The Bayesian reader: explaining word recognition as an optimal Bayesian decision process.

Psychological Review
Dennis Norris

Abstract

This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully simulates some of the most significant data on human reading. The model accounts for the nature of the function relating word frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks. Both the general behavior of the model and the way the model predicts different patterns of results in different tasks follow entirely from the assumption that human readers approximate optimal Bayesian decision makers.

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Citations

Jul 11, 2009·The Quarterly Journal of Experimental Psychology : QJEP·Sachiko Kinoshita, Dennis Norris
Feb 2, 2010·The Quarterly Journal of Experimental Psychology : QJEP·Javier García-OrzaSamara Muñoz
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Dec 18, 2013·Memory & Cognition·Sachiko KinoshitaDennis Norris
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Sep 10, 2013·Trends in Cognitive Sciences·Dennis Norris
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