September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
The neural basis of face ensemble processing: An EEG-based investigation of facial identity summary statistics
Author Affiliations
  • Tyler Roberts
    Department of Psychology at University of Toronto Scarborough, Scarborough, ON
  • Jonathan Cant
    Department of Psychology at University of Toronto Scarborough, Scarborough, ON
  • Adrian Nestor
    Department of Psychology at University of Toronto Scarborough, Scarborough, ON
Journal of Vision September 2018, Vol.18, 1089. doi:10.1167/18.10.1089
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      Tyler Roberts, Jonathan Cant, Adrian Nestor; The neural basis of face ensemble processing: An EEG-based investigation of facial identity summary statistics. Journal of Vision 2018;18(10):1089. doi: 10.1167/18.10.1089.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Extensive behavioural work has documented our ability to extract summary statistics from groups of faces such as average emotion, gender, and identity. However, to date little is known about the neural mechanisms subserving the extraction of summary statistics from face ensembles. Here, we used electroencephalography (EEG) to examine and compare the neural processing of facial identity from ensembles and single faces. To this end, we collected EEG data across 14 participants who viewed ensembles composed of 6 faces as well as single faces presented one at a time. Critically, ensembles were designed such that, though they consisted of different individual faces, they could lead, half of the time, to the same summary representation (i.e., to the same average face), and, half of the time, to different summary representations. Pattern analyses were then conducted across spatiotemporal signals recorded from 12 bilateral occipitotemporal electrodes. These analyses found, first, that single faces can be well discriminated from their corresponding EEG signal, consistent with previous work. Second, ensembles with different average identities, but not those with the same average identity, could be discriminated from each other above chance. Third, critically, classifiers trained on ensembles with different average identities were able to successfully discriminate their corresponding average identities presented as single stimuli. Finally, face ensembles and single faces exhibited different time courses of discrimination. Specifically, ensemble discrimination reached significance earlier, but peaked later than single-face discrimination, suggesting an extensive interval of evidence accumulation and information processing (i.e., average identity extraction). Thus, to our knowledge, the present findings provide the first evidence based on EEG data regarding the extraction of summary statistics from face ensembles. Further, they serve to characterize the temporal profile of ensemble processing and its relationship with single face recognition.

Meeting abstract presented at VSS 2018

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