Visual processing is the earliest core process required to support a normal reading system. However, little attention has been given to its role in any of the existing cognitive/computational models of reading. The ultimate goal of this thesis is to create a large-scale model of reading, which can generate phonology and semantics from print. Building such a model will allow for the exploration of a number of theoretically important cognitive phenomena in both normal and impaired reading including: font and size invariance; letter confusability; length effects; and pure alexic reading patterns. To achieve this goal, there are a number of important sub-goals that need to be achieved: (1) to develop a visual processing component which is capable of recognising letters in different fonts and sizes; (2) to produce a model that can develop useful intermediate (orthographic) representations as a consequence of learning; (3) to develop a set of semantic representations compact enough to allow efficient learning but that can still capture realistic semantic similarity relationships; (4) to integrate all the components together into a large-scale recurrent reading model; and (5) to extend the model to support picture naming, and to explore whether damage to the visual system can produce symptoms similar to those found in PA patients. Chapter 2 started by developing a simple feedforward network for letter recognition. The model was trained with letters in various transformations, which allowed the model to learn to deal with size and shape invariance problems as well as accounting for letter confusability effects and generalising to previously unseen letters. The model achieved this by extracting key features from visual input which could be used to support accurate letter recognition. Chapter 3 incorporated the letter recognition component developed in Chapter 2 into a word reading model. The reading model was trained on the mappings between print and phonology, with the orthographic representations which learn to emerge over training. The model could support accurate nonword naming and simulated the length by lexicality interaction observed in normal reading. A system of semantic representations was developed in Chapter 4 by using co-occurrence statistics to generate semantic codes that preserved realistic similarity relationships. Chapter 5 integrated all the components developed in the previous chapters together into a large-scale recurrent reading model. Finally, Chapter 6 extended the reading model to perform object recognition along with the reading task. When the model's visual system was damaged it was able to simulate the abnormal length effect typically seen in PA patients. The damaged model also showed impaired reaction times in object naming and preserved sensitivity to lexical/semantic variables in reading. The picture naming performance was modulated by visual complexity. In summary, the results highlight the importance of incorporating visual information into computational models of single word reading, and suggest that doing so will enable the exploration of a wide range of effects that were previously inaccessible to these types of connectionist models.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:724630 |
| Date | January 2012 |
| Creators | Chang, Ya-Ning |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/the-role-of-visual-processing-in-computational-models-of-reading(86ca64ef-b471-4932-a891-53728301ff1c).html |
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