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Related Experiment Videos

A probabilistic contrast model of causal induction.

P W Cheng1, L R Novick

  • 1Department of Psychology, University of California, Los Angeles 90024-1563.

Journal of Personality and Social Psychology
|April 1, 1990
PubMed
Summary

Causal attribution models often show deviations, but these may not be biases. New research suggests unspecified information or model limitations explain these differences, indicating an unbiased inference process.

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Area of Science:

  • Cognitive Psychology
  • Social Psychology
  • Decision Making

Background:

  • Covariational models of causal attribution frequently report deviations in empirical studies.
  • Observed deviations include underutilization of consensus information and overemphasis on dispositional factors.
  • Previous research has often interpreted these deviations as irrational biases.

Purpose of the Study:

  • To re-evaluate reported deviations from covariational models of causal attribution.
  • To propose that deviations may stem from unspecified information or model normativeness issues.
  • To introduce and test a probabilistic extension of Kelley's analysis-of-variance model.

Main Methods:

  • A probabilistic extension of Kelley's analysis-of-variance analogy was developed.

Related Experiment Videos

  • An experiment was conducted to assess common attributional biases.
  • The proposed model was evaluated against competing attributional models.

    • Main Results:

    • The experimental results suggest that the human causal inference process is unbiased.
    • Deviations observed in previous studies may be attributable to factors not accounted for in existing models.
    • The proposed probabilistic model offered a better explanation for observed attributional patterns.

    Conclusions:

    • Deviations from covariational models do not necessarily indicate irrational biases in causal attribution.
    • Unspecified information and the normativeness of comparison models are critical factors influencing causal inferences.
    • The findings support a more nuanced understanding of human causal reasoning, highlighting its inherent unbiased nature under appropriate theoretical frameworks.