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Research article summary:
The multifaceted nature of unsupervised category learning.
Abstract Extract:
A substantial portion of category-learning research has focused on one learning mode--namely, classification learning (a supervised learning mode). Subsequently, theories of category learning have focused on how the abstract structure of categories ... (Full abstract text below) Published 2003Mar
in Journal: Psychon Bull Rev
(Language : eng)
Full Pubmed Extract
This information was retrieved, real-time, on your behalf from the public area of the Pubmed website:
1. Psychon Bull Rev.
2003 Mar;10(1):190-7
The multifaceted nature of unsupervised category learning.
Love BC
Department of Psychology, University of Texas, Austin, Texas 78712, USA. love@psy.utexas.edu
A substantial portion of category-learning research has focused on one learning mode--namely, classification learning (a supervised learning mode). Subsequently, theories of category learning have focused on how the abstract structure of categories (i.e., the co-occurrence patterns of feature values) affects acquisition. Recent work in supervised learning has shown that a learner's interactions with the stimulus set also plays an important role in acquisition. The present study extends this work to unsupervised learning situations involving simple one-dimensional stimuli. The results suggest that categorization performance is a function of both learning mode (i.e., study conditions) and learning problem (i.e., category structure). Unsupervised learning, like supervised learning, appears to be multifaceted, with different learning modes best paired with certain learning problems.
PMID : 12747507 [PubMed - Indexed for MEDLINE]
This information is obtained from the National Library of Medicine (NLM). Abstract text and other information may be subject to copyright. Type "NLM copyright" into Google for more information.
Full Author Information
| First Name | LastName | Initials |
| Bradley C | Love | BC |
Affiliation: Department of Psychology, University of Texas, Austin, Texas 78712, USA. love@psy.utexas.edu
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