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It Takes Two–Skilled Recognition of Objects Engages Lateral Areas in Both Hemispheres

Overview of attention for article published in PLOS ONE, January 2011
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Title
It Takes Two–Skilled Recognition of Objects Engages Lateral Areas in Both Hemispheres
Published in
PLOS ONE, January 2011
DOI 10.1371/journal.pone.0016202
Pubmed ID
Authors

Merim Bilalić, Andrea Kiesel, Carsten Pohl, Michael Erb, Wolfgang Grodd

Abstract

Our object recognition abilities, a direct product of our experience with objects, are fine-tuned to perfection. Left temporal and lateral areas along the dorsal, action related stream, as well as left infero-temporal areas along the ventral, object related stream are engaged in object recognition. Here we show that expertise modulates the activity of dorsal areas in the recognition of man-made objects with clearly specified functions. Expert chess players were faster than chess novices in identifying chess objects and their functional relations. Experts' advantage was domain-specific as there were no differences between groups in a control task featuring geometrical shapes. The pattern of eye movements supported the notion that experts' extensive knowledge about domain objects and their functions enabled superior recognition even when experts were not directly fixating the objects of interest. Functional magnetic resonance imaging (fMRI) related exclusively the areas along the dorsal stream to chess specific object recognition. Besides the commonly involved left temporal and parietal lateral brain areas, we found that only in experts homologous areas on the right hemisphere were also engaged in chess specific object recognition. Based on these results, we discuss whether skilled object recognition does not only involve a more efficient version of the processes found in non-skilled recognition, but also qualitatively different cognitive processes which engage additional brain areas.

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Geographical breakdown

Country Count As %
United States 4 5%
United Kingdom 3 4%
Germany 2 3%
Luxembourg 1 1%
Unknown 68 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 18%
Student > Ph. D. Student 13 17%
Professor 9 12%
Other 7 9%
Student > Bachelor 7 9%
Other 20 26%
Unknown 8 10%
Readers by discipline Count As %
Psychology 29 37%
Medicine and Dentistry 8 10%
Neuroscience 7 9%
Engineering 4 5%
Business, Management and Accounting 4 5%
Other 10 13%
Unknown 16 21%