Journal of Cognitive Neuroscience
Reinforcement Learning for Decision Making in Sequential Visual Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Visual search in static and dynamic scenes using fine-grain top-down visual attention
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
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Cognitive behaviour requires complex context-dependent processing of information that emerges from the links between attentional perceptual processes, working memory and reward-based evaluation of the performed actions. We describe a computational neuroscience theoretical framework which shows how an attentional state held in a short term memory in the prefrontal cortex can by top-down processing influence ventral and dorsal stream cortical areas using biased competition to account for many aspects of visual attention. We also show how within the prefrontal cortex an attentional bias can influence the mapping of sensory inputs to motor outputs, and thus play an important role in decision making. This theoretical framework incorporates spiking and synaptic dynamics which enable single neuron responses, fMRI activations, psychophysical results, and the effects of damage to parts of the system, to be explicitly simulated and predicted. This computational neuroscience framework provides an approach for integrating different levels of investigation of brain function, and for understanding the relations between them.