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Is Coarse-to-Fine Strategy Sensitive to Normal Aging?

Overview of attention for article published in PLOS ONE, June 2012
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Title
Is Coarse-to-Fine Strategy Sensitive to Normal Aging?
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038493
Pubmed ID
Authors

Benoit Musel, Alan Chauvin, Nathalie Guyader, Sylvie Chokron, Carole Peyrin

Abstract

Theories on visual perception agree that visual recognition begins with global analysis and ends with detailed analysis. Different results from neurophysiological, computational, and behavioral studies all indicate that the totality of visual information is not immediately conveyed, but that information analysis follows a predominantly coarse-to-fine processing sequence (low spatial frequencies are extracted first, followed by high spatial frequencies). We tested whether such processing continues to occur in normally aging subjects. Young and aged participants performed a categorization task (indoor vs. outdoor scenes), using dynamic natural scene stimuli, in which they resorted to either a coarse-to-fine (CtF) sequence or a reverse fine-to-coarse sequence (FtC). The results show that young participants categorized CtF sequences more quickly than FtC sequences. However, sequence processing interacts with semantic category only for aged participants. The present data support the notion that CtF categorization is effective even in aged participants, but is constrained by the spatial features of the scenes, thus highlighting new perspectives in visual models.

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Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 4%
Switzerland 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 25%
Researcher 5 18%
Professor > Associate Professor 4 14%
Professor 2 7%
Student > Doctoral Student 2 7%
Other 5 18%
Unknown 3 11%
Readers by discipline Count As %
Psychology 18 64%
Medicine and Dentistry 2 7%
Neuroscience 2 7%
Computer Science 1 4%
Unknown 5 18%