Evolving process-based models from psychological data using genetic programming


The development of computational models to provide explanations of psychological data can be achieved using semi-automated search techniques, such as genetic programming. One challenge with these techniques is to control the type of model that is evolved to be cognitively plausible – a typical problem is that of “bloating”, where continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and algorithms for evolving models. We apply this system to the delayed match-to-sample task. We show how the challenge of bloating may be addressed by extending the fitness function to include measures of cognitive performance



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