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| The Evolutionary Theory of Behavior Dynamics |
| Monday, September 30, 2019 |
| 11:30 AM–12:20 PM |
| Stockholm Waterfront Congress Centre, Level 2, C4 |
| Area: EAB |
| Instruction Level: Intermediate |
| Chair: Jack J. McDowell (Emory University) |
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| Current Status of the Evolutionary Theory of Behavior Dynamics:
Empirical Support and Untested Predictions |
| Domain: Theory |
| JACK J. MCDOWELL (Emory University) |
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| Abstract: A comprehensive theory of adaptive behavior is a desirable goal for a science of behavior. The evolutionary theory of behavior dynamics is one candidate for such a theory. It is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. This presentation is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory’s untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Future directions will be discussed briefly, including clinical translational research, and research on the animation of mechanical agents. Based on the empirical evidence reviewed in this presentation, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior. |
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| Using an Evolutionary Theory of Behavior Dynamics to Predict the Superior Quantitative Model of Punishment |
| Domain: Theory |
| BRYAN KLAPES (Emory University), Jack J. McDowell (Emory University) |
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| Abstract: Using an information theoretic model comparison technique, Klapes, Riley, & McDowell (2018) showed that no published matching-law-based model of punishment quantitatively outperformed the generalized matching law (Baum, 1974) when fitted to data from a set of concurrent schedules of VI reinforcement with superimposed VI punishment schedules. Given this result, they called for new models to be developed and tested. However, the small number of data points per individual in currently available datasets would result in the model comparison technique unfairly discounting more complex models (i.e., models with more free parameters). An Evolutionary Theory of Behavior Dynamics (ETBD; McDowell, Caron, Kulubekova, & Berg, 2008) is a selectionist theory of dynamic operant behavior that can simulate the behavior of live organisms with great accuracy (McDowell, 2013). As a computational model, the ETBD can generate datasets with as many data points as desired. McDowell & Klapes (in preparation) have developed a method to incorporate punishment into the ETBD. Here we present a selection of new and more complex matching-law-based models of punishment, and propose a study using the ETBD to predict which of these models is likely to be superior when applied to data from experiments with live organisms. |
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