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

Adaptive categorization in unsupervised learning.

John P Clapper1, Gordon H Bower2

  • 1Humboldt State U.

Journal of Experimental Psychology. Learning, Memory, and Cognition
|September 11, 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

Graded similarity in free categorization.

Cognition·2019
Same author

Alignability-based free categorization.

Cognition·2017
Same author

The impact of training sequence and between-category similarity on unsupervised induction.

Quarterly journal of experimental psychology (2006)·2014
Same author

The effects of prior knowledge on incidental category learning.

Journal of experimental psychology. Learning, memory, and cognition·2012
Same author

The evolution of a cognitive psychologist: a journey from simple behaviors to complex mental acts.

Annual review of psychology·2007
Same author

Prior knowledge and correlational structure in unsupervised learning.

Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale·2007
Same journal

Testing the predictions of a distinctiveness model of memory: The production effect in backward recall.

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

On the impact of adjacency on transposed-word effects under serial presentation.

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

It's time to opt out: Metacognitive analysis of time regulation under uncertainty.

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

The role of statistical learning in attentional guidance during search through naturalistic scenes.

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

Representing objects and features in long-term memory: A case for direct feature-feature binding.

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

Crossmodal correspondences influence adaptation during rule-based category learning of objects.

Journal of experimental psychology. Learning, memory, and cognition·2026
See all related articles

This study reveals a distinct category-invention process in unsupervised learning. Expectation failure, not just feature correlation, drives the creation of new categories during discovery learning.

Area of Science:

  • Cognitive Psychology
  • Machine Learning
  • Learning Sciences

Background:

  • Unsupervised learning involves discovering patterns without explicit labels.
  • Existing models often focus on incremental feature correlation tracking.
  • The role of category invention in unsupervised discovery learning requires further investigation.

Purpose of the Study:

  • To provide evidence for a distinct category-invention process in unsupervised learning.
  • To establish a method for observing and investigating this category-invention process.
  • To contrast this process with incremental correlation-based learning models.

Main Methods:

  • Three experiments were conducted using unlabeled training instances.
  • Instance sequencing was manipulated to assess its effect on pattern discovery.

Related Experiment Videos

  • Diagnostic labels were introduced in a third experiment to evaluate their impact on learning.

    • Main Results:

    • Instance sequencing significantly impacted participants' ability to discover categories.
    • Providing diagnostic labels improved category discovery and learning, even for difficult sequences.
    • Results challenge models based solely on incremental feature correlation.

    Conclusions:

    • Learners utilize expectation failure as a trigger to invent distinct categories.
    • This category-invention process is a key mechanism in unsupervised discovery learning.
    • Findings align with and extend rational models of categorization for real-world applications.