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Related Experiment Videos

A model for evidence accumulation in the lexical decision task.

Eric-Jan Wagenmakers1, Mark Steyvers, Jeroen G W Raaijmakers

  • 1Department of Psychology, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands. ej@northwestern.edu

Cognitive Psychology
|March 17, 2004
PubMed
Summary

We developed REM-LD, a new computational model for lexical decision tasks. It uses Bayes' rule to weigh evidence for word and nonword identification, improving accuracy in word recognition.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Psycholinguistics

Background:

  • Lexical decision tasks are crucial for understanding word recognition.
  • Existing models may not fully capture the decision process in lexical access.

Purpose of the Study:

  • Introduce REM-LD, a novel model for lexical decision based on REM theory.
  • Investigate the time course of lexical processing using a signal-to-respond paradigm.

Main Methods:

  • Developed REM-LD, a principled model using Bayes' rule for word/nonword decision.
  • Conducted two experiments using a signal-to-respond paradigm.
  • Analyzed effects of word frequency, nonword lexicality, and repetition priming.

Main Results:

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  • REM-LD accurately predicted that word frequency influences nonword performance.
  • The model successfully accounted for experimental results concerning lexicality and priming.
  • Verified the model's ability to capture the time course of lexical processing.

Conclusions:

  • REM-LD offers a principled approach to lexical decision, integrating evidence diagnosticity.
  • The model provides a framework for understanding word recognition dynamics.
  • Potential extensions include accounting for phonological effects and predicting response times in various paradigms.