Modelling normal and impaired letter recognition: Implications for understanding pure alexic reading

Ya Ning Chang, Steve Furber, Stephen Welbourne

Research output: Contribution to journalArticlepeer-review

123 Downloads (Pure)

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. © 2012 Elsevier Ltd.
Original languageEnglish
Pages (from-to)2773-2788
Number of pages15
JournalNEUROPSYCHOLOGIA
Volume50
Issue number12
DOIs
Publication statusPublished - Oct 2012

Keywords

  • Computational modelling
  • Letter confusability
  • Letter recognition
  • Pure alexia

Fingerprint

Dive into the research topics of 'Modelling normal and impaired letter recognition: Implications for understanding pure alexic reading'. Together they form a unique fingerprint.

Cite this