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      Modelling reading development through phonological decoding and self-teaching: implications for dyslexia

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          Abstract

          The most influential theory of learning to read is based on the idea that children rely on phonological decoding skills to learn novel words. According to the self-teaching hypothesis, each successful decoding encounter with an unfamiliar word provides an opportunity to acquire word-specific orthographic information that is the foundation of skilled word recognition. Therefore, phonological decoding acts as a self-teaching mechanism or ‘built-in teacher’. However, all previous connectionist models have learned the task of reading aloud through exposure to a very large corpus of spelling–sound pairs, where an ‘external’ teacher supplies the pronunciation of all words that should be learnt. Such a supervised training regimen is highly implausible. Here, we implement and test the developmentally plausible phonological decoding self-teaching hypothesis in the context of the connectionist dual process model. In a series of simulations, we provide a proof of concept that this mechanism works. The model was able to acquire word-specific orthographic representations for more than 25 000 words even though it started with only a small number of grapheme–phoneme correspondences. We then show how visual and phoneme deficits that are present at the outset of reading development can cause dyslexia in the course of reading development.

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          Toward a modern theory of adaptive networks: expectation and prediction.

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            Developmental dyslexia: the visual attention span deficit hypothesis.

            The visual attention (VA) span is defined as the amount of distinct visual elements which can be processed in parallel in a multi-element array. Both recent empirical data and theoretical accounts suggest that a VA span deficit might contribute to developmental dyslexia, independently of a phonological disorder. In this study, this hypothesis was assessed in two large samples of French and British dyslexic children whose performance was compared to that of chronological-age matched control children. Results of the French study show that the VA span capacities account for a substantial amount of unique variance in reading, as do phonological skills. The British study replicates this finding and further reveals that the contribution of the VA span to reading performance remains even after controlling IQ, verbal fluency, vocabulary and single letter identification skills, in addition to phoneme awareness. In both studies, most dyslexic children exhibit a selective phonological or VA span disorder. Overall, these findings support a multi-factorial view of developmental dyslexia. In many cases, developmental reading disorders do not seem to be due to phonological disorders. We propose that a VA span deficit is a likely alternative underlying cognitive deficit in dyslexia.
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              A distributed, developmental model of word recognition and naming.

              A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propagation learning algorithm. The model simulates many aspects of human performance, including (a) differences between words in terms of processing difficulty, (b) pronunciation of novel items, (c) differences between readers in terms of word recognition skill, (d) transitions from beginning to skilled reading, and (e) differences in performance on lexical decision and naming tasks. The model's behavior early in the learning phase corresponds to that of children acquiring word recognition skills. Training with a smaller number of hidden units produces output characteristic of many dyslexic readers. Naming is simulated without pronunciation rules, and lexical decisions are simulated without accessing word-level representations. The performance of the model is largely determined by three factors: the nature of the input, a significant fragment of written English; the learning rule, which encodes the implicit structure of the orthography in the weights on connections; and the architecture of the system, which influences the scope of what can be learned.
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                Author and article information

                Journal
                Philos Trans R Soc Lond B Biol Sci
                Philos. Trans. R. Soc. Lond., B, Biol. Sci
                RSTB
                royptb
                Philosophical Transactions of the Royal Society B: Biological Sciences
                The Royal Society
                0962-8436
                1471-2970
                19 January 2014
                19 January 2014
                : 369
                : 1634 , Discussion Meeting Issue 'Language in developmental and acquired disorders: converging evidence for models of language representation in the brain' organized and edited by Dorothy V. M. Bishop, Kate Nation and Karalyn Patterson
                : 20120397
                Affiliations
                [1 ]Laboratoire de Psychologie Cognitive, Aix-Marseille University and Centre National de la Recherche Scientifique, Fédération de Recherche 3C, Brain and Language Research Institute , 3 place Victor Hugo, 13331 Marseille, France
                [2 ]Faculty of Life and Social Sciences, Swinburne University of Technology , John Street, Hawthorn, Victoria 3122, Australia
                [3 ]Dipartimento di Psicologia Generale, Università di Padova , Via Venezia, 8-35131 Padova, Italy
                [4 ]IRCCS San Camillo Neurorehabilitation Hospital , Via Alberoni 70, 30126 Venice, Italy
                Author notes
                Article
                rstb20120397
                10.1098/rstb.2012.0397
                3866426
                24324240
                7d8f67ca-94ce-4b6c-9e13-925413abac6f

                © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.

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                January 19, 2014

                Philosophy of science
                computational modelling,phonological decoding,developmental dyslexia,reading development

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