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Neural Correlates of the Perception for Novel Objects

Overview of attention for article published in PLOS ONE, April 2013
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Title
Neural Correlates of the Perception for Novel Objects
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0062979
Pubmed ID
Authors

Hao Zhang, Jia Liu, Qinglin Zhang

Abstract

Perception of novel objects is of enormous importance in our lives. People have to perceive or understand novel objects when seeing an original painting, admiring an unconventional construction, and using an inventive device. However, very little is known about neural mechanisms underlying the perception for novel objects. Perception of novel objects relies on the integration of unusual features of novel objects in order to identify what such objects are. In the present study, functional Magnetic Resonance Imaging (MRI) was employed to investigate neural correlates of perception of novel objects. The neuroimaging data on participants engaged in novel object viewing versus ordinary object viewing revealed that perception of novel objects involves significant activation in the left precuneus (Brodmann area 7) and the right visual cortex. The results suggest that the left precuneus is associated with the integration of unusual features of novel objects, while the right visual cortex is sensitive to the detection of such features. Our findings highlight the left precuneus as a crucial component of the neural circuitry underlying perception of novel objects.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
China 1 2%
Brazil 1 2%
Unknown 40 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Researcher 7 16%
Other 5 12%
Professor 5 12%
Professor > Associate Professor 3 7%
Other 5 12%
Unknown 7 16%
Readers by discipline Count As %
Psychology 16 37%
Neuroscience 9 21%
Engineering 3 7%
Medicine and Dentistry 2 5%
Computer Science 1 2%
Other 3 7%
Unknown 9 21%