Proceedings of the Annual Meeting of the Cognitive Science Society

Here we used a hidden Markov model (HMM) based ap- proach to infer individual choices from eye movements in preference decision-making. We assumed that during a deci- sion making process, participants may switch between explo- ration and decision-making periods, and this behavior can be better captured with a Switching HMM (SHMM). Through clustering individual eye movement patterns described in SHMMs, we automatically discovered two groups of partici- pants with different decision making behavior. One group showed a strong and early bias to look more often at the to-be chosen stimulus (i.e., the gaze cascade effect; Shimojo et al., 2003) with a short final decision-making period. The other group showed a weaker cascade effect with a longer final de- cision-making period. The SHMMs also showed capable of inferring participants’ preference choice on each trial with high accuracy. Thus, our SHMM approach made it possible to reveal individual differences in decision making and discover individual preferences from eye movement data.