September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Isolating rapid and automatic human facial expression categorization
Author Affiliations
  • Fanny Poncet
    Group "Developmental Ethology and Cognitive Psychology", Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, F-21000 Dijon, France
  • Milena Dzhelyova
    Psychological Sciences Research Institute and Institute of Neuroscience, Université catholique de Louvain (UCL), 1348 Louvain-la-Neuve, Belgium
  • Jean-Yves Baudouin
    Group "Developmental Ethology and Cognitive Psychology", Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, F-21000 Dijon, France
  • Bruno Rossion
    Psychological Sciences Research Institute and Institute of Neuroscience, Université catholique de Louvain (UCL), 1348 Louvain-la-Neuve, BelgiumService de Neurologie, Centre Hospitalier Universitaire de Nancy, 54035 Nancy, France
  • Arnaud Leleu
    Group "Developmental Ethology and Cognitive Psychology", Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, F-21000 Dijon, France
Journal of Vision September 2018, Vol.18, 907. doi:10.1167/18.10.907
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      Fanny Poncet, Milena Dzhelyova, Jean-Yves Baudouin, Bruno Rossion, Arnaud Leleu; Isolating rapid and automatic human facial expression categorization. Journal of Vision 2018;18(10):907. doi: 10.1167/18.10.907.

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

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Abstract

Efficient understanding of emotions from expressive faces is crucial for human interactions. Specific human electrophysiological responses to brief neutral-to-emotion changes of facial expression can be isolated in a few minutes, without explicit task, with fast periodic visual stimulation (Dzhelyova et al., 2017). Here we aimed at extending these observations to the categorization of human basic facial expressions. We recorded scalp EEG from 15 participants (10 females). In experiment 1, a neutral face was presented 6 times per second (i.e., 6 Hz) and the same face expressing an emotion (i.e., anger, disgust, fear, happiness or sadness in different sequences) appeared every five pictures to measure detection of facial expressions at the 1.2 Hz frequency. Participants performed an orthogonal task (fixation circle-to-square change detection) throughout the stimulation. In experiment 2, a specific facial expression also appeared at the 1.2 Hz rate but all other facial expressions were randomly displayed in between. Hence, expression-changes intervened at 6 Hz and only the categorization of a specific emotional expression is measured at 1.2 Hz. Significant 1.2 Hz (and harmonics, 2.4 Hz, etc.) responses were found in both experiments in the EEG spectra, showing that the categorization of an emotional expression can be isolated irrespective of expression-change detection. A decoding approach reveals distinct topographies between the different emotions in both experiments. However, decoding performance was greater than chance for all emotions only in experiment 2, which isolates facial categorization from general expression-change detection processes. Overall, these findings indicate that rapid emotion categorization exempt from more general expression-change detection processes can be objectively (i.e., at pre-determined frequencies) isolated in the human brain in a few minutes of recording and support partly distinct neural sources for the visual processing of different emotion categories. Keywords: Fast Periodic Visual Stimulation, EEG, frequency-tagging, facial expression, detection, categorization.

Meeting abstract presented at VSS 2018

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