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

Expanding the search for a linear separability constraint on category learning.

M Blair1, D Homa

  • 1Department of Psychology, Arizona State University, Tempe 85287-1104, USA. mark.blair@asu.edu

Memory & Cognition
|March 27, 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

Schizophrenia syndrome due to C9ORF72 mutation case report: a cautionary tale and role of hybrid brain imaging!

BMC psychiatry·2021
Same author

Parent and staff attitudes towards in-hospital opportunistic vaccination.

Public health·2020
Same author

A systematic surveillance programme for infectious salmon anaemia virus supports its absence in the Pacific Northwest of the United States.

Journal of fish diseases·2017
Same author

Whole population integrated child health: moving beyond pathways.

Archives of disease in childhood·2016
Same author

Synthesis, cytotoxicity, and hydroxyapatite formation in 27-Tris-SBF for sol-gel based CaO-P2O5-SiO2-B2O3-ZnO bioactive glasses.

Scientific reports·2014
Same author

Organization and long-term memory search.

Memory & cognition·2013
Same journal

The properties of personal semantics.

Memory & cognition·2026
Same journal

Music enhances associative generalization: Evidence from a memory integration task.

Memory & cognition·2026
Same journal

Video, text, and memory: An emotional verbal overshadowing effect.

Memory & cognition·2026
Same journal

Limited protective effects of multilingualism against age-related cognitive decline.

Memory & cognition·2026
Same journal

Validation of illustrated texts: Can pictures raise awareness of inconsistencies?

Memory & cognition·2026
Same journal

I remember (and forget) your happy smiling face: Directed forgetting of emotionally expressive faces of in-group and out-group members.

Memory & cognition·2026
See all related articles

This study found that linearly separable categories are easier to learn than non-linearly separable ones, especially for larger categories. Findings suggest multiple cognitive processes are involved in category learning.

Area of Science:

  • Cognitive Science
  • Psychology
  • Machine Learning

Background:

  • Formal models of categorization offer varying predictions on the significance of linear separability.
  • Previous research, often using small two-category tasks, has yielded limited evidence for linear separability constraints in category learning.

Purpose of the Study:

  • To investigate the role of linear separability in category learning with larger and more complex category structures.
  • To determine if category size influences the application of linear separability constraints.
  • To evaluate the explanatory power of exemplar models in light of empirical findings.

Main Methods:

  • An experiment was conducted using four categories, each containing either three or nine patterns.
  • Stimuli were designed to be either linearly separable or not linearly separable, while maintaining equivalent overall category structure.

Related Experiment Videos

  • Participant data were analyzed to identify patterns of constraint usage and to test formal models.

    • Main Results:

    • Linearly separable categories were learned more easily than non-linearly separable categories.
    • A greater proportion of participants applied linear separability constraints when learning larger categories compared to smaller ones.
    • An exemplar model failed to adequately account for a significant portion of the observed data.

    Conclusions:

    • Linear separability plays a significant role in category learning, particularly with increased category complexity.
    • The findings suggest that category learning is not governed by a single process but involves multiple, potentially interacting, cognitive mechanisms.
    • The results challenge the sufficiency of simple exemplar models for explaining complex categorization behavior.