Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Learning and inference in the brain
Neural Networks - Special issue: Neuroinformatics
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Visual Selective Behavior Can Be Triggered by a Feed-Forward Process
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Object recognition by artificial cortical maps
Neural Networks
Observing learned object-specific functional grasps preferentially activates the ventral stream
Journal of Cognitive Neuroscience
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Visual perception involves the grouping of individual elements into coherent patterns, such as object representations, that reduce the descriptive complexity of a visual scene. The computational and physiological bases of this perceptual remain poorly understood. We discuss recent fMRI evidence from our laboratory where we measured activity in a higher object processing area (LOC), and in primary visual cortex (V1) in response to visual elements that were either grouped into objects or randomly arranged. We observed significant activity increases in the LOC and concurrent reductions of activity in V1 when elements formed coherent shapes, suggesting that activity in early visual areas is reduced as a result of grouping processes performed in higher areas. In light of these results we review related empirical findings of context-dependent changes in activity, recent neurophysiology research related to cortical feedback, and computational models that incorporate feedback operations. We suggest that feedback from high-level visual areas reduces activity in lower areas in order to simplify the description of a visual image--consistent with both predictive coding models of perception and probabilistic notions of 'explaining away.'