Predicting and Testing a Contemporary Quantitative Model of Punishment Open Access
Klapes, Bryan (Summer 2020)
Published
Abstract
In this dissertation, five novel quantitative models of punishment based on the generalized matching law (GML; Baum, 1974) were developed. The descriptive accuracies of these models were tested against one another and against the GML in three experiments using information criteria. Experiment 1 entailed a reanalysis of previously collected live organism data. None of the punishment models were supported over the GML. It was hypothesized that this result was likely due to the small number of data points used to fit each model. Thus, two additional experiments were performed using datasets with many more data points per fit. Experiment 2 utilized a well-regarded computational theory of operant behavior known as the Evolutionary Theory of Behavior Dynamics (ETBD; McDowell, 2004). Experiment 3 was a replication of Experiment 2 using human participants who worked on a recently developed rapid-acquisition procedure called a Procedure for Rapidly Establishing Steady-State Behavior (PRESS-B; Klapes et al. 2020). These experiments initially resulted in divergent conclusions: the ETBD predicted that the GML was the superior model, while data generated by PRESS-B showed that a punishment model based on the concatenated GML (cGML; Davison & McCarthy, 1988) was superior to the GML and the other punishment models. Experiment 2 was found to have relatively weak punishing contingencies, however, which was the likely source of the discrepant conclusions. Thus, the cGML-based punishment model is presumed to be the best contemporary quantitative model of punishment.
Table of Contents
1. Introduction
1.1. The Matching Law.……………………………………………………………………...1
1.2. Matching Law-Based Punishment Models………………………………………...5
2. Developing Contemporary Matching-Law-Based Punishment Models
2.1. Model Constraints……………………………………………………………………...20
2.2. Building the Candidate Models.……………………………………………….…….21
2.3. A Theoretical Comparison of the New Models…………………………….……..27
3. Experiment 1: Fitting New Models to Available Data
3.1. Methods……………………………………………………………………………..…...29
3.2. Results………………………………………………………………………………...….30
3.3. Discussion…………………………………………………………………………….....31
4. Experiment 2: An ETBD Prediction of Model Superiority
4.1. Methods……………………………………………………………………………….....38
4.2. Results………………………………………………………………………………...….39
4.3. Discussion…………………………………………………………………………….....40
5. Experiment 3: A Test of the ETBD Prediction Using Human Participants
5.1. Methods……………………………………………………………………………….....45
5.2. Results…………………………………………………………………………………....47
5.3. Discussion…………………………………………………………………………..…...48
6. General Discussion
6.1. Failure to Replicate the ETBD Qualitative Prediction with PRESS-B….…...50
6.2. Future Directions……………………………………………………………………….52
6.3. Conclusion……………………………………………………………………………....55
7. References…………………………………………………………………………….......56
8. Tables and Figures
Table 1……………………………………………………………………………………......66
Table 2……………………………………………………………………………….…..…...67
Table 3…………………………………………………………………………………...…...68
Table 4……………………………………………………………………………….…..…...69
Table 5……………………………………………………………………………….…..…...70
Table 6……………………………………………………………………………….…….....71
Figure 1…………………………………………………………………………...……..……72
Figure 2………...………………………………………………………………………..……73
Figure 3…………………….………………………………………………………………....74
Figure 4………………………….………………………………………………………..…..75
9. Appendix A…………………………………………………………………..…….…..…76
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Primary PDF
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Predicting and Testing a Contemporary Quantitative Model of Punishment () | 2020-07-09 14:50:00 -0400 |
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Supplemental Files
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Figure 2 (Example game-play of PRESS-B) | 2020-07-09 14:50:05 -0400 |
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