Literature DB >> 15709932

Human category learning.

F Gregory Ashby1, W Todd Maddox.   

Abstract

Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular-rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.

Entities:  

Mesh:

Year:  2005        PMID: 15709932     DOI: 10.1146/annurev.psych.56.091103.070217

Source DB:  PubMed          Journal:  Annu Rev Psychol        ISSN: 0066-4308            Impact factor:   24.137


  248 in total

1.  Analogical transfer in perceptual categorization.

Authors:  Michael B Casale; Jessica L Roeder; F Gregory Ashby
Journal:  Mem Cognit       Date:  2012-04

2.  Perceptual fluency can be used as a cue for categorization decisions.

Authors:  Sarah J Miles; John Paul Minda
Journal:  Psychon Bull Rev       Date:  2012-08

3.  Effects of musicality and motivational orientation on auditory category learning: a test of a regulatory-fit hypothesis.

Authors:  J Devin McAuley; Molly J Henry; Alan Wedd; Timothy J Pleskac; Joseph Cesario
Journal:  Mem Cognit       Date:  2012-02

Review 4.  Adaptation, expertise, and giftedness: towards an understanding of cortical, subcortical, and cerebellar network contributions.

Authors:  Leonard F Koziol; Deborah Ely Budding; Dana Chidekel
Journal:  Cerebellum       Date:  2010-12       Impact factor: 3.847

5.  Computational Models Inform Clinical Science and Assessment: An Application to Category Learning in Striatal-Damaged Patients.

Authors:  W Todd Maddox; J Vincent Filoteo; Dagmar Zeithamova
Journal:  J Math Psychol       Date:  2010-02-01       Impact factor: 2.223

6.  The dimensionality of perceptual category learning: a state-trace analysis.

Authors:  Ben R Newell; John C Dunn; Michael Kalish
Journal:  Mem Cognit       Date:  2010-07

7.  Classification response times in probabilistic rule-based category structures: contrasting exemplar-retrieval and decision-boundary models.

Authors:  Robert M Nosofsky; Daniel R Little
Journal:  Mem Cognit       Date:  2010-10

8.  Role of a lateralized parietal-basal ganglia circuit in hierarchical pattern perception: evidence from Parkinson's disease.

Authors:  Haline E Schendan; Melissa M Amick; Alice Cronin-Golomb
Journal:  Behav Neurosci       Date:  2009-02       Impact factor: 1.912

9.  Initial training with difficult items facilitates information integration, but not rule-based category learning.

Authors:  Brian J Spiering; F Gregory Ashby
Journal:  Psychol Sci       Date:  2008-11

10.  The C957T polymorphism in the dopamine receptor D₂ gene modulates domain-general category learning.

Authors:  Zilong Xie; W Todd Maddox; John E McGeary; Bharath Chandrasekaran
Journal:  J Neurophysiol       Date:  2015-03-11       Impact factor: 2.714

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