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

@article{Norris2006TheBR,
  title={The Bayesian reader: explaining word recognition as an optimal Bayesian decision process.},
  author={Dennis Norris},
  journal={Psychological review},
  year={2006},
  volume={113 2},
  pages={
          327-57
        }
}
  • D. Norris
  • Published 1 April 2006
  • Computer Science, Medicine
  • Psychological review
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… Expand

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