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Bias in proportion judgments: the cyclical power model.

J G Hollands1, B P Dyre

  • 1Department of Psychology, University of Idaho, USA. justin.hollands@dciem.dnd.ca

Psychological Review
|August 15, 2000
PubMed
Summary

Systematic bias in part-whole proportion judgments can be explained by a cyclical power model. This model, derived from Stevens

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Area of Science:

  • Cognitive psychology
  • Psychophysics
  • Mathematical psychology

Background:

  • Part-whole proportion judgments often exhibit systematic biases.
  • Observed biases can include overestimation of small proportions and underestimation of large ones, or vice versa.
  • Bias patterns can sometimes repeat cyclically.

Purpose of the Study:

  • To develop a model that accounts for various systematic biases in part-whole proportion judgments.
  • To explain the amplitude and frequency of observed bias patterns.
  • To investigate the influence of reference points on bias patterns.

Main Methods:

  • Derivation of a cyclical power model from Stevens' power law.
  • Experimental validation of the model's assumptions.
  • Proposal of a mixed-cycle version of the model.

Main Results:

  • The cyclical power model predicts bias patterns based on the Stevens exponent (beta).
  • Beta < 1 predicts an over-then-under estimation pattern; beta > 1 predicts an under-then-over pattern.
  • A mixed-cycle model accounts for asymmetries in bias when reference points vary.

Conclusions:

  • The cyclical power model successfully explains systematic biases in proportion judgments.
  • The Stevens exponent and reference points are key determinants of bias patterns.
  • The model provides a framework for understanding perceptual judgment biases.

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