Predicting character discrimination difficulties using gaze fixation while browsing the web using reinforcement learning

Abstract

This study develops a system to predict, with high precision and in real-time, the occurrence of difficulty in character identification during web browsing, based on gaze data. Specifically, the system leverages fixation duration, which evolves incrementally, and employs a reinforcement learning algorithm based on SARSA, to evaluate the occurrence of the difficulty at each step. Since fixation durations caused by character identification difficulty are not necessarily longer than those resulting from other factors, establishing a reliable threshold for character magnification is difficult. Nevertheless, the system must refrain from magnifying characters when users do not feel them difficult to identify. Therefore, this study introduces saccadic velocity and amplitude as two external parameters, categorizes them into distinct groups, and calculates the Q-value for each category pair, thereby enabling a precise determination of magnification thresholds.