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Selection for Object Identification: Modelling Emergent Attentional Processes in Normality and Pathology

  • Glyn W. Humphreys
  • Dietmar Heinke
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

We report a model of translation-invariant object recognition: SAIM (for Selective Attention for Identification Model). In SAIM, objects compete in a “selection network” to achieve a mapping from their location on the retina to a translation-invariant “focus of attention”. We investigated spatially selective lesions affecting either the mapping from one side of the retina into the selection network, or the mapping from the selection network to one side of the attentional window. Lesions into the selection network produced a pattern of retinotopic neglect, with processing biased against stimuli on the affected side of the retina. Lesions out of the selection network produced a form of neglect that was tied to one side of an object irrespective of where the object fell on the retina. These different forms of neglect match patterns reported in brain lesioned human patients.We propose that forms of selection in vision emerge as a consequence of procedures for achieving viewpoint-independent object recognition.

Keywords

Selection Network Control Layer Left Visual Field Retinal Location Cognitive Neuropsychology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1998

Authors and Affiliations

  • Glyn W. Humphreys
    • 1
  • Dietmar Heinke
    • 1
  1. 1.University of BirminghamUK

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