Decoding face recognition abilities in the human brain
Why are some individuals better at recognizing faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multimodal data-driven approach combining neuroimaging, computational modeling, and behavioral tests. We recorded the hig...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
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Oxford University Press
2024
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| _version_ | 1811139686845906944 |
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| author | Faghel-Soubeyrand, S Ramon, M Bamps, E Zoia, M Woodhams, J Richoz, A-R Caldara, R Gosselin, F Charest, I |
| author_facet | Faghel-Soubeyrand, S Ramon, M Bamps, E Zoia, M Woodhams, J Richoz, A-R Caldara, R Gosselin, F Charest, I |
| author_sort | Faghel-Soubeyrand, S |
| collection | OXFORD |
| description | Why are some individuals better at recognizing faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multimodal data-driven approach combining neuroimaging, computational modeling, and behavioral tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognizers—and typical recognizers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 s of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognizers, we found stronger associations between early brain representations of super-recognizers and midlevel representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognizers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multimodal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. |
| first_indexed | 2024-09-25T04:10:02Z |
| format | Journal article |
| id | oxford-uuid:1c973fe1-e442-419d-9ec0-14ec4e490900 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:10:02Z |
| publishDate | 2024 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:1c973fe1-e442-419d-9ec0-14ec4e4909002024-06-17T15:54:29ZDecoding face recognition abilities in the human brainJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1c973fe1-e442-419d-9ec0-14ec4e490900EnglishSymplectic ElementsOxford University Press2024Faghel-Soubeyrand, SRamon, MBamps, EZoia, MWoodhams, JRichoz, A-RCaldara, RGosselin, FCharest, IWhy are some individuals better at recognizing faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multimodal data-driven approach combining neuroimaging, computational modeling, and behavioral tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognizers—and typical recognizers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 s of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared representations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognizers, we found stronger associations between early brain representations of super-recognizers and midlevel representations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognizers and representations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multimodal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. |
| spellingShingle | Faghel-Soubeyrand, S Ramon, M Bamps, E Zoia, M Woodhams, J Richoz, A-R Caldara, R Gosselin, F Charest, I Decoding face recognition abilities in the human brain |
| title | Decoding face recognition abilities in the human brain |
| title_full | Decoding face recognition abilities in the human brain |
| title_fullStr | Decoding face recognition abilities in the human brain |
| title_full_unstemmed | Decoding face recognition abilities in the human brain |
| title_short | Decoding face recognition abilities in the human brain |
| title_sort | decoding face recognition abilities in the human brain |
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