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Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

The visual system discounts emotional deviants when extracting average expression.

Jason Haberman1, David Whitney

  • 1University of California, Davis, California, USA. haberman@wjh.harvard.edu

Attention, Perception & Psychophysics
|October 19, 2010
PubMed
Summary
This summary is machine-generated.

The visual system efficiently processes sets of faces by averaging expressions, automatically discounting emotional outliers. This rapid ensemble coding occurs even with brief exposures, highlighting efficient visual perception.

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Area of Science:

  • Cognitive psychology
  • Visual neuroscience
  • Perception

Background:

  • Ensemble coding represents visual stimuli using summary statistics.
  • This ability extends to complex objects like faces, requiring high-level processing.
  • Debate exists on whether perceived averages reflect true summaries or subset sampling.

Purpose of the Study:

  • Investigate ensemble coding of facial expressions, specifically how emotional outliers are handled.
  • Determine if the visual system computes a true average or relies on sampling.
  • Explore the speed and attentional constraints of ensemble perception.

Main Methods:

  • Observers judged average expressions of face sets containing emotional outliers.
  • Computational modeling and behavioral experiments were conducted.
  • Stimulus exposure was limited to 250 milliseconds.

Main Results:

  • The visual system implicitly discounts emotional outliers when computing average facial expressions.
  • Perceived averages represent the majority of items, not a small subset.
  • Precise ensemble information is derived rapidly, suggesting minimal attentional limits.

Conclusions:

  • Ensemble coding for faces is robust and automatically discounts outliers.
  • The process is rapid and flexible, not limited by serial attention.
  • This challenges models relying solely on cognitive sampling strategies.