavioral economist (post-doc training at the Behavioral Pharmacology Research Unit at Johns Hopkins University School of Medicine), and a data scientist (post-doc training through an Insight! Data Science Fellowship). He has worked in behavior analysis for 20 years as a clinician, academic researcher, scholar, technologist, and all-around behavior science junky. From his work and collaborations, David has published over 70 peer-reviewed articles, book chapters, and books. And, has had the fortune to serve as Editor in Chief for The Experimental Analysis of Human Behavior Bulletin and Associate or Guest Editor for Perspectives on Behavior Science, Behavior Analysis in Practice, Journal of Applied Behavior Analysis, Psychological Record, Education and Treatment of Children, Toward Data Science, and Behavior and Social Issues. When he's not doing research or building quantitative models of behavior-environment relations, he enjoys spending time with his wife, two beagles, and two kittens around St. John's, FL.
Dig into the research behind this topic — plain-English summaries written for BCBAs.
279 research articles with practitioner takeaways
258 research articles with practitioner takeaways
231 research articles with practitioner takeaways
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All behavior-analytic intervention is individualized. The information on this page is for educational purposes and does not constitute clinical advice. Treatment decisions should be informed by the best available published research, individualized assessment, and obtained with the informed consent of the client or their legal guardian. Behavior analysts are responsible for practicing within the boundaries of their competence and adhering to the BACB Ethics Code for Behavior Analysts.