Enhancing Cognitive Load Measurement and User Performance in Human-Computer Interaction

Publication Year:
2016
Usage 66
Abstract Views 50
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Repository URL:
https://scholarworks.umb.edu/doctoral_dissertations/292
Author(s):
Attar, Nada
Tags:
Cognitive Load; Eye-tracking; Human-Computer Interaction; Computer Sciences
thesis / dissertation description
Enhancing user performance is a constant goal for human computer interaction (HCI) researchers. To achieve this, we need to have an accurate measurement of a user's performance that reflects moment-to-moment of the user's cognitive state. By measuring users' cognitive state during a specific task, such as visual search, reading, or counting, we can improve a given interface based on this implicit data stream. The measurement of cognitive load per se can be improved using psychophysical and machine learning methods. In this dissertation, I study how both this measurement and the users' task performance can be enhanced using various methods, with a focus on eye movement data as an indicator of cognitive state. I used a safe, non-invasive eye tracking technology that can collect pupil dilation and eye fixation as indicators of cognitive state during certain searching and reading tasks. To enhance users' performance, I discuss the applicability of using two main interaction areas in HCI: interaction techniques using auditory feedback during search tasks, and the contextual design of reading interfaces. I examined the users' performance using cognitive load measurement and recommended the appropriate interface that can optimize performance. To obtain better measurement of cognitive load as an indicator of a user's task performance, I ran a series of controlled experiments. I studied the automatic recognition of the level of cognitive load in different reading and visual search tasks. I improved the measurement of cognitive load during a search task, focusing on filtering the eye data either by using machine learning classification methods or statistical analysis. Also, I was able to predict the user's efficiency by measuring eye movement data at the early stage of the task using a novel interface during two different search tasks. For the reading tasks, I presented an interface that improved speed reading by guiding gaze fixations toward the middle of a word and measured the correlation between pupil size and comprehension. The results were significant in measuring cognitive processing and can be used to determine the users' comprehension level.