Evaluation of 2D combination of eye-tracking metrics for task distinction

Abstract

Eye-tracking techniques enable researchers to observe human behaviors by using eye tracking metrics. Machine learning is one of the techniques used in task inference. However, in our research in order to decrease the effort to analyze the task inference, we consider two combinations of different metrics on a two-dimensional scatter plot. Also, we analyze the data with K-Means clustering and correlation analysis to determine the task inference. Two-dimensional scatter plot let the analyst interact with the data in a better manner. In this thesis, we reduced the metrics dimensions, for example, calculating the mean value of the fixation durations that gave us a single value. We examined a few metrics such as crossings of saccades, first fixation duration after the onset of a stimulus, fixation duration mean, and fixation duration median. Furthermore, we created some custom metrics specifically for this research to analyze the tasks for the participants better. Next, we developed a simple game. In the game, there were three game modes for building distinctive gaze behavior. Those game modes include changes in the color tint information, size changes of the stimulus, and as a control mode, a text-only representation which does not contain any color or size differences. Finally, we made a study with six participants. They played our game to give us a dataset which we can work in the analysis with K-means clustering. Nevertheless, the results were promising and helpful in distinguishing human behavior on different tasks. However, this research is not enough for task inference, and there are further improvements which could achieve a better result than the current state.