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Automatic Generation of Cognitive Theories using Genetic Programming

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Abstract

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories.

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Notes

  1. In this paper we use ‘cognitive neuroscience’ as a generic term covering neuroscience, cognitive science, and cognitive psychology.

  2. The same is true for experiments done with animals, for which the concepts presented in this paper also apply.

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Acknowledgment

We thank Guillermo Campitelli for providing advice on the delayed-match-to-sample task, as well as Veronica Dark and anonymous referees for useful comments.

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Correspondence to Enrique Frias-Martinez.

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Frias-Martinez, E., Gobet, F. Automatic Generation of Cognitive Theories using Genetic Programming. Minds & Machines 17, 287–309 (2007). https://doi.org/10.1007/s11023-007-9070-6

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  • DOI: https://doi.org/10.1007/s11023-007-9070-6

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