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Title: Automatic Generation of Cognitive Theories using Genetic Programming
Authors: Frias-Martinez, E;Gobet, F
subject: Cognitive Neuroscience;Computational Neuroscience;Theory generation;Theory building;Genetic Programming;evolutionary computation;Delayed-Match-To-Sample;scientific discovery;search;fitness function;tree representation;modularity;hierarchy;bloating;cognitive architecture;memory
Year: 2007
Publisher: Springer Verlag
Description: 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. 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.
Standard no: Frias-Martinez , E., & Gobet, F. (in press). Automatic generation of cognitive theories using genetic programming. Minds & Machines.
Appears in Collections:Dept of Life Sciences Research Papers

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