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Modeling sensory-motor decisions in natural behavior.
Zhang, Ruohan; Zhang, Shun; Tong, Matthew H; Cui, Yuchen; Rothkopf, Constantin A; Ballard, Dana H; Hayhoe, Mary M.
Afiliação
  • Zhang R; Department of Computer Science, The University of Texas at Austin, Austin, TX, USA.
  • Zhang S; Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Tong MH; Center for Perceptual Systems, The University of Texas at Austin, Austin, TX, USA.
  • Cui Y; Department of Computer Science, The University of Texas at Austin, Austin, TX, USA.
  • Rothkopf CA; Cognitive Science Center and Institute of Psychology, Technical University Darmstadt, Darmstadt, Germany.
  • Ballard DH; Department of Computer Science, The University of Texas at Austin, Austin, TX, USA.
  • Hayhoe MM; Center for Perceptual Systems, The University of Texas at Austin, Austin, TX, USA.
PLoS Comput Biol ; 14(10): e1006518, 2018 10.
Article em En | MEDLINE | ID: mdl-30359364
ABSTRACT
Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Desempenho Psicomotor / Reforço Psicológico / Tomada de Decisões Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Desempenho Psicomotor / Reforço Psicológico / Tomada de Decisões Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos
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