Investigating the Effect of Inter-letter Spacing Modulation on Data-Driven Detection of Developmental Dyslexia Based on Eye-Movement Correlates of Reading: A Machine Learning Approach

Abstract

Developmental dyslexia is a reading disability estimated to affect between 5 to 10% of the population. However, current screening methods are limited as they tell very little about the oculomotor processes underlying natural reading. Accordingly, investigating the eye-movement correlates of reading in a machine learning framework could potentially enhance the detection of poor readers. Here, the capability of eye-movement measures in classifying dyslexic and control young adults (24 dyslexic, 24 control) was assessed on eye-tracking data acquired during reading of isolated sentences presented at five inter-letter spacing levels. The set of 65 eye-movement features included properties of fixations, saccades and glissades. Classification accuracy and importance of features were assessed for all spacing levels by aggregating the results of five feature selection methods. Highest classification accuracy (73.25%) was achieved for an increased spacing level, while the worst classification performance (63%) was obtained for the minimal spacing condition. However, the classification performance did not differ significantly between these two spacing levels (p = 0.28). The most important features contributing to the best classification performance across the spacing levels were as follows: median of progressive and all saccade amplitudes, median of fixation duration and interquartile range of forward glissade duration. Selection frequency was even for the median of fixation duration, while the median amplitude of all and forward saccades measures exhibited complementary distributions across the spacing levels. The results suggest that although the importance of features may vary with the size of inter-letter spacing, the classification performance remains invariant.