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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
Published on: July 22, 2025
A computational theory of selection by consequences applied to concurrent schedules
J J McDowell1, Marcia L Caron, Saule Kulubekova
1Department of Psychology, Emory University, Atlanta, GA 30322, USA. jack.mcdowell@emory.edu
Journal of the Experimental Analysis of Behavior
|December 17, 2008
Summary
Virtual organisms evolved using a computational theory of selection by consequences demonstrated behaviors consistent with matching theory. This suggests matching theory equations emerge from evolutionary selection dynamics.
Area of Science:
- Computational Neuroscience
- Behavioral Economics
- Evolutionary Psychology
Background:
- Behavioral research often uses reinforcement schedules to study decision-making.
- Matching theory predicts how organisms allocate responses between concurrent schedules.
- Computational models offer a novel approach to understanding behavioral evolution.
Purpose of the Study:
- To test a computational theory of selection by consequences (CSC) using virtual organisms.
- To investigate if Darwinian principles can explain behavior on concurrent reinforcement schedules.
- To determine if matching theory equations emerge from evolutionary dynamics.
Main Methods:
- Developed virtual organisms animated by a CSC computational theory.
- Exposed virtual organisms to symmetrical and asymmetrical concurrent schedules of reinforcement.
- Analyzed the steady-state behavior and parameters of the virtual organisms' responses.
Main Results:
- Virtual organisms' behavior on concurrent schedules was accurately described by the power function matching equation.
- The parameters of the matching equation in virtual organisms mirrored findings from live organisms.
- Behavioral evolution under CSC showed consistency with established principles.
Conclusions:
- The study supports the CSC theory as a mechanism for behavior evolution.
- Matching theory equations appear to be emergent properties of evolutionary selection by consequences.
- Computational models can effectively simulate and explain complex behavioral phenomena.
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