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Related Experiment Videos

Counter model for word identification: reply to Bowers (1999).

G McKoon1, R Ratcliff

  • 1Department of Psychology, Northwestern University, Evanston, Illinois 60208, USA.

Psychological Review
|August 8, 2001
PubMed
Summary
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The counter model explains word identification, showing prior encounters usually bias responses. However, it also explains how encountering low-frequency words can improve performance.

Area of Science:

  • Cognitive Psychology
  • Psycholinguistics
  • Computational Neuroscience

Background:

  • The counter model (Ratcliff & McKoon, 1997) models word identification processes.
  • It explains how prior word encounters influence current identification.
  • Prior encounters typically create response biases, favoring previously seen words.

Purpose of the Study:

  • To demonstrate how the counter model explains improved performance with low-frequency words after prior encounters.
  • To refute claims that data supporting the counter model in forced-choice experiments are spurious.
  • To advocate for a comprehensive theoretical framework for word identification.

Main Methods:

  • Analysis of word identification experiments, particularly those involving brief word presentations.

Related Experiment Videos

  • Examination of data concerning low-frequency words and prior encounter effects.
  • Re-evaluation of findings from forced-choice experiments with dissimilar alternatives.

    • Main Results:

    • The counter model successfully accounts for improved performance on low-frequency words following prior exposure.
    • The authors demonstrate that criticisms regarding data supporting the counter model are unfounded.
    • The model's explanatory power extends across various tasks and variables.

    Conclusions:

    • The counter model provides a robust explanation for word identification phenomena, including the effects of prior encounters.
    • The study reaffirms the validity of data supporting the counter model.
    • A detailed cognitive account of word identification is essential for understanding performance variations.