Detecting readers with dyslexia using machine learning with eye tracking measures

Abstract

Worldwide, around 10% of the population has dyslexia, a specific learning disorder. Most of previous eye tracking ex periments with people with and without dyslexia have found differences between populations suggesting that eye move ments reflect the difficulties of individuals with dyslexia. In this paper, we present the first statistical model to predict readers with and without dyslexia using eye tracking mea sures. The model is trained and evaluated in a 10-fold cross experiment with a dataset composed of 1,135 readings of people with and without dyslexia that were recorded with an eye tracker. Our model, based on a Support Vector Ma chine binary classifier, reaches 80.18% accuracy using the most informative features. To the best of our knowledge, this is the first time that eye tracking measures are used to predict automatically readers with dyslexia using machine learning.

Is visual content in textual search interfaces beneficial to dyslexic users?

“…Huettig and Brouwer (2015) applied eye tracking to investigate whether auditory language processing related to word decoding abilities. Rello and Ballesteros (2015) used eye tracking to test a statistical model to predict whether a person is dyslexic or not, based on eye movement measures, with the aim of automatically detecting dyslexia. They could predict dyslexic readers with an accuracy of 80.18%, and concluded that eye tracking has potential in developing more efficient diagnostic tools.…”

A plug-in to aid online reading in Spanish

“…In future work we plan to make the plug-in faster, add a module that simplifies numerical expressions [27], include new languages to CASSA plug-in, and be able to activate the plug-in automatically by detecting users with reading disorders [26].…”

Assessing Language Proficiency from Eye Movements in Reading

“…Our framework builds on previous research in psycholinguistics demonstrating that the eyetracking record reflects how readers interact with the text and how language processing unfolds over time (Frazier and Rayner, 1982;Rayner, 1998;Rayner et al, 2012). In particular, it has been shown that key aspects of the reader's characteristics and cognitive state, such as mind wandering during reading (Reichle et al, 2010), dyslexia (Rello and Ballesteros, 2015) and native language (Berzak et al, 2017) can be inferred from their gaze record. Despite these advances, the potential of the rich and highly informative behavioral signal obtainable from human reading for automated inference about readers, and specifically about their linguistic proficiency has thus far been largely unutilized.…”