Adolescent weak decoders writing in a shallow orthography: process and product

It has been hypothesised that students with dyslexia struggle with writing because of a word level focus that reduces attention to higher level textual features (structure, theme development). This may result from difficulties with spelling and/or from difficulties with reading. 26 Norwegian upper secondary students (M = 16.9 years) with weak decoding skills and 26 age-matched controls composed expository texts by keyboard under two conditions: normally and with letters masked to prevent them reading what they were writing. Weak decoders made more spelling errors and produced poorer quality text. Their inter key-press latencies were substantially longer pre-word, at word-end, and within-word. These findings provide some support for the word-level focus hypothesis, although we found that weak decoders were slightly less likely to engage in word-level editing. Masking did not affect differences between weak decoders and controls indicating that reduced fluency was associated with production rather than monitoring what they had produced

Ultrasound evaluation in combination with finger extension force measurements of the forearm musculus extensor digitorum communis in healthy subjects

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to evaluate the usefulness of an ultrasound-based method of examining extensor muscle architecture, especially the parameters important for force development. This paper presents the combination of two non-invasive methods for studying the extensor muscle architecture using ultrasound simultaneously with finger extension force measurements.</p> <p>Methods</p> <p>M. extensor digitorum communis (EDC) was examined in 40 healthy subjects, 20 women and 20 men, aged 35–73 years. Ultrasound measurements were made in a relaxed position of the hand as well as in full contraction. Muscle cross-sectional area (CSA), pennation angle and contraction patterns were measured with ultrasound, and muscle volume and fascicle length were also estimated. Finger extension force was measured using a newly developed finger force measurement device.</p> <p>Results</p> <p>The following muscle parameters were determined: CSA, circumference, thickness, pennation angles and changes in shape of the muscle CSA. The mean EDC volume in men was 28.3 cm³and in women 16.6 cm³. The mean CSA was 2.54 cm²for men and 1.84 cm²for women. The mean pennation angle for men was 6.5° and for women 5.5°. The mean muscle thickness for men was 1.2 cm and for women 0.76 cm. The mean fascicle length for men was 7.3 cm and for women 5.0 cm. Significant differences were found between men and women regarding EDC volume (p < 0.001), CSA (p < 0.001), pennation angle (p < 0.05), muscle thickness (p < 0.001), fascicle length (p < 0.001) and finger force (p < 0.001). Changes in the shape of muscle architecture during contraction were more pronounced in men than women (p < 0.01). The mean finger extension force for men was 96.7 N and for women 39.6 N. Muscle parameters related to the extension force differed between men and women. For men the muscle volume and muscle CSA were related to extension force, while for women muscle thickness was related to the extension force.</p> <p>Conclusion</p> <p>Ultrasound is a useful tool for studying muscle architectures in EDC. Muscle parameters of importance for force development were identified. Knowledge concerning the correlation between muscle dynamics and force is of importance for the development of new hand training programmes and rehabilitation after surgery.</p

Reading Development and Reading Disability : Analyses of eye-movements and word recognition

The primary ambition of this doctoral thesis is to provide an empirical basis for a better understanding of reading disability among school children. The current consensus in the research community is that most disabled readers fail in the acquisition of fast, accurate and automatic word recognition. The thesis includes a number of studies where normal and disabled readers are compared on various aspects of visual word recognition and related processes. The participants, about 100 disabled readers and 90 normal readers, were selected from a full cohort of 2nd graders (N = 2165) in the county of Kronoberg, Sweden, and were carefully matched on gender, non-verbal intelligence and school class. Extensive assessments of reading and spelling, cognitive ability, vision, eye movements, motor skill and socio-economic background were carried out as well as teachers’ ratings of school behaviour. First, basic visual functions, including eye movements, were examined. Only minor differences between the groups were observed, such as a slightly lower contrast sensitivity and poorer control of binocular saccades among the reading disabled children. Some of the small differences in visual functions could be interpreted as reflecting differences in attention mechanisms. A new group test for assessing the growth of word recognition skill was developed; the Wordchains test. The course of development of word reading skill was examined in a cross-sectional study covering the whole period of compulsory schooling. A modified quadratic function yielded the best fit to the data, and the asymptote level was reached around the age of 18. Girls outperformed boys at all stages. A longitudinal study gave support for a deficit model of reading disability. In summary, 3 out of 5 of the children initially classified as reading disabled were stable poor readers seven years later. Only a minority (18%) followed a lag model, reaching a normal level by grade 9. For the reading disabled boys a growth curve analysis demonstrated that about 25% of the variance in slope (average rate of individual growth) could be explained by factors related to early reading skill and non-verbal intelligence. A wide range of neuropsychological and social variables failed to predict individual development in word reading. For the control group none of the independent variables explained the slope variance. Methodological issues involved in the growth study were analysed and discussed

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

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

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

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