The present study investigated the relationship between eye movement pattern in face recognition and cognitive performance during natural aging through modeling and comparing eye movement of young (18-24 years) and older (65-81 years) adults using Hidden Markov Model (HMM) based approach. Young adults recognized faces better than older adults, particularly when measured by the false alarm rate. Older adults’ recognition performance, on the other hand, correlated with their cognitive status assessed by the Montreal Cognitive Assessment (MoCA). Eye movement analysis with HMM revealed two different strategies, namely “analytic” and “holistic”. Participants using the analytic strategy had better recognition performance (particularly in the false alarm rate) than those using the holistic strategy. Significantly more young adults adopted the analytic strategy; whereas more older adults adopted the holistic strategy. Interestingly, older adults with lower cognitive status were associated with higher likelihood of using the holistic strategy. These results suggest an association between holistic eye movement patterns and cognitive decline in the elderly.

Recent research has revealed two different eye movement pat-terns during face recognition: holistic and analytic. The pre-sent study investigated the neural correlates of these two pat-terns through functional magnetic resonance imaging (fMRI).A more holistic pattern was associated with more activation inthe face-selective perceptual areas, including the occipitalface area and fusiform face area. In contrast, participants us-ing a more analytic pattern demonstrated more activation inareas important for top-down control of visual attention, in-cluding the frontal eye field and intraparietal sulcus. In addi-tion, participants using the analytic patterns had better recog-nition performance than those showing holistic patterns. The-se results suggest that analytic eye movement patterns are as-sociated with more engagement of top-down control of visualattention, which may consequently enhance recognition per-formance.