August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Parametric study of N170 sensitivity to diagnostic facial information during face identification
Author Affiliations & Notes
  • Pierre-Louis Audette
    Université du Québec en Outaouais
  • Justin Duncan
    Université du Québec en Outaouais
  • Caroline Blais
    Université du Québec en Outaouais
  • Daniel Fiset
    Université du Québec en Outaouais
  • Footnotes
    Acknowledgements  This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2022-04350).
Journal of Vision August 2023, Vol.23, 5077. doi:https://doi.org/10.1167/jov.23.9.5077
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      Pierre-Louis Audette, Justin Duncan, Caroline Blais, Daniel Fiset; Parametric study of N170 sensitivity to diagnostic facial information during face identification. Journal of Vision 2023;23(9):5077. https://doi.org/10.1167/jov.23.9.5077.

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

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Abstract

Studies have suggested that the face-sensitive N170 indexes a face or an eye detection process. However, such studies have often explored N170 sensitivity in a dichotomic way, to the presence or absence of different facial features, either alone or within a facial context (e.g Parkington & Itier, 2018). Other studies have proposed that the N170 could reflect in-depth integration of diagnostic information (Schyns et al., 2007). The objective of this study was to parametrically investigate whether the N170 reflects the quantity of diagnostic information integrated by the brain. To this end, we randomly created sparse facial stimuli with Bubbles, and used previously published classification images (Royer et al., 2018) to calculate the amount of available diagnostic information on a stimulus basis. Stimuli were then divided into ten bins covering a range from 0.01% to 80% information. Furthermore, a 0% (scrambled face) and 100% (whole face) bin were also adjoined at each extremity of the information spectrum. To equalize energy across stimuli, we applied discrete wavelet transform to unfiltered faces, and filtered the scrambled output with inverse bubbles. In other words, face regions hidden by bubbles were replaced by scrambled face information. EEG was collected from five participants as they each underwent 1,440 trials of a 10-identities recognition task. Using the 0% information condition as baseline, we then looked at the N170 peak amplitude and latency at PO8, in addition to behavioral responses. Results showed both parameters were sensitive to the amount of diagnostic information. As diagnostic information increased, amplitude linearly increased, and latency decreased. Interestingly, individual N170 amplitudes across information bins almost perfectly predicted corresponding recognition accuracies. Perhaps surprisingly, no other electrophysiological process (at PO8) seemed responsive to diagnostic information. Thus, it appears the N170 reflects in-depth processing of diagnostic information during face identification.

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