Understanding the role of eye movement pattern and consistency during face recognition through EEG decoding

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.