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Attention as reward-driven optimization of sensory processing.
Chalk, Matthew; Murray, Iain; Seriès, Peggy.
Afiliação
  • Chalk M; Group for Neural Theory, LNC, DEC, Ecole Supérieure, Paris 75005, France matthewjchalk@gmail.com.
Neural Comput ; 25(11): 2904-33, 2013 Nov.
Article em En | MEDLINE | ID: mdl-23777518
ABSTRACT
Attention causes diverse changes to visual neuron responses, including alterations in receptive field structure, and firing rates. A common theoretical approach to investigate why sensory neurons behave as they do is based on the efficient coding

hypothesis:

that sensory processing is optimized toward the statistics of the received input. We extend this approach to account for the influence of task demands, hypothesizing that the brain learns a probabilistic model of both the sensory input and reward received for performing different actions. Attention-dependent changes to neural responses reflect optimization of this internal model to deal with changes in the sensory environment (stimulus statistics) and behavioral demands (reward statistics). We use this framework to construct a simple model of visual processing that is able to replicate a number of attention-dependent changes to the responses of neurons in the midlevel visual cortices. The model is consistent with and provides a normative explanation for recent divisive normalization models of attention (Reynolds & Heeger, 2009).
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Recompensa / Atenção / Percepção Visual / Encéfalo / Modelos Neurológicos Limite: Animals / Humans Idioma: En Ano de publicação: 2013 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Recompensa / Atenção / Percepção Visual / Encéfalo / Modelos Neurológicos Limite: Animals / Humans Idioma: En Ano de publicação: 2013 Tipo de documento: Article