It is well known that there exist preferred landing positions for eye fixations in visual word recognition. However, the existence of preferred landing positions in face recognition is less well established. It is also unknown how many fixations are required to recognize a face. To investigate these questions, we recorded eye movements during face recognition. During an otherwise standard face-recognition task, subjects were allowed a variable number of fixations before the stimulus was masked. We found that optimal recognition performance is achieved with two fixations; performance does not improve with additional fixations. The distribution of the first fixation is just to the left of the center of the nose, and that of the second fixation is around the center of the nose. Thus, these appear to be the preferred landing positions for face recognition. Furthermore, the fixations made during face learning differ in location from those made during face recognition and are also more variable in duration; this suggests that different strategies are used for face learning and face recognition.

We use the logographic characteristic of Chinese orthography to examine whether face-specific effects, such as holistic processing and the left side bias effect, can also be observed in expertise-level Chinese character processing by comparing novices' and experts' perception of Chinese characters. We show that non-Chinese readers (novices) perceive characters more holistically than Chinese readers (experts). Chinese readers have a better awareness of the components of characters, which are not clearly separable to novices. This suggests that holistic processing is not a general visual expertise marker; it depends on the features of the stimuli and the tasks typically performed on them. In contrast, similar to face perception, Chinese readers have a left side bias effect in the perception of mirror-symmetric characters, whereas novices do not; this effect is also reflected in their eye fixation behavior. This suggests that the left side bias effect may be a visual expertise marker.3

We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone.

How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, can be used to infer observer-related (e.g., task at hand) and stimuli-related (e.g., image semantic category) information. However, eye movements are complex signals and many of these studies rely on limited gaze descriptors and bespoke datasets. Here, we provide a turnkey method for scanpath modeling and classification. This method relies on variational hidden Markov models (HMMs) and discriminant analysis (DA). HMMs encapsulate the dynamic and individualistic dimensions of gaze behavior, allowing DA to capture systematic patterns diagnostic of a given class of observers and/or stimuli. We test our approach on two very different datasets. Firstly, we use fixations recorded while viewing 800 static natural scene images, and infer an observer-related characteristic: the task at hand. We achieve an average of 55.9% correct classification rate (chance = 33%). We show that correct classification rates positively correlate with the number of salient regions present in the stimuli. Secondly, we use eye positions recorded while viewing 15 conversational videos, and infer a stimulus-related characteristic: the presence or absence of original soundtrack. We achieve an average 81.2% correct classification rate (chance = 50%). HMMs allow to integrate bottom-up, top-down, and oculomotor influences into a single model of gaze behavior. This synergistic approach between behavior and machine learning will open new avenues for simple quantification of gazing behavior. We release SMAC with HMM, a Matlab toolbox freely available to the community under an open-source license agreement.

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.