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Eye movement patterns and consistency during face recognition reveal distinct cognitive mechanisms. Eye-focused patterns enhance neural representation quality, while consistency indicates efficient neural development for better face recognition.

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Area of Science:

  • Cognitive Neuroscience
  • Neuroscience
  • Psychology

Background:

  • Eye movement patterns and consistency correlate with face recognition performance.
  • Understanding the underlying neural mechanisms is crucial for cognitive science.

Purpose of the Study:

  • To investigate whether eye movement patterns and consistency reflect different neural mechanisms during face recognition.
  • To assess the relationship between eye movement characteristics and electroencephalography (EEG) decoding accuracy.

Main Methods:

  • Eighty-four participants completed an old-new face recognition task.
  • Eye movement patterns and consistency were quantified using eye movement analysis with hidden Markov models (EMHMM).
  • EEG data was analyzed using a support vector machine classifier to decode neural representations of faces.

Main Results:

  • An eye-focused pattern correlated with higher decoding accuracy in the high-alpha band, indicating better neural representation quality.
  • Higher eye movement consistency was linked to shorter latency of peak decoding accuracy in the high-alpha band, suggesting more efficient neural representation development.
  • Both eye movement pattern and consistency showed associations with electroencephalography (EEG) decoding accuracy.

Conclusions:

  • Eye movement patterns are associated with the effectiveness of neural representations in face recognition.
  • Eye movement consistency reflects the efficiency of neural representation development during face recognition.
  • These findings unravel distinct cognitive processes underlying face recognition linked to eye movement behaviors.