July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
The contents of the search template for naturalistic visual search
Author Affiliations
  • Reshanne Reeder
    Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
  • Marius Peelen
    Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
Journal of Vision July 2013, Vol.13, 699. doi:https://doi.org/10.1167/13.9.699
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      Reshanne Reeder, Marius Peelen; The contents of the search template for naturalistic visual search. Journal of Vision 2013;13(9):699. https://doi.org/10.1167/13.9.699.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Visual search involves the matching of visual input to a "search template" – an internal representation of task-relevant information. The present study investigated the contents of the search template during naturalistic visual search, for which low-level features do not reliably distinguish targets from non-targets. Participants were cued to detect people or cars in diverse photographs of real-world scenes. On a subset of trials, the cue was followed by task-irrelevant stimuli in place of scenes, directly followed by a dot that participants were instructed to detect. We hypothesized that stimuli that matched the active search template would capture attention, resulting in faster detection of the dot when presented at the location of a template-matching stimulus. Results revealed attentional capture for silhouettes, but not surface features (color and texture), of the cued category. Silhouettes captured attention irrespective of their orientation (0°, 90°, or 180°). Interestingly, strong capture was observed for silhouettes of category-diagnostic object parts, such as the wheel of a car. Finally, attentional capture was also observed for silhouettes presented at task-irrelevant locations. Together, these results indicate that search for object categories in real-world scenes is mediated by spatially global search templates that consist of view-invariant shape representations of category-diagnostic object parts.

Meeting abstract presented at VSS 2013

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