Year-end Sale % OFF 
Abstract
Today, eye trackers are extensively used in studying human cognition. However, it is hard to analyze and interpret eye movement data from the cognitive comprehension perspective of poster reading. To find quantitative links between eye movements and cognitive comprehension, we tracked observers’ eye movement for reading scientific poster publications. We model in this paper eye tracking fixation sequences between content-dependent Areas of Interests (AOIs) as a Markov chain. Furthermore, we use the fact that a Markov chain is a special case of information or communication channel. Then, the gaze transition can be modeled as a discrete information channel, the gaze information channel. Next, some traditional eye tracking metrics, together with the gaze entropy and mutual information of the gaze information channel are calculated to quantify cognitive comprehension for every participant. The analysis of the results demonstrate that the gaze entropy and mutual information from individual gaze information channel are related to participants’ individual differences. This is the first study that eye tracking technology has been used to assess the cognitive comprehension of poster reading. The present work provides insights into human cognitive comprehension by using the novel gaze information channel methodology.
Highlights
IntroductionThe eye is an important organ of the human being
As we all know, the eye is an important organ of the human being
For the sake of space, we focus on only two participants and three posters based on the different number of Areas of Interests (AOIs) to show the analysis of the results
Summary
The eye is an important organ of the human being. As an important psychological experiment research method, eye movement provides a new perspective and way for educational technology research [17,18,19]. Ponsoda et al [37] introduced probability vectors and transition matrices by classifying the directions of saccade. Their matrices were based on transition between the eight main saccade directions rather than between the Areas of Interests (AOIs), which are more commonly used. Ponsoda et al compared the matrices with a statistical method, they did not model the sequence of saccade directions as a Markov chain. Its value provided a measure of the statistical dependency in the spatial pattern of fixations represented by the transition matrix
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
