Screening for Dyslexia Using Eye Tracking during Reading

Abstract

Dyslexia is a neurodevelopmental reading disability estimated to affect 5–10% of the population. While there is yet no full understanding of the cause of dyslexia, or agreement on its precise definition, it is certain that many individuals suffer persistent problems in learning to read for no apparent reason. Although it is generally agreed that early intervention is the best form of support for children with dyslexia, there is still a lack of efficient and objective means to help identify those at risk during the early years of school. Here we show that it is possible to identify 9–10 year old individuals at risk of persistent reading difficulties by using eye tracking during reading to probe the processes that underlie reading ability. In contrast to current screening methods, which rely on oral or written tests, eye tracking does not depend on the subject to produce some overt verbal response and thus provides a natural means to objectively assess the reading process as it unfolds in real-time. Our study is based on a sample of 97 high-risk subjects with early identified word decoding difficulties and a control group of 88 low-risk subjects. These subjects were selected from a larger population of 2165 school children attending second grade. Using predictive modeling and statistical resampling techniques, we develop classification models from eye tracking records less than one minute in duration and show that the models are able to differentiate high-risk subjects from low-risk subjects with high accuracy. Although dyslexia is fundamentally a language-based learning disability, our results suggest that eye movements in reading can be highly predictive of individual reading ability and that eye tracking can be an efficient means to identify children at risk of long-term reading difficulties.

Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

“…An alternative paradigm to psycholinguisticsbased feature extraction is to instead represent raw recorded scanpaths over entire word sequences as 2D or 3D matrices and images (von der Malsburg et al, 2012;Martínez-Gómez et al, 2012;Benfatto et al, 2016;Mishra et al, 2017a). However, this paradigm has only been explored in a jointly labeled setting where gaze data is assumed to be available at test time.…”

Screening Dyslexia for English Using HCI Measures and Machine Learning

“…In 2015, the first method to screen dyslexia in Spanish was introduced; it used eye-tracking measures from 97 subjects (48 with dyslexia) [21]. Later in 2016, eye-tracking measures were also used to predict dyslexia for Swedish (185 subjects, 97 of then with high-risk of dyslexia) [2]. Both methods used Support Vector Machines.…”

“…There were 9 participants at risk of having dyslexia or suspected of having dyslexia -Class M (Maybe)-(4 female, 5 male, M = 17.66, SD = 16.17). 2 The first language of all participants was English, although 84 participants spoke another language (mostly Spanish in the Texas area). A total of 224 participants reported having trouble with language classes at school.…”

Dyslexic children need more robust information to resolve conflicting sensory situations

“…Several experts have suggested that dyslexic individuals may have a mildly affected cerebellum, which could impair literacy as well as motor skill performance (Fawcett & Nicolson, 2004;Nicolson, Fawcett, & Dean, 2001;Stoodley & Stein, 2013). For example, dyslexic children spend a longer time to read when compared to nondyslexic children (Benfatto et al, 2016;de Luca, Marinelli, Spinelli, & Zoccolotti, 2017;Parka & Lombardino, 2013), walk significantly slower when asked to walk as fast as possible (Moe-Nilssen, Helbostad, Talcott, & Toennessen, 2003), and exhibit poorer performance when compared to normal reading children in a task involving isometric force production and control guided by visual feedback (de Freitas, Pedao, & Barela, 2014). For example, dyslexic children spend a longer time to read when compared to nondyslexic children (Benfatto et al, 2016;de Luca, Marinelli, Spinelli, & Zoccolotti, 2017;Parka & Lombardino, 2013), walk significantly slower when asked to walk as fast as possible (Moe-Nilssen, Helbostad, Talcott, & Toennessen, 2003), and exhibit poorer performance when compared to normal reading children in a task involving isometric force production and control guided by visual feedback (de Freitas, Pedao, & Barela, 2014).…”

“…The main idea is that the mild cerebellar insult would impair the ability of dyslexic individual to learn how to perform tasks with automaticity, with much less conscious efforts while reading, writing, and performing other tasks such as postural control (i.e., cerebellar hypothesis) (Fawcett & Nicolson, 2004). For example, dyslexic children spend a longer time to read when compared to nondyslexic children (Benfatto et al, 2016;de Luca, Marinelli, Spinelli, & Zoccolotti, 2017;Parka & Lombardino, 2013), walk significantly slower when asked to walk as fast as possible (Moe-Nilssen, Helbostad, Talcott, & Toennessen, 2003), and exhibit poorer performance when compared to normal reading children in a task involving isometric force production and control guided by visual feedback (de Freitas, Pedao, & Barela, 2014). Furthermore, dyslexic children manifest poor performance during a task requiring postural control (Brookes, Tinkler, Nicolson, & Fawcett, 2010;Moe-Nilssen et al, 2003;Patel, Magnusson, Lush, Gomez, & Fransson, 2010;Pozzo et al, 2006;Stoodley, Fawcett, Nicolson, & Stein, 2005;Vieira, Quercia, Michel, Pozzo, & Bonnetblanc, 2009).…”