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How face perception unfolds over time

Author

Listed:
  • Katharina Dobs

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Leyla Isik

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Dimitrios Pantazis

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Nancy Kanwisher

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

Abstract

Within a fraction of a second of viewing a face, we have already determined its gender, age and identity. A full understanding of this remarkable feat will require a characterization of the computational steps it entails, along with the representations extracted at each. Here, we used magnetoencephalography (MEG) to measure the time course of neural responses to faces, thereby addressing two fundamental questions about how face processing unfolds over time. First, using representational similarity analysis, we found that facial gender and age information emerged before identity information, suggesting a coarse-to-fine processing of face dimensions. Second, identity and gender representations of familiar faces were enhanced very early on, suggesting that the behavioral benefit for familiar faces results from tuning of early feed-forward processing mechanisms. These findings start to reveal the time course of face processing in humans, and provide powerful new constraints on computational theories of face perception.

Suggested Citation

  • Katharina Dobs & Leyla Isik & Dimitrios Pantazis & Nancy Kanwisher, 2019. "How face perception unfolds over time," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09239-1
    DOI: 10.1038/s41467-019-09239-1
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    Cited by:

    1. Rodrigo Quian Quiroga & Marta Boscaglia & Jacques Jonas & Hernan G. Rey & Xiaoqian Yan & Louis Maillard & Sophie Colnat-Coulbois & Laurent Koessler & Bruno Rossion, 2023. "Single neuron responses underlying face recognition in the human midfusiform face-selective cortex," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Haider Al-Tahan & Yalda Mohsenzadeh, 2021. "Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-19, March.
    3. Irina Higgins & Le Chang & Victoria Langston & Demis Hassabis & Christopher Summerfield & Doris Tsao & Matthew Botvinick, 2021. "Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    4. Katherine L. Hermann & Shridhar R. Singh & Isabelle A. Rosenthal & Dimitrios Pantazis & Bevil R. Conway, 2022. "Temporal dynamics of the neural representation of hue and luminance polarity," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    5. Annika Garlichs & Helen Blank, 2024. "Prediction error processing and sharpening of expected information across the face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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