Division of Labor in a Computational
Model of Visual Word Recognition
Ph.D. Thesis, August 1998, University of Southern California
here for a BibTeX entry.
How do we compute the meanings of written words? For decades, the
basic mechanisms underlying visual word recognition have remained
controversial. The intuitions of educators and policy makers, and the
existing empirical evidence have resulted in contradictory
conclusions, particularly about the role of the sound structure of
language (phonology) in word recognition. To explore the relative
contributions of phonological and direct information in word
recognition, a large scale connectionist model of visual word
recognition was created containing orthographic, semantic and
phonological representations. The behavior of the model is analyzed
and explained in terms of redundant representations, the development
of dynamic attractors in representational space, the time course of
activation and processing within such networks, and demands of the
reading task itself. The different patterns of results that have been
obtained in previous behavioral studies are explained by appeal to
stimulus composition and properties of a common experimental paradigm.
A unified explanation of a wide range of empirical phenomena is
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