Understanding Dyslexia Through Personalized Large-Scale Computational Models

Abstract

Learning to read is foundational for literacy development, yet many children in primary school fail to become efficient readers despite normal intelligence and schooling. This condition, referred to as developmental dyslexia, has been hypothesized to occur because of deficits in vision, attention, auditory and temporal processes, and phonology and language. Here, we used a developmentally plausible computational model of reading acquisition to investigate how the core deficits of dyslexia determined individual learning outcomes for 622 children (388 with dyslexia). We found that individual learning trajectories could be simulated on the basis of three component skills related to orthography, phonology, and vocabulary. In contrast, single-deficit models captured the means but not the distribution of reading scores, and a model with noise added to all representations could not even capture the means. These results show that heterogeneity and individual differences in dyslexia profiles can be simulated only with a personalized computational model that allows for multiple deficits.

Keywords: computer simulation; dyslexia; reading.

Fig. 1.

Schematics illustrating how a developmentally plausible computational model of reading development can be used to predict learning outcomes. After initial explicit teaching on a small set of grapheme–phoneme correspondences (GPCs), the decoding network (a) is able to decode words that have a preexisting representation in the phonological lexicon but no orthographic representation. If the decoding mechanism activates a word in the phonological lexicon, an orthographic entry is created, and the phonology is used as an internally generated teaching signal (red arrows) to refine and strengthen letter–sound connections, thereby improving the efficiency of the decoding network. In the individual-deficit simulation approach (b), the efficiency of various components of the reading network can be estimated individually for each child (N = 622) through performance on component tasks that map directly onto model components. The performance of each child in the three component tasks is used to individually set the parameters of the model in order to predict individual learning outcomes.

Fig. 2.

Predicted versus actual reading performance. The bar graphs (a) show the proportion of correct responses for regular words, irregular words, and nonwords by the multideficit model (MDM) and humans, separately for all children (N = 622), children with dyslexia (n = 388), and normally developing children (controls; n = 234). Error bars show 95% confidence intervals. The scatterplots (b) show the relationship between predicted and actual individual reading scores for regular words, irregular words, and nonwords for all children.

Fig. 3.

The use of decoding versus direct instruction as a function of reading skill. Simulations show (a) the proportion of words that were learned via self-generated decoding and via direct instruction and (b) the number of direct instruction attempts. Both are plotted as a function of the average reading performance of each child. Colored lines represent the individual data, and the black overlaid lines are the results in deciles. The proportions of words in (a) do not add up to 1.0 because they refer to a full-size phonological lexicon, which includes words that were not learned by either decoding or direct instruction for most of the simulated individuals.

Fig. 4.

Reading performance for regular words, irregular words, and nonwords for all children, dyslexic children, and control children, compared with performance of the multideficit model (MDM), the global-noise model (noise), the phonological-deficit model (phon), and the visual-deficit model (visual). Error bars show 95% confidence intervals.

Fig. 5.

Predicted mean dyslexic reading performance (bar graphs) and the association between predicted and actual reading performance of individual dyslexics (scatterplots) of the multideficit, global-noise, phonological-deficit, and visual-deficit models. A Bayesian information criterion (BIC) difference of 10 corresponds to a posterior odds of about 150:1 (Raftery, 1995), and a larger negative value is an index of better fit. Error bars show 95% confidence intervals.

Fig. 6.

Predicting learning outcomes as a function of improvements in orthography, phonology, and vocabulary. The scores of each child were normalized to start at 0, and the component scores were increased by 0.2 of a z score until they were at their maximum. Thus, the start of a line represents a child’s initial state, and the end of a line represents how a child was predicted to perform when a single component score was increased as much as possible. Thus, the length of the line represents the potential gain (in z scores) for a given child.