47th Annual Convention; Online; 2021
All times listed are Eastern time (GMT-4 at the time of the convention in May).
Event Details
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| The Differential Outcome Effect: Exploring Predictive Models and its Relevance to Basic Behavioral Phenomena |
| Saturday, May 29, 2021 |
| 5:00 PM–5:25 PM |
| Online |
| Area: PCH |
| Instruction Level: Intermediate |
| Chair: Russell Silguero (PENDING) |
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| The Differential Outcome Effect: Exploring Predictive Models and its Relevance to Basic Behavioral Phenomena |
| Domain: Theory |
| RUSSELL SILGUERO (University of North Texas), Manish Vaidya (University of North Texas) |
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| Abstract: When two or more occasions for responding are associated with differential outcomes, the development of discriminated operants is faster relative to nondifferential outcomes. This phenomenon, first reported by Trapold (1970), is called the differential outcomes effect (DOE). This presentation will suggest that the DOE has an important bearing on many fundamental concepts in behavior analysis, such as respondent conditioning, discrimination learning, differential reinforcement, and choice. We will explore both Bayesian and more traditional approaches to modeling the relatively simple conditions under which the DOE is observed with nominal variables (e.g., Response 1 vs. Response 2; Outcome 1 vs. Outcome 2). Next, we will discuss how these models relate to current theoretical accounts of the DOE such as outcome expectancy and equivalence classes. Finally, we will consider how a generalization of this approach to modeling the DOE may be a useful framework for understanding both simpler and more complex learning phenomena. |
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