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The Spatial Coding Model of Visual Word Identification

The Spatial Coding Model of Visual Word Identification Visual word identification requires readers to code the identity and order of the letters in a word and match this code against previously learned codes. Current models of this lexical matching process posit context-specific letter codes in which letter representations are tied to either specific serial positions or specific local contexts (e.g., letter clusters). The spatial coding model described here adopts a different approach to letter position coding and lexical matching based on context-independent letter representations. In this model, letter position is coded dynamically, with a scheme called spatial coding. Lexical matching is achieved via a method called superposition matching, in which input codes and learned codes are matched on the basis of the relative positions of their common letters. Simulations of the model illustrate its ability to explain a broad range of results from the masked form priming literature, as well as to capture benchmark findings from the unprimed lexical decision task. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychological Review American Psychological Association

The Spatial Coding Model of Visual Word Identification

Psychological Review , Volume 117 (3): 46 – Jul 1, 2010

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Publisher
American Psychological Association
Copyright
Copyright © 2010 American Psychological Association
ISSN
0033-295x
eISSN
1939-1471
DOI
10.1037/a0019738
pmid
20658851
Publisher site
See Article on Publisher Site

Abstract

Visual word identification requires readers to code the identity and order of the letters in a word and match this code against previously learned codes. Current models of this lexical matching process posit context-specific letter codes in which letter representations are tied to either specific serial positions or specific local contexts (e.g., letter clusters). The spatial coding model described here adopts a different approach to letter position coding and lexical matching based on context-independent letter representations. In this model, letter position is coded dynamically, with a scheme called spatial coding. Lexical matching is achieved via a method called superposition matching, in which input codes and learned codes are matched on the basis of the relative positions of their common letters. Simulations of the model illustrate its ability to explain a broad range of results from the masked form priming literature, as well as to capture benchmark findings from the unprimed lexical decision task.

Journal

Psychological ReviewAmerican Psychological Association

Published: Jul 1, 2010

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