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Related Concept Videos

Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus: Comparing...
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
Causality in Epidemiology01:21

Causality in Epidemiology

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Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

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Related Experiment Video

Updated: Jul 17, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Models of Variability in Probabilistic Causal Judgments.

Ivar Kolvoort1,2, Zachary J Davis3, Bob Rehder3

  • 1Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.

Computational Brain & Behavior
|July 16, 2026
PubMed
Summary

Causal reasoning judgments vary significantly within individuals. Computational models, particularly the Bayesian Mutation Sampler, best explain this variability, suggesting stochastic sampling and non-reasoning processes are key to human causal inference.

Keywords:
Causal judgmentCausal reasoningCognitive modelingRepeated-measuresResponse distributionsSampling

Related Experiment Videos

Last Updated: Jul 17, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Cognitive Science
  • Computational Psychology
  • Decision Science

Background:

  • Traditional causal reasoning theories focus on central tendencies, yet empirical data reveal significant within-participant variability.
  • Understanding the sources of this variability is crucial for a comprehensive model of human causal judgment.

Purpose of the Study:

  • To experimentally demonstrate meaningful within-participant variability in causal judgments.
  • To introduce and evaluate computational cognitive models explaining the sources of this variability.
  • To identify the best-fitting model for observed response distributions in causal reasoning.

Main Methods:

  • A novel repeated measures experimental design was employed to capture within-participant variability.
  • Multiple computational cognitive models were developed and assessed.
  • Models were fitted to empirical data, focusing on the full response distributions.

Main Results:

  • Significant, non-noise-related within-participant variability in causal judgments was confirmed.
  • The Bayesian Mutation Sampler model demonstrated the best fit to the empirical data.
  • The best model accounted for unusual response distribution features like bi-modality.

Conclusions:

  • Stochastic sampling mechanisms, as posited by the Bayesian Mutation Sampler, likely underlie human causal inference.
  • Incorporating non-reasoning processes (rounding, guessing) enhances model accuracy.
  • Computational modeling of full response distributions offers valuable insights into causal reasoning mechanisms.