Predicting Reading Speed from Eye-Movement Measures.

Examining eye-movement measures makes understanding the intricacies of reading processes possible. Previous studies have identified some eye-movement measures such as fixation time, number of progressive and regressive saccades as possible major indices for measuring silent reading speed, however, not quite intensively and systematically investigated. The purpose of this study was to exhaustively reveal the functions of different global eye-movement measures and their contribution to reading speed using linear regression analysis. Twenty-four young adults underwent an eye-tracking experiment while reading text paragraphs. Reading speed and a set of twenty-three eye-movement measures including properties of saccades, glissades and fixations were estimated. Correlation analysis indicated multicollinearity between several eye-movement measures, and accordingly, linear regression with elastic net regularization was used to model reading speed with eye-movement explanatory variables. Regression analyses revealed the capability of progressive saccade frequency and the number of progressive saccades normalized by the number of words in predicting reading speed. Furthermore, the results supported claims in the existing literature that reading speed depends on fixation duration, as well as the amplitude, number and percentage of progressive saccades, and also indicated the potential importance of glissade measures in deeper understanding of reading processes. Our findings indicate the possibility of the applied linear regression modeling approach to eventually identify important eye-movement measures related to different reading performance metrics, which could potentially improve the assessment of reading abilities.