Accordance of the Unified Theory of Reinforcement's Model of Behavior with the Modern Quantitative Law of Effect Open Access

Abstract

Virtual organisms animated by the Unified Theory of Reinforcement's neural network model of behavior responded on random interval schedules in a method that was largely consistent with the quantitative law of effect. The virtual organisms were evolved using an evolutionary algorithm to determine an optimum set of parameters that maximized the number of collected reinforcers while simultaneously minimizing the number of extraneous responses that were emitted. The behavior of the evolved virtual organisms was compared to the quantitative law of effect (Herrnstein, 1961), a modified version of the quantitative law of effect informed by the modern matching law (Soto et al., 2005), and to four comparison functions. The modern quantitative law of effect best described the data with 99.7% of the variance accounted for, but showed non-random standardized residuals. The median exponent was 0.74 for the best fits to the modern quantitative law of effect. The observed k was greater than the possible number of responses that the virtual organisms could emit in a time period, which supports an interpretation of k as simply a parameter rather than as the constant rate of responding (Dallery et al., 2000; McDowell, 2005). Although the virtual organisms exhibited very slight discrepancies from the modern quantitative law of effect, these results expand the number of phenomenon that can be demonstrated by the neural network models to include the quantitative law of effect.

Table of Contents

Table of Contents
I. Introduction...1
II. Experiment I: Confirming the current implementation

a. Method...10

i. Subjects...10
ii. Apparatus and Materials...10
iii. Procedure...11

b. Results...12

III. Experiment II: Behavior on single RI schedules

a. Method...14

i. Subjects...14
ii. Procedure...15
iii. Evolutionary Algorithm...17

1. Fitness...18
2. Parental Selection...19
3. Reproduction...19
4. Mutation...20

b. Results...21

IV. Discussion...24

References...30
Figures...34
Appendix...41
Appendix Figure...43

About this Master's Thesis

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School	Laney Graduate School
Department	Psychology
Subfield / Discipline	Clinical Psychology
Degree	MA
Submission	Master's Thesis
Language	English
Research Field	Psychology, Behavioral
Keyword	Reinforcement Law of Effect Neural Network Behavior
Committee Chair / Thesis Advisor	McDowell, Jack J, Emory University
Committee Members	Lilienfeld, Scott O, Emory University Wolff, Phillip, Emory University

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