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

What makes a categorization task difficult?

Leola A Alfonso-Reese1, F Gregory Ashby, David H Brainard

  • 1Department of Psychology, San Diego State University, California 92182-4611, USA. leola@alum.mit.edu

Perception & Psychophysics
|July 23, 2002
PubMed
Summary
This summary is machine-generated.

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

Sense of agency and decision making.

Frontiers in psychology·2026
Same author

Sense of agency and addiction.

Frontiers in psychology·2026
Same author

An image-computable spatio-chromatic receptive field model of the midget retinal ganglion cell mosaic across the retina.

Journal of computational neuroscience·2026
Same author

A deep convolutional neural network trained for lightness constancy is susceptible to lightness illusions.

Journal of vision·2026
Same author

The Cone Optoretinogram as a Function of Retinal Eccentricity.

Photonics·2026
Same author

Is there a criterion in criterial learning? Insights from studying feedback delays.

Journal of experimental psychology. Learning, memory, and cognition·2026
Same journal

Response organization in selective adaptation to speech sounds.

Perception & psychophysics·2014
Same journal

Reaction times to comparisons within and across phonetic categories.

Perception & psychophysics·2012
Same journal

Auditory and phonetic memory codes in the discrimination of consonants and vowels.

Perception & psychophysics·2012
Same journal

Simple and contingent adaptation effects for place of articulation in stop consonants.

Perception & psychophysics·2012
Same journal

Auditory property detectors and processing place features in stop consonants.

Perception & psychophysics·2012
Same journal

Visual working memory for line orientations and face identities.

Perception & psychophysics·2008
See all related articles

Covariance complexity, a measure of task complexity, is a key predictor of human performance in categorization tasks. This study found it largely determines task difficulty, more so than other factors like internal noise.

Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Information Theory

Background:

  • Categorization tasks vary in difficulty, impacting human performance.
  • Understanding factors influencing task difficulty is crucial for cognitive modeling.
  • Previous models have not fully explained human performance variations in categorization.

Purpose of the Study:

  • To identify key factors predicting human performance in categorization tasks.
  • To investigate the role of covariance complexity in task difficulty.
  • To challenge existing categorization theories with new empirical evidence.

Main Methods:

  • An experiment was designed using a simulated medical decision-making task.
  • Human observers classified patient profiles (three-dimensional stimuli) into two disease categories.

Related Experiment Videos

  • Covariance complexity was systematically manipulated across five experimental conditions.

    • Main Results:

    • Human performance variation was primarily explained by covariance complexity.
    • Internal noise partially accounted for performance differences.
    • Optimal accuracy level, rule orientation, and class separability did not significantly explain performance.

    Conclusions:

    • Covariance complexity is a robust predictor of human categorization task difficulty.
    • Existing categorization models may need refinement to incorporate covariance complexity.
    • Further research should focus on developing models that align with complexity-driven performance.