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Lateralized repetition priming for unfamiliar faces.

Douglas Martin1, Louise K Nind, C Neil Macrae

  • 1University of Aberdeen, UK. doug.martin@northumbria.ac.uk

Experimental Psychology
|March 18, 2009
PubMed
Summary

Repetition priming (RP) for unfamiliar faces occurs only in the right hemisphere, regardless of presentation timing, but only when hair is removed from the faces. This finding offers new insights into facial recognition and hemispheric specialization.

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Area of Science:

  • Cognitive Neuroscience
  • Neuroscience
  • Psychology

Background:

  • Repetition priming (RP) enhances stimulus recognition after prior exposure.
  • Previous studies show RP for familiar faces is right-hemisphere dominant.
  • The current study investigates RP for unfamiliar faces.

Purpose of the Study:

  • To determine if repetition priming for unfamiliar faces exhibits right-hemisphere lateralization.
  • To examine the influence of stimulus presentation timing (study vs. test) on this effect.
  • To assess the impact of facial features, specifically hair, on RP for unfamiliar faces.

Main Methods:

  • Utilized a divided-visual-field methodology to present unfamiliar faces to either the left or right hemisphere.
  • Assessed repetition priming effects for unfamiliar faces under different presentation conditions.
  • Manipulated the presence or absence of hair on the faces during the priming task.

Main Results:

  • Repetition priming for unfamiliar faces was observed exclusively in the right hemisphere.
  • This right-hemisphere effect was consistent whether faces were presented laterally at study or at test.
  • Priming for unfamiliar faces occurred only when hair was cropped from the stimuli.

Conclusions:

  • Unfamiliar face recognition exhibits right-hemisphere lateralization, similar to familiar faces.
  • The right hemisphere's role in processing unfamiliar faces is robust to presentation timing.
  • The absence of hair is a critical factor for right-hemisphere repetition priming of unfamiliar faces, suggesting specific feature processing.