Abstract
With the development of modern technology and social media, notifications from our devices make concentrating on our goals of working and learning more and more difficult. It is thus crucial to identify mind-wandering, the phenomenon that people sometimes think about task-unrelated things that can cause negative effects on learning and working efficiency. Since eye movements have been shown to be highly correlated with attention, the present study aimed to examine whether eye movement patterns can be used to categorize people who are more prone to mind-wandering. Participants performed the sustained attention to response task (SART) while eye movement data were recorded. The SART comprised of 25 trials (with one target as the no-go trial and 24 go trials) per block, and there was a total of 40 blocks. At the end of each block, participants were probed to subjectively rate their state of attention on a 7-point scale. By applying the eye movement hidden Markov model (EMHMM) to analyze eye movement data, we classified participants into two different eye movement patterns: the centralized-viewing pattern and the distributed-viewing pattern. Results showed that participants using a centralized-viewing pattern showed better task performances (higher d’) compared to those using a distributed-viewing pattern to the target (the no-go trials). We also found that people who had a centralized-viewing pattern tended to rate themselves as more focused and had smaller reaction-time variability and fixation dispersion before the subjective-rating probe. These results suggest that people’s mind-wandering traits can be differentiated by specific eye movement patterns. The current study highlights the connection between eye movements and attention, and also provides new insight in utilizing EMHMM to study attention. Our data is stored in the MM-SART database and can be accessed through the provided link (http://mmsart.ee.ntu.edu.tw/NTU_SART/download.html).