Inference for the Occurrence of Difficulty in Character Identification on Browsing Websites from Gaze Data

Abstract

This study devises a Bayesian network model to infer the occurrence of difficulty in character identification when browsing a website. Subjects are classified into two groups: one is a group of people with fast eye movement and the other is that of people with slow eye movement. It is shown that, in either of groups, the average fixation duration when feeling difficulty in character identification is significantly longer than when feeling difficulty in sentence comprehension. Browsing properties of subjects in both groups are analyzed by inputting time series data consisting of eye movement values in two previous periods into the inference model. It turns out that in both groups, the occurrence probability of the difficulty in character identification tends to increase when subjects move their eyes rapidly to the character from a distant position or move their eyes closer to the character little by little.