The Hidden Markov Modeling approach for eye-movement data analysis is able to quantitatively assess differences and similarities among individual patterns. Here we applied this approach to examine the relationships between eye-movement patterns in face recognition and age-related cognitive decline. We found that significantly more older than young adults adopted "holistic" patterns, in which most eye fixations landed around the face center, as opposed to "analytic" patterns, in which eye movements switched among the two eyes and the face center. Participants showing analytic patterns had better performance than those with holistic patterns regardless of age. Interestingly, older adults with lower cognitive status (as assessed by the Montreal Cognitive Assessment), particularly in executive and visual attention functioning (as assessed by Tower of London and Trail Making Tests) were associated with a higher likelihood of holistic patterns. This result suggests the possibility of using eye movements as an easily deployable screening assessment for cognitive decline in older adults.

The well-known other-race effect in face recognition has been widely studied, both for its theoretical insights into the nature of face expertise and because of its social and forensic importance. Here we demonstrate an other-race effect for the perception of a simple visual signal provided by the eyes, namely gaze direction. In Study 1, Caucasian and Asian participants living in Australia both showed greater perceptual sensitivity to detect direct gaze in own-race than other-race faces. In Study 2, Asian (Chinese) participants living in Australia and Asian (Chinese) participants living in Hong Kong both showed this other-race effect, but Caucasian participants did not. Despite this inconsistency, meta-analysis revealed a significant other-race effect when results for all 5 participant groups from corresponding conditions in the 2 studies were combined. These results demonstrate a new other-race effect for the perception of the simple, but socially potent, cue of direct gaze. When identical morphed-race eyes were inserted into the faces, removing race-specific eye cues, no other-race effect was found (with 1 exception). Thus, the balance of evidence implicated perceptual expertise, rather than social motivation, in the other-race effect for detecting direct gaze. (PsycINFO Database Record

Recent research using Eye Movement analysis with Hidden Markov Models (EMHMM) has reported that in face recognition, participants who look more at the eyes in addition to the face center (the analytic pattern) have better recognition performance than those looking mainly at the face center (the holistic pattern). Here we examined the mechanisms underlying the advantage of the analytic pattern through an Event-Related Potential (ERP) examination with simultaneous recording of EEG and eye movement. Using EMHMM, we identified nose-focused and eyes-focused eye movement patterns from participants. As compared with participants using nose-focused patterns, those with eyes-focused patterns showed increased N170 amplitude, reduced P2 amplitude, increased N250 amplitude, and prolonged P300 latency, suggesting that they may have engaged more in local face processing, perceived faces as less typical, developed richer perceptual memory representations, and engaged more in stimulus evaluation processes. Importantly, optimal face recognition performance was associated with a mixture of nose- and eyes-focused eye movement patterns and an intermediate level of P300 latency. Also, among all behavioral and ERP measures, eye movement pattern uniquely predicted participants’ recognition performance. Further regression analysis showed that eye movement pattern could be predicted by a combination of Tower of London task performance and ERP P2 amplitude. These results suggest that the optimal face recognition performance involves a balance between global and local face processing, and eye movement pattern is a multifaceted measure that reflects individual differences in both executive function and quality of perceptual representation, making it a unique predictor for recognition performance.

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