Understanding eye movements in face recognition with hidden Markov model

Fulltext in: http://mindmodeling.org/cogsci2013/papers/0085/paper0085.pdfIn this paper we propose a hidden Markov model (HMM)-based method to analyze eye movement data. We conducted a simple face recognition task and recorded eye movements and performance of the participants. We used a variational Bayesian framework for Gaussian mixture models to estimate the distribution of fixation locations and modelled the fixation and transition data using HMMs. We showed that using HMMs, we can describe individuals’ eye movement strategies with both fixation locations and transition probabilities. By clustering these HMMs, we found that the strategies can be categorized into two subgroups; one was more holistic and the other was more analytical. Furthermore, we found that correct and wrong recognitions were associated with distinctive eye movement strategies. The difference between these strategies lied in their transition probabilities

Objective Measures of IS Usage Behavior Under Conditions of Experience and Pressure Using Eye Fixation Data

The core objective of this study is to understand individuals IS usage by going beyond the traditional subjective self-reported and objective system-log measures to unveil the delicate process through which users interact with IS. In this study, we conducted a laboratory experiment to capture users’ eye movement and, more importantly, applied a novel methodology that uses the Gaussian mixture model (GMM) to analyze the gathered physiological data. We also examine how performance pressure and prior usage experience of the investigative system affect IS usage patterns. Our results suggest that experienced and pressured users demonstrate more efficient and focused usage patterns than inexperienced and non-pressured ones, respectively. Our findings constitute an important advancement in the IS use literature. The proposed statistical approach for analyzing eye-movement data is a critical methodological contribution to the emerging research that uses eye-tracking technology for investigation

ANALYSIS OF GAZE TRANSITION PATTERNS BY HIDDEN MARKOV MODEL IN COGNITIVE PROCESS OF IMPRESSION EVALUATION OF PAINTINGS

We propose a hidden Markov model (HMM) based method to categorize eye movement pattern in terms of machine learning technique, which was applied to the cognitive process of impression evaluation of paintings. I n our experiment, we tracked the gaze transition pattern of subjects while they evaluated their personal impressions of traditional Japanese Ukiyo-e paintings. For each class of positive/negative impressions, we estimated the HMM parameters from the training samples of eye movements. For the eye movement data of the test samples, we conducted a classification test based on the differences of the log likelihood values obtained from each HMM. For the cases of receiving negative impression, we identified a common pattern of eye movement from a broader perspective, but no such holistic patterns of eye movement were found for the cases of receiving positive impression