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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Spatially generalizable representations of facial expressions: Decoding across partial face samples.

Steven G Greening1, Derek G V Mitchell2, Fraser W Smith3

  • 1Department of Psychology, Louisiana State University, Baton Rouge, USA; Pennington Biomedical Research Institute, Louisiana State University, Baton Rouge, USA.

Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
|February 8, 2018
PubMed
Summary

Facial expression recognition networks generalize across different face parts. Neural information in regions like the superior temporal sulcus (STS) and dorsal prefrontal cortex (dPFC) aids decoding, even with visual occlusion.

Keywords:
Emotion recognitionMVPAPattern classificationfMRI

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Psychology

Background:

  • Facial expression processing involves a network of cortical and sub-cortical regions.
  • Previous research has not determined if facial expression representations generalize across different facial features (e.g., eyes vs. mouth).

Purpose of the Study:

  • To investigate if neural representations of facial expressions generalize across independent facial samples.
  • To identify brain regions containing generalizable facial expression information.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) was used in a rapid event-related design.
  • Participants viewed partial facial samples of five expression categories.
  • Machine learning classifiers were trained to decode expression categories from neural data.

Main Results:

  • A network of face-sensitive regions was identified, containing expression category information irrespective of the facial part presented.
  • Neural information in dorsal prefrontal cortex (dPFC), superior temporal sulcus (STS), lateral occipital, ventral temporal, and early visual cortex allowed generalization across 'eyes only' and 'eyes removed' conditions.
  • Classification performance correlated with behavioral performance in STS and dPFC.

Conclusions:

  • Both higher-level (STS, dPFC) and lower-level cortical regions contain generalizable facial expression information.
  • Neural decoding of facial expressions extends beyond the visually presented information.
  • Cortical feedback mechanisms may play a crucial role in facial expression perception under visual occlusion.