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Related Experiment Videos

Discrete threshold versus continuous strength models of perceptual recognition.

K R Paap1, E Chun, P Vonnahme

  • 1Department of Psychology, New Mexico State University, Las Cruces 88003, USA. kenp@crl.nmsu.edu

Canadian Journal of Experimental Psychology = Revue Canadienne De Psychologie Experimentale
|January 26, 2000
PubMed
Summary

This study compared discrete-threshold and continuous-strength models of word recognition. Continuous strength models better predicted experimental results, suggesting decision criteria are applied to continuous measures rather than discrete thresholds.

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Area of Science:

  • Cognitive Psychology
  • Psycholinguistics
  • Computational Neuroscience

Background:

  • Models of word recognition vary in their assumptions about decision-making processes.
  • Discrete-threshold models propose distinct cutoffs for recognition, while continuous-strength models suggest criteria applied to graded activation levels.

Purpose of the Study:

  • To empirically test discrete-threshold models against continuous-strength models of letter and word recognition.
  • To determine which class of models better accounts for behavioral data in recognition tasks.

Main Methods:

  • Experiment 1: Two-alternative forced-choice task with confidence ratings and whole-report, manipulating neighborhood frequency, lexical bias, word superiority, and pseudoword advantage.
  • Experiment 2: Same-different task with confidence ratings to generate receiver operating characteristics (ROCs).

Related Experiment Videos

  • Model predictions from the Dual-Readout Model (DROM) and a revised Activation-Verification Model were compared to empirical data.

    • Main Results:

    • Experiment 1 showed discrepancies between DROM predictions and observed data; continuous strength model predictions for response distributions were superior.
    • Experiment 2 ROC curves were more consistent with continuous strength assumptions than discrete threshold models.

    Conclusions:

    • Findings support models where decision criteria are applied to continuous measures of word strength over discrete-threshold models.
    • The continuous strength assumption provides a more accurate account of human word recognition processes.