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Article Dans Une Revue NeuroImage Année : 2020

Decoding dynamic affective responses to naturalistic videos with shared neural patterns

Ale Smidts
  • Fonction : Auteur
Vincent C. Schoots
  • Fonction : Auteur
Alan G. Sanfey
  • Fonction : Auteur
Maarten A. S. Boksem
  • Fonction : Auteur

Résumé

This study explored the feasibility of using shared neural patterns from brief affective episodes (viewing affective pictures) to decode extended, dynamic affective sequences in a naturalistic experience (watching movie-trailers). Twenty-eight participants viewed pictures from the International Affective Picture System (IAPS) and, in a separate session, watched various movie-trailers. We first located voxels at bilateral occipital cortex (LOC) responsive to affective picture categories by GLM analysis, then performed between-subject hyperalignment on the LOC voxels based on their responses during movie-trailer watching. After hyperalignment, we trained between-subject machine learning classifiers on the affective pictures, and used the classifiers to decode affective states of an out-of-sample participant both during picture viewing and during movie-trailer watching. Within participants, neural classifiers identified valence and arousal categories of pictures, and tracked self-reported valence and arousal during video watching. In aggregate, neural classifiers produced valence and arousal time series that tracked the dynamic ratings of the movie-trailers obtained from a separate sample. Our findings provide further support for the possibility of using pre-trained neural representations to decode dynamic affective responses during a naturalistic experience.

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Dates et versions

hal-03188208 , version 1 (01-04-2021)

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  • HAL Id : hal-03188208 , version 1

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Hang-Yee Chan, Ale Smidts, Vincent C. Schoots, Alan G. Sanfey, Maarten A. S. Boksem. Decoding dynamic affective responses to naturalistic videos with shared neural patterns. NeuroImage, 2020, 216. ⟨hal-03188208⟩

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