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Area of Science:

  • Cognitive Psychology
  • Perception Science
  • Human Behavior

Background:

  • Facial expression recognition is rapid, often under 1 second.
  • Research indicates facial attributes like sex influence expression perception.
  • Inconsistencies exist regarding participant sex differences in these perceptions.

Purpose of the Study:

  • To clarify participant sex differences in facial expression recognition.
  • To investigate the influence of analytical methods on reaction time (RT) data.
  • To identify robust methods for analyzing RT data in facial perception studies.

Main Methods:

  • Multiverse and distributional analyses were employed.
  • Outlier removal methods and specific distribution models were compared.
  • The Diffusion Model, ex-Gaussian, ex-Wald, and shifted Wald distributions were considered.

Main Results:

  • Participant sex differences in RT were contingent on outlier removal and distribution modeling choices.
  • The Face Sex × Expression interaction pattern varied for female participants across stimulus sets.
  • Distributional models like ex-Gaussian offer improved RT analysis over mean RTs.

Conclusions:

  • Analytical choices significantly impact findings on sex differences in facial perception.
  • Distributional models are recommended for more accurate reaction time analyses.
  • Understanding these analytical nuances is crucial for reliable conclusions in facial expression research.