Understanding Dyslexia Through Personalized Large-Scale Computational Models

International audienceLearning 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

Language acquisition in developmental disorders

In this chapter, I review recent research into language acquisition in developmental disorders, and the light that these findings shed on the nature of language acquisition in typically developing children. Disorders considered include Specific Language Impairment, autism, Down syndrome, and Williams syndrome. I argue that disorders of language should be construed in terms of differences in the constraints that shape the learning process, rather than in terms of the normal system with components missing or malfunctioning. I outline the integrative nature of this learning process and how properties such as redundancy and compensation may be key characteristics of learning systems with atypical constraints. These ideas, as well as the new methodologies now being used to study variations in pathways of language acquisition, are illustrated with case studies from Williams syndrome and Specific Language Impairment

A finite-state approach to arabic broken noun morphology

In this paper, a finite-state computational approach to Arabic broken plural noun morphology is introduced. The paper considers the derivational aspect of the approach, and how generalizations about dependencies in the broken plural noun derivational system of Arabic are captured and handled computationally in this finite-state approach. The approach will be implemented using Xerox finite-state tool

Encoding of phonology in a recurrent neural model of grounded speech

We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.Comment: Accepted at CoNLL 201

When orthography is not enough: the effect of lexical stress in lexical decision.

Three lexical decision experiments were carried out in Italian, in order to verify if stress dominance (the most frequent stress type) and consistency (the proportion and number of existent words sharing orthographic ending and stress pattern) had an effect on polysyllabic word recognition. Two factors were manipulated: whether the target word carried stress on the penultimate (dominant; graNIta, seNIle 'slush, senile') or on the antepenultimate (non-dominant) syllable (MISsile, BIbita 'missile, drink'), and whether the stress neighborhood was consistent (graNIta, MISsile) or inconsistent (seNIle, BIbita) with the word\u2019s stress pattern. In Experiment 1 words were mixed with nonwords sharing the word endings, which made words and nonwords more similar to each other. In Experiment 2 words and nonwords were presented in lists blocked for stress pattern. In Experiment 3 we used a new set of nonwords, which included endings with (stress) ambiguous neighborhoods and/or with low number of neighbors, and which were overall less similar to words. In all three experiments there was an advantage for words with penultimate (dominant) stress, and no main effect of stress neighborhood. However, the dominant stress advantage decreased in Experiments 2 and 3. Finally, in Experiment 4 the same materials used in Experiment 1 were also used in a reading aloud task, showing a significant consistency effect, but no dominant stress advantage. The influence of stress information in Italian word recognition is discussed

In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology

This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in a multilingual setting. We employ models using three different types of language embedding (dense, sigmoid, and straight-through). We find that the Straight-Through model outperforms the other two in terms of accuracy, but the Sigmoid model's language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages. We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research