Dyslexia Prediction from Natural Reading of D anish Texts (2023)

Abstract: Dyslexia screening in adults is an open challenge since difficulties may not align with standardised tests designed for children. We collect eye-tracking data from natural reading of Danish texts from readers with dyslexia while closely following the experimental design of a corpus of readers without dyslexia. Research suggests that the opaque orthography of the Danish language affects the diagnostic characteristics of dyslexia. To the best of our knowledge, this is the first attempt to classify dyslexia from eye movements during reading in Danish. We experiment with various machine-learning methods, and our best model yields 0.85 F1 score.

## introduction dyslexia is a learning disorder of neurological origin that reportedly affects about 10-20% of the world population @xcite . it involves difficulties with reading, spelling, and decoding words, and is not related to intelligence @xcite . detecting dyslexia as early as possible is vital, as the disorder can lead to many negative consequences that can be mitigated with proper assistance. these include low self-esteem and high rates of depression and anxiety @xcite . there are qualitative studies suggesting that living with an undiagnosed learning disorder leads to frustrations @xcite , feelings of being misunderstood @xcite , and of failure, @xcite . being diagnosed with a learning disorder as an adult has been reported to lead to a sense of relief @xcite , validation @xcite and liberation @xcite . dyslexia can be difficult to diagnose due to its indications and impairments occurring in varying degrees @xcite , and is therefore often recognised as a hidden disability @xcite . popular methods of detecting dyslexia usually include standardised lexical assessment tests that involve behavioural aspects, such as reading and spelling tasks @xcite . @xcite explain that computerised screening methods have been wellestablished for children in the uk, but developing such tests for adult readers with dyslexia is exceptionally challenging as adults with dyslexia may not show obvious literacy difficulties that align with what standardised tests distinguish as dyslexic tendencies. for one thing, dyslexia is experienced differently from person to person. still, also, most adults with dyslexia have developed strategies that help them disguise weaknesses and may thus remain unnoticed and result in falsenegative tests @xcite . less frequently used methods are eye tracking during reading or neuroimaging techniques such as (functional) magnetic resonance imaging, electroencephalogram, brain positron emission tomography, and magnetoencephalography methods @xcite . these models are yet under experimental development and are currently not used for screening dyslexia @xcite . a small body of studies investigates dyslexia detection using eye tracking with the help of machine-learning techniques outlined in §2.4. compared to neuroimaging techniques, eye tracking is more affordable and faster to record and its link to online text processing is well established @xcite . using eye-tracking records for dyslexia detection does not necessarily require readers to respond or perform a test but merely objectively observes the reader during natural reading @xcite . although eye-tracking experiments are often limited to a relatively small number of participants compared to computerized tools, the method typically produces many data points from each participant. the purpose of the current paper is twofold: this dataset uses the same experimental design as the copco corpus by hollenstein et al. (2022), which allows us to compare the eye movement patterns from readers with dyslexia to those without from copco. 2) we train the first machine learning (ml) classifiers for dyslexia prediction from eye movements in danish. the data is available as raw gaze recording, fixation-level information, and word-level eye tracking features. foot_0 the code for all our experiments is also available online. foot_1 2 related work 2.1 dyslexia screening in denmark in 2015, the ministry of children and education in denmark launched a national electronic dyslexia test, ordblindetesten 'the dyslexia test'. the test is a screening method for children, youths, and adults speculated to have dyslexia. it is accessible through educational institutions and is performed under the observation of a supervisor (centre for reading research et al., 2020). it consists of three multiple-choice subtests, performed electronically, that focus on phonological decoding abilities. the result is 'not dyslexic,' 'uncertain phonological decoding,' or 'dyslexic.' the official instruction strictly denies the uncertain group to be dyslexic foot_2 and therefore not entitled to dyslexia support. but they may benefit from other support and are subject to further assessment, e.g., text comprehension, reading speed, spelling, and vocabulary tests appropriate for the examinee's age and educational requirements (centre for reading research et al., 2020). to this end, helleruptesten "the hellerup test" is used by educational institutions for adults. foot_3 ## data collection data acquisition follows @xcite , but the most important points are repeated here. the only procedural difference is the additional two reading tests administered to participants with dyslexia as described in §3.3. ## dyslexia classification we experiment with three types of classifiers using features on two different levels of aggregation; sentence-level and trial-level. a trial corresponds to the text presented on a single screen, roughly corresponding to paragraphs from the original text materials. for both levels of aggregation, the eyetracking features of each word in a sentence or trial, respectively, are averaged to get a single vector of eight features for each sample. further, we experiment with adding standard deviations (+std) and max values (+max). therefore, we svm and random forest classifiers the eyetracking features are normalised with a min-max scaler that gives each instance a number between 0 and 1.we use a grid search to tune the hyperparameters of both svm (the best regularization parameter @xmath0, and the optimal number of es-@xmath1. the classifiers are implemented with the scikit-learn library for python. the svm uses a linear kernel. in addition to taking the mean feature values per word or trial (i.e., aggregating the eye-tracking features of all individual words), we also experiment with adding the standard deviations and maximum values of each feature. ## discussion & conclusion we presented a dataset of eye-tracking recordings from natural reading from adults with dyslexia, which complements the copco dataset of readers without dyslexia @xcite . additionally, to the best of our knowledge, we presented the first attempt to predict dyslexia from eye-tracking features using danish as a target language. the best-performing classifier of the current study achieves an f1 score of 0.85, using a random forest classifier trained with a feature combination that includes the aggregation of means, standard deviations, and maximum values of eight eye-tracking features. while the recorded eye-tracking features proved to reflect vital information about the reading mechanisms of the participants, there were a considerably high number of misclassifications of fast and skilled readers with dyslexia. this indicates that a fast reading speed is atypical for a reader with dyslexia. these results contribute to findings that the symptoms of dyslexia occur in varying degrees and thus underline the importance of developing a reliable assessment tool for dyslexia that can reduce the number of misclassifications. moreover, due to known comorbidities across reading disorders @xcite that can be reflected in eye movements (e.g., attention and autism spectrum disorders), as the dataset continues to grow, we will include these populations of readers in the data collection to learn to classify different subgroups readers correctly. precise criteria for dyslexia diagnosis remain difficult to standardise with the varying degrees of the symptoms and indicators of the disorder, which is why the condition deserves more attention. as eye-tracking recordings provide insightful information about cognitive processes in naturalistic tasks such as reading, they can be a beneficial tool for dyslexia prediction. eye tracking can be a stepping stone to achieving more reliable screening methods for dyslexia.