Behavioral and Brain Sciences 12 (3):381-397 (1989)

Purely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions have been demonstrated empirically. Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: How can objects be identified from simultaneously attended features in a parallel, distributed representation? How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention.
Keywords affordance   attention   connectionist network   eye movements   illusory conjunction   neural network   object recognition   retinotopic representations   saccades   spatial localization
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DOI 10.1017/s0140525x0005679x
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Image and Mind.Stephen M. Kosslyn - 1980 - Harvard University Press.

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Analyzing Vision at the Complexity Level.John K. Tsotsos - 1990 - Behavioral and Brain Sciences 13 (3):423-445.

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