NDLI: Global statistical regularities modulate the speed of visual search in patients with focal attentional deficits.

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Author	Lucilla eLanzoni David eMelcher Gabriele eMiceli Jennifer Elise Corbett
Abstract	There is growing evidence that the statistical properties of ensembles of similar objects are processed in a qualitatively different manner than the characteristics of individual items. It has recently been proposed that these types of perceptual statistical representations are part of a strategy to complement focused attention in order to circumvent the visual system's limited capacity to represent more than a few individual objects in detail. Previous studies have demonstrated that patients with attentional deficits are nonetheless sensitive to these sorts of statistical representations. Here, we examined how such global representations may function to aid patients in overcoming focal attentional limitations by manipulating the statistical regularity of a visual scene while patients performed a search task. Three patients previously diagnosed with visual neglect searched for a target Gabor tilted to the left or right of vertical in displays of horizontal distractor Gabors. Although the local sizes of the distractors changed on every trial, the mean size remained stable for several trials. Patients made faster correct responses to targets in neglected regions of the visual field when global statistics remained constant over several trials, similar to age-matched controls. Given neglect patients' attentional deficits, these results suggest that stable perceptual representations of global statistics can establish a context to speed search without the need to represent individual elements in detail.
e-ISSN	16641078
DOI	10.3389/fpsyg.2014.00514
Journal	Frontiers in Psychology
Volume Number	5
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2014-06-01
Publisher Place	Switzerland
Access Restriction	Open
Subject Keyword	Psychology Attention Visual Search Visual Neglect Mean Size Representation Perceptual Averaging Ensemble Statistics
Content Type	Text
Resource Type	Article

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