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.


36th Annual Convention; San Antonio, TX; 2010

Event Details

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Paper Session #373
Monday, May 31, 2010
10:00 AM–10:50 AM
Lone Star Ballroom Salon E (Grand Hyatt)
Area: EAB
Chair: Gabriel D. Searcy (Western Michigan University)
Matching and dynamical concurrent schedules
Domain: Experimental Analysis
ROBERT W. ALLAN (Lafayette College), William L. Palya (Jacksonville State University)
Abstract: Palya and Allan (2003) demonstrated that pigeons' matching behavior would track a dynamically changing concurrent VI VI schedule over relatively brief (5-minute) components. The present experiments sought to delineate the limits of matching in dynamical schedules by doubling the rate of schedule change and then by reducing the difference between highest and lowest VI schedule values. Even with these changes there continues to be evidence of matching to the dynamically changing concurrent VI schedules suggesting that the matching law offers good predictive function in changing choice conditions.
Optimal Risky Choice: Does Amount of Variability Make a Difference?
Domain: Experimental Analysis
GABRIEL D. SEARCY (Western Michigan University), J. Adam Bennett (Western Michigan University), Maija M. Graudins (Western Michigan University), Cynthia J. Pietras (Western Michigan University)
Abstract: Two experiments investigated risky choice in 15 adult humans across procedural manipulations designed to model energy-budget manipulations. During positive and negative budget conditions, participants were presented with repeated choices between low-variability and high-variability (Experiment 1) or fixed and high-variability compared to low-variability and high-variability (Experiment 2) monetary outcomes. Choice was analyzed in relation to the predictions of static and dynamic optimization modeling. The models provided predictions for which choice was optimal under particular earnings budget conditions (static) or from trial to trial (dynamic). In both experiments, static modeling revealed that choice was generally consistent with the predictions of the energy-budget rule. Also in both experiments, dynamic modeling showed that choice was more consistent with predictions during negative-budget conditions, regardless of the nature of the choice options (i.e., low-variability or fixed). However, in the negative-budget condition of Experiment 2, choice was most consistent with dynamic predictions when choice was between fixed and high-variability options as opposed to low-variability and high-variability. The results from this study present further evidence that the energy-budget rule may have broad applicability and that it can be a useful model for analyzing human decision making.



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