title: Attention as reward-driven optimization of sensory processing.
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).
authors.affiliation: Group for Neural Theory, LNC, DEC, Ecole Supérieure, Paris 75005, France matthewjchalk@gmail.com.
authors.forename: Matthew
authors.initials: M
authors.lastname: Chalk
authors.forename: Iain
authors.initials: I
authors.lastname: Murray
authors.forename: Peggy
authors.initials: P
authors.lastname: Seriès
mesh_terms: D000818:Animals; D001288:Attention / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D000882:Haplorhini; D006801:Humans; D008959:Models, Neurological; D012201:Reward; D014796:Visual Perception / Q000502:physiology*