Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Within-category feature correlations and Bayesian adjustment strategies.

L Elizabeth Crawford1, Janellen Huttenlocher, Larry V Hedges

  • 1Department of Psychology, University of Richmond, Richmond, VA 23173, USA. lcrawfor@richmond.edu

Psychonomic Bulletin & Review
|August 9, 2006
PubMed
Summary

Category correlations improve judgment accuracy by acting as Bayesian priors, reducing variability in stimulus estimates. This demonstrates how statistical structure in experience enhances cognitive performance.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correcting the Variance of Effect Sizes Based on Binary Outcomes for Clustering.

Educational and psychological measurement·2025
Same author

Computing Statistical Power for the Difference in Differences Design.

Evaluation review·2025
Same author

Effect sizes for experimental research.

The British journal of mathematical and statistical psychology·2025
Same author

Interpretation of the Standardized Mean Difference Effect Size When Distributions Are Not Normal or Homoscedastic.

Educational and psychological measurement·2024
Same author

ABkPowerCalculator: An App to Compute Power for Balanced (AB)<sup>k</sup> Single Case Experimental Designs.

Multivariate behavioral research·2023
Same author

Robust variance estimation in small meta-analysis with the standardized mean difference.

Research synthesis methods·2023

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Making

Background:

  • Categories influence judgments by reflecting the statistical structure of past experiences.
  • Accurate category representations are hypothesized to improve judgment and estimation accuracy.
  • Bayesian models suggest that priors derived from statistical regularities can reduce estimation variability.

Purpose of the Study:

  • To investigate whether detected feature correlations in stimuli serve as Bayesian priors.
  • To determine if these priors enhance the accuracy of stimulus estimations.
  • To examine the effect of statistical structure within categories on judgment accuracy.

Main Methods:

  • Participants viewed objects varying on two dimensions, either uncorrelated or correlated.

Related Experiment Videos

  • Participants estimated presented stimuli by matching them with a response object.
  • Classification and feature-inference tasks assessed correlation detection and memory recall.

    • Main Results:

    • Participants successfully detected the feature correlation between stimulus dimensions.
    • Variability in stimulus recollections indicated that feature correlations influenced estimates.
    • Results align with a Bayesian model where category structure informs memory-based estimations.

    Conclusions:

    • Representations of feature correlations function as Bayesian priors in perception and memory.
    • These priors enhance judgment accuracy by constraining estimates and reducing variability.
    • The statistical structure of experience, when captured by categories, significantly impacts cognitive accuracy.