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Modelling Normal and Impaired Letter Recognition: Implications for Understanding Pure Alexic Reading

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Title: Modelling Normal and Impaired Letter Recognition: Implications for Understanding Pure Alexic Reading
Language: English
Authors: Chang, Ya-Ning, Furber, Steve, Welbourne, Stephen
Source: Neuropsychologia. Oct 2012 50(12):2773-2788.
Availability: Elsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
Peer Reviewed: Y
Physical Description: PDF
Page Count: 16
Publication Date: 2012
Document Type: Journal Articles
Reports - Research
Descriptors: Alphabets, Reading Difficulties, Patients, Structural Analysis (Linguistics), Models, Computation, Data Analysis, Recognition (Psychology), Correlation, Predictor Variables
DOI: 10.1016/j.neuropsychologia.2012.07.031
ISSN: 0028-3932
Abstract: Letter recognition is the foundation of the human reading system. Despite this, it tends to receive little attention in computational modelling of single word reading. Here we present a model that can be trained to recognise letters in various spatial transformations. When presented with degraded stimuli the model makes letter confusion errors that correlate with human confusability data. Analyses of the internal representations of the model suggest that a small set of learned visual feature detectors support the recognition of both upper case and lower case letters in various fonts and transformations. We postulated that a damaged version of the model might be expected to act in a similar manner to patients suffering from pure alexia. Summed error score generated from the model was found to be a very good predictor of the reading times of pure alexic patients, outperforming simple word length, and accounting for 47% of the variance. These findings are consistent with a hypothesis suggesting that impaired visual processing is a key to understanding the strong word-length effects found in pure alexic patients. (Contains 9 tables and 8 figures.)
Abstractor: As Provided
Entry Date: 2012
Accession Number: EJ982707
Database: ERIC
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  Data: Elsevier. 6277 Sea Harbor Drive, Orlando, FL 32887-4800. Tel: 877-839-7126; Tel: 407-345-4020; Fax: 407-363-1354; e-mail: usjcs@elsevier.com; Web site: http://www.elsevier.com
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  Data: Letter recognition is the foundation of the human reading system. Despite this, it tends to receive little attention in computational modelling of single word reading. Here we present a model that can be trained to recognise letters in various spatial transformations. When presented with degraded stimuli the model makes letter confusion errors that correlate with human confusability data. Analyses of the internal representations of the model suggest that a small set of learned visual feature detectors support the recognition of both upper case and lower case letters in various fonts and transformations. We postulated that a damaged version of the model might be expected to act in a similar manner to patients suffering from pure alexia. Summed error score generated from the model was found to be a very good predictor of the reading times of pure alexic patients, outperforming simple word length, and accounting for 47% of the variance. These findings are consistent with a hypothesis suggesting that impaired visual processing is a key to understanding the strong word-length effects found in pure alexic patients. (Contains 9 tables and 8 figures.)
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