IMR Press / FBL / Volume 13 / Issue 14 / DOI: 10.2741/3080

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article
The redundant-signals paradigm and preattentive visual processing
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1 Ludwig-Maximilians-Universitat, Germany
2 Universite de Fribourg, Switzerland

*Author to whom correspondence should be addressed.

Academic Editor: Stefan Pollmann

Front. Biosci. (Landmark Ed) 2008, 13(14), 5279–5293; https://doi.org/10.2741/3080
Published: 1 May 2008
(This article belongs to the Special Issue Prefrontal correlates of attention and executive control)
Abstract

Physiological and cognitive models of vision agree that the preattentive processing of visual stimuli is organized in a parallel and segregated fashion. However, several incompatible models have been proposed for the subsequent processing stages. They differ in their assumptions about architecture (serial, parallel, or coactive/integrative), stopping-rules (self-terminating, or exhaustive), spatial specificity of saliency signal coding (signal pooling across locations, or spatially distinct processing), and dependency of target detection on the prior allocation of attention (preattentive, or post-selective). We review how studies employing the redundant-signals paradigm in visual pop-out search contribute to discerning between the different assumptions. We find strong support for the notion of a saliency map, into which feature contrast signals are pooled, and especially the dimension weighting account (1) receives further support: Instead of a priming mechanism that could increase weights for several dimensions independently, evidence favors a weighting mechanism that effectively limits the total weight available for allocation to the various dimensions through competitive interactions, whereby increasing the weight for one dimension goes along with decreasing the weights for other dimensions.

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