To better understand the association between eye movement patterns in visual tasks and individual differences in cognitive style or abilities, we recently developed a state-of-the-art eye movement data analysis method, eye movement analysis with hidden Markov models (EMHMM), which takes individual differences in the temporal and spatial dimensions of eye movements into account (Chuk, Chan, & Hsiao, 2014 an EMHMM Matlab toolbox is available at ).
Sekiguchi ( 2011) found that people with good and with bad face memory performance showed different eye movement patterns during face learning. Wu, Bischof, Anderson, Jakobsen, and Kingstone ( 2014) found that when viewing human faces, those who scored higher on extraversion and agreeableness personality traits looked at the eyes significantly more often than did those who scored lower. For example, Risko, Anderson, Lanthier, and Kingstone ( 2012) found that participants who demonstrated higher levels of curiosity made significantly more fixations in a scene-viewing task than did those who demonstrated lower levels of curiosity.
These idiosyncratic eye movement patterns may reflect individual differences in cognitive style or abilities.
Recent research has shown that people have idiosyncratic eye movement patterns in visual tasks that are consistent across different stimuli and tasks (e.g., Andrews & Coppola, 1999 Castelhano & Henderson, 2008 Kanan, Bseiso, Ray, Hsiao, & Cottrell, 2015 Poynter, Barber, Inman, & Wiggins, 2013). Thus, EMSHMM reveals and provides quantitative measures of individual differences in cognitive behavior/style, making a significant impact on the use of eyetracking to study cognitive behavior across disciplines. As compared with our previous HMM method, which assumes no cognitive state change (i.e., EMHMM), EMSHMM captured eye movement behavior in the task better, resulting in higher decision inference accuracy. This finding was not revealed by any other method.
#HIDDEN MARKOV MODEL MATLAB CODE FORECASTING TRIAL#
One pattern showed both a later transition from the exploration to the preference-biased cognitive state and a stronger tendency to look at the preferred stimulus at the end, and was associated with higher decision inference accuracy at the end the other pattern entered the preference-biased cognitive state earlier, leading to earlier above-chance inference accuracy in a trial but lower inference accuracy at the end. We applied EMSHMM to a face preference decision-making task with two pre-assumed cognitive states-exploration and preference-biased periods-and we discovered two common eye movement patterns through clustering the cognitive state transitions. We used a switching hidden Markov model (SHMM) to capture a participant’s cognitive state transitions during the task, with eye movement patterns during each cognitive state being summarized using a regular HMM. Here we propose the eye movement analysis with switching hidden Markov model (EMSHMM) approach to analyzing eye movement data in cognitive tasks involving cognitive state changes.