Eye movements during reading and their relationship to reading assessment outcomes in Swedish elementary school children

The characteristics of children’s eye movements during reading change as they gradually become better readers. However, few eye tracking studies have investigated children’s reading and reading development and little is known about the relationship between reading-related eye movement measures and reading assessment outcomes. We recorded and analyzed three basic eye movement measures in an ecologically valid eye-tracking set-up. The participants were Swedish children (n = 2876) who were recorded in their normal school environment. The relationship between eye movements and reading assessment outcomes was analyzed in using linear mixed effects models. We found similar age-related changes in eye movement characteristics as established in previous studies, and that eye movements seem to correlate with reading outcome measures. Additionally, our results show that eye movements predict the results on several tests from a word reading assess- ment. Hence eye tracking may potentially be a useful tool in assessing reading development

Recording Data

Eye movement recording

Screening for Dyslexia Using Eye Tracking during Reading

<div><p>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.</p></div

Experimental test protocol based on repeated cross-validation with internal feature selection.

<p>The entire dataset is randomly divided into 10 subsets, setting aside one subset (10% of all subjects) as a test sample and the remaining nine subsets (90% of all subjects) as a training sample. A feature selection algorithm is applied on the training sample to select a subset of <i>n</i> features. Using this feature subset, a classification algorithm is applied on the training sample, producing a parametrized classifier as output. This classifier is then used to classify the subjects in the test sample and the predicted results are compared to the actual identity (HR or LR) of the test subjects. This step is iterated 10 times, with a different training and test set for each iteration. After one completed run of 10-fold cross validation, each subject in the entire dataset has been tested exactly once, while we still have maintained a strict separation between training and test subjects. To reduce the variance of the cross-validated performance estimate, the whole process is repeated 100 times with different initial random splits of the original dataset. The final estimate of the expected predictive performance is calculated by averaging the cross-validation performance over all 100 repetitions. This estimate represents the expected prediction accuracy of the final model. The final model–the one we would deploy in practice–is the classifier we would build from the entire dataset using feature selection method <i>m</i> to select <i>n</i> features.</p

Prediction accuracy as a function of the numbers of features selected during training.

<p>Accuracy is shown for classifiers based on recursive feature elimination (solid blue line), random feature selection (dashed red line), and chance (dotted green line). Chance-level accuracy is based on Y-randomization of training data. Accuracy is the percentage of correctly identified HR and LR subjects averaged over 100 × 10-fold cross-validation. Maximum accuracy, 95.6%, (± 4.5%), is obtained using recursive feature elimination to select 48 features from the original feature set of 168 features. Shaded regions indicate mean ± 1 standard deviation over the 100 repetitions. Performance at chance level, averaged over the feature subset sizes, is 49.3%.</p

Frequency of features selected during training of the best performing classification model grouped by progressive/regressive fixation- and saccade features.

<p>The Y-axis shows the number of times a feature in the original feature set was selected by the recursive feature elimination algorithm (SVM-RFE) during the 10 x 100 cross-validation with internal feature selection. The X-axis shows the features (represented by their index in the dataset) grouped by progressive/regressive fixation- and saccade features.</p

Box plots of features selected in 1000 (100%) training folds by the best performing classification model.

<p>The box plots show the distribution of values, normalized to range between 0 and 1, by feature and group HR (<i>n</i> = 97) and LR (<i>n</i> = 88).</p

Example of eye movement analysis where the horizontal (CH) and vertical (CV) eye movement signal is plotted over time.

<p>Light green stripes represent saccades, light gray areas represent fixations. Light blue stripes represent sweeping movements (most commonly return sweeps) and red stripes represent transients. Plot <b>A</b> represents a subject from the HR group and plot <b>B</b> a subject from the LR group. The analysis was performed using a dynamic dispersion threshold algorithm based on the physiological properties of the foveal and parafoveal fields of vision. The algorithm analyzes the tracking signal sample by sample and switches between four mutually exclusive states: distortions, transients, fixations, and saccades. A distortion state is detected if the horizontal or vertical signal is missing for both eyes. A transient state is detected if the horizontal and vertical position is within a threshold distance of 0.5 degrees + signal noise (2.5 × RMS error of the last 25 samples) from the average of the samples in the current state. A fixation state is detected when the eyes have remained stable for at least 50 ms, and a saccade state when the eyes have moved beyond the threshold distance. Once a change of state is detected, the samples of the previous state are identified as a new event.</p