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dc.contributor.author
Stienen, Bernard M.C.
dc.contributor.author
Schindler, Konrad
dc.contributor.author
de Gelder, Beatrice
dc.date.accessioned
2022-08-31T08:01:17Z
dc.date.available
2022-08-31T07:58:51Z
dc.date.available
2022-08-31T08:01:17Z
dc.date.issued
2012-07
dc.identifier.issn
0899-7667
dc.identifier.issn
1530-888X
dc.identifier.other
10.1162/NECO_a_00305
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/567922
dc.description.abstract
Given the presence of massive feedback loops in brain networks, it is difficult to disentangle the contribution of feedforward and feedback processing to the recognition of visual stimuli, in this case, of emotional body expressions. The aim of the work presented in this letter is to shed light on how well feedforward processing explains rapid categorization of this important class of stimuli. By means of parametric masking, it may be possible to control the contribution of feedback activity in human participants. A close comparison is presented between human recognition performance and the performance of a computational neural model that exclusively modeled feedforward processing and was engineered to fulfill the computational requirements of recognition. Results show that the longer the stimulus onset asynchrony (SOA), the closer the performance of the human participants was to the values predicted by the model, with an optimum at an SOA of 100 ms. At short SOA latencies, human performance deteriorated, but the categorization of the emotional expressions was still above baseline. The data suggest that, although theoretically, feedback arising from inferotemporal cortex is likely to be blocked when the SOA is 100 ms, human participants still seem to rely on more local visual feedback processing to equal the model's performance.
en_US
dc.language.iso
en
en_US
dc.publisher
MIT Press
en_US
dc.title
A Computational Feedforward Model Predicts Categorization of Masked Emotional Body Language for Longer, but Not for Shorter, Latencies
en_US
dc.type
Journal Article
ethz.journal.title
Neural Computation
ethz.journal.volume
24
en_US
ethz.journal.issue
7
en_US
ethz.journal.abbreviated
Neural Comput
ethz.pages.start
1806
en_US
ethz.pages.end
1821
en_US
ethz.identifier.wos
ethz.publication.place
Cambridge, MA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::03886 - Schindler, Konrad / Schindler, Konrad
ethz.date.deposited
2017-06-10T01:16:20Z
ethz.source
ECIT
ethz.identifier.importid
imp59364f467d40722281
ethz.identifier.importid
imp593650517de2b70254
ethz.ecitpid
pub:81835
ethz.ecitpid
pub:100148
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-08-31T07:59:01Z
ethz.rosetta.lastUpdated
2022-08-31T07:59:01Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/49851
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/163418
ethz.COinS
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