Abstract
Eye movement patterns and consistency during face recognition are both associated with recognition performance. We examined whether they reflect different mechanisms through EEG decoding. Eighty-four participants performed an old-new face recognition task with eye movement pattern and consistency quantified using eye movement analysis with hidden Markov models (EMHMM). Temporal dynamics of neural representation quality for face recognition were assessed through decoding old vs new faces using a support vector machine classifier. Results showed that a more eye-focused pattern was associated with higher decoding accuracy in the high-alpha band, reflecting better neural representation quality. In contrast, higher eye movement consistency was associated with shorter latency of peak decoding accuracy in the high-alpha band, which suggested more efficient neural representation development, in addition to higher ERP decoding accuracy. Thus, eye movement patterns are associated with neural representation effectiveness, whereas eye movement consistency reflects neural representation development efficiency, unraveling different aspects of cognitive processes.