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Robust averaging protects decisions from noise in neural computations

Abstract:

An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1371/journal.pcbi.1005723

Authors


More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Experimental Psychology
ORCID:
0000-0002-7934-5137
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Experimental Psychology
Solomon, JA More by this author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Experimental Psychology
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Experimental Psychology
ORCID:
0000-0002-2941-2653
Croucher Foundation More from this funder
Economic and Social Research Council More from this funder
Publisher:
Public Library of Science Publisher's website
Journal:
PLoS Computational Biology Journal website
Volume:
13
Issue:
8
Pages:
Article: e1005723
Publication date:
2017-08-25
Acceptance date:
2017-08-12
DOI:
EISSN:
1553-7358
ISSN:
1553-734X
Pubs id:
pubs:725890
URN:
uri:49eaec8e-e439-408b-b450-40cf8ec0a31c
UUID:
uuid:49eaec8e-e439-408b-b450-40cf8ec0a31c
Local pid:
pubs:725890
Language:
English

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