Automatic Generation of Cognitive Theories using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Frias-Martinez:2007,
  author =       "Enrique Frias-Martinez and Fernand Gobet",
  title =        "Automatic Generation of Cognitive Theories using
                 Genetic Programming",
  journal =      "Minds and Machines",
  volume =       "17",
  number =       "3",
  year =         "2007",
  pages =        "287--309",
  month =        oct,
  address =      "Hingham, MA, USA",
  publisher =    "Kluwer Academic Publishers",
  keywords =     "genetic algorithms, genetic programming, Cognitive
                 neuroscience, Computational neuroscience, Automatic
                 generation of cognitive theories,
                 Delayed-match-to-sample",
  ISSN =         "0924-6495",
  DOI =          "doi:10.1007/s11023-007-9070-6",
  size =         "23 pages",
  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.",
  notes =        "Artificial intelligence, cognitive memory. Fit both
                 mean and standard deviation of experimental (people)
                 results. Lisp. Data from Chao et al. 1999, pictures of
                 animals and pictures of tools. WSTM. Table 3 putSTM ...
                 {"}Write the input parameter in STM{"}

                 Also known as \cite{1298700}",
}

Genetic Programming entries for Enrique Frias-Martinez Fernand Gobet

Citations