Evolving process-based models from psychological datausing genetic programming

Lane, Peter C. R., Sozou, Peter D., Addis, Mark and Gobet, Fernand (2014) Evolving process-based models from psychological datausing genetic programming. In: Proceedings of the AISB-50 Conference, 2014-04-01, London, United Kingdom, GBR.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Official URL:	http://gold.ac.uk/
Additional Information:	© 2014 The Authors
Divisions:	Philosophy, Logic and Scientific Method
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Date Deposited:	18 Apr 2016 15:20
Last Modified:	12 Dec 2024 04:55
Projects:	ES/L003090/1
Funders:	Economic and Social Research Council
URI:	http://eprints.lse.ac.uk/id/eprint/66170

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