Journal
BRAIN RESEARCH
Volume 1157, Issue -, Pages 167-176Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.brainres.2007.03.090
Keywords
visual perception; computational neural network model; shape selectivity; crowding
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Recent theories of visual perception propose that feedforward cortical processing enables rapid and automatic object categorizations, yet incorporates a limited amount of detail. Subsequent feedback processing highlights high-resolution representations in early visual areas and provides spatial detail. To verify this hypothesis, we separate the contributions of feedforward and feedback signals to the selectivity of cortical neurons in a neural network simulation that is modeled after the hierarchical feedforward-feedback organization of cortical areas. We find that in such a network the responses of high-level neurons can initially distinguish between low-resolution aspects of objects but are 'blind' to differences in detail. After several feedback-feedforward cycles of processing, however, they can also distinguish between objects that differ in detail. Moreover, we find that our model captures recent paradoxical results of crowding phenomena, showing that spatial detail that is lost in visual crowding is nevertheless able to evoke specific adaptation effects. Our results thus provide an existence proof of the feasibility of novel theoretical models and provide a mechanism to explain various psychophysical and physiological results. (c) 2007 Elsevier B.V. All rights reserved.
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