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Nat Commun. 2014 Dec 8;5:5672. doi: 10.1038/ncomms6672.

Dynamic encoding of face information in the human fusiform gyrus.

Author information

1
1] University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, Pennsylvania 15213, USA [2] Department of Neurological Surgery, University of Pittsburgh, 200 Lothrop St, Pittsburgh, Pennsylvania 15213, USA [3] Center for the Neural Basis of Cognition, 4400 Fifth Ave., Pittsburgh, Pennsylvania 15213, USA.
2
1] University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, Pennsylvania 15213, USA [2] Department of Neurological Surgery, University of Pittsburgh, 200 Lothrop St, Pittsburgh, Pennsylvania 15213, USA.
3
1] Center for the Neural Basis of Cognition, 4400 Fifth Ave., Pittsburgh, Pennsylvania 15213, USA [2] Department of Psychology, Carnegie Mellon University, Baker Hall 342c, Pittsburgh, Pennsylvania 15213, USA.
4
1] University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, Pennsylvania 15213, USA [2] Center for the Neural Basis of Cognition, 4400 Fifth Ave., Pittsburgh, Pennsylvania 15213, USA [3] Department of Physical Medicine and Rehabilitation, University of Pittsburgh, 3471 Fifth Ave, Pittsburgh, Pennsylvania 15213, USA.

Abstract

Humans' ability to rapidly and accurately detect, identify and classify faces under variable conditions derives from a network of brain regions highly tuned to face information. The fusiform face area (FFA) is thought to be a computational hub for face processing; however, temporal dynamics of face information processing in FFA remains unclear. Here we use multivariate pattern classification to decode the temporal dynamics of expression-invariant face information processing using electrodes placed directly on FFA in humans. Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face. Activity between 200 and 500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations. Long-lasting (500+ms) broadband gamma frequency activity predicted task performance. These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses.

PMID:
25482825
PMCID:
PMC4339092
DOI:
10.1038/ncomms6672
[Indexed for MEDLINE]
Free PMC Article

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