Literature DB >> 18405041

The case for implicit category learning.

Edward E Smith1.   

Abstract

This article evaluates the evidence regarding the claim that people can learn a novel category implicitly--that is, by an implicit memory system that is qualitatively different from an explicit system. The evidence that is considered is based on the prototype extraction task, in which participants are first exposed to a set of category exemplars under incidental learning instructions and are then required to categorize novel test items. Knowlton and Squire (1993) first reported that memory-impaired patients performed normally on the prototype extraction task while being impaired on a comparable recognition task. Several studies have replicated these results, but other articles have criticized the evidence for implicit category learning on both methodological and theoretical grounds. In this article, we consider five of these criticisms-for example, that the normal performance of the patients is due to intact working memory mechanisms (see, e.g., Palmeri & Flannery, 1999) or to the lesser cognitive demands of prototype extraction rather than recognition (e.g., Nosofsky & Zaki, 1998). For each of the five criticisms, we offer counterevidence that supports implicit category learning.

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Year:  2008        PMID: 18405041     DOI: 10.3758/cabn.8.1.3

Source DB:  PubMed          Journal:  Cogn Affect Behav Neurosci        ISSN: 1530-7026            Impact factor:   3.282


  31 in total

1.  Complementary category learning systems identified using event-related functional MRI.

Authors:  H J Aizenstein; A W MacDonald; V A Stenger; R D Nebes; J K Larson; S Ursu; C S Carter
Journal:  J Cogn Neurosci       Date:  2000-11       Impact factor: 3.225

2.  Math modeling, neuropsychology, and category learning:

Authors: 
Journal:  Trends Cogn Sci       Date:  1999-04       Impact factor: 20.229

3.  A single-system interpretation of dissociations between recognition and categorization in a task involving object-like stimuli.

Authors:  S R Zaki; R M Nosofsky
Journal:  Cogn Affect Behav Neurosci       Date:  2001-12       Impact factor: 3.282

Review 4.  Alternative strategies of categorization.

Authors:  E E Smith; A L Patalano; J Jonides
Journal:  Cognition       Date:  1998-01

5.  Learning about categories in the absence of memory.

Authors:  L R Squire; B J Knowlton
Journal:  Proc Natl Acad Sci U S A       Date:  1995-12-19       Impact factor: 11.205

6.  Categorization and recognition performance of a memory-impaired group: evidence for single-system models.

Authors:  Safa R Zaki; Robert M Nosofsky; Nenette M Jessup; Frederick W Unverzagt
Journal:  J Int Neuropsychol Soc       Date:  2003-03       Impact factor: 2.892

7.  Striatal activity in concept learning.

Authors:  Carol A Seger; Corinna M Cincotta
Journal:  Cogn Affect Behav Neurosci       Date:  2002-06       Impact factor: 3.282

Review 8.  Human category learning.

Authors:  F Gregory Ashby; W Todd Maddox
Journal:  Annu Rev Psychol       Date:  2005       Impact factor: 24.137

9.  The learning of categories: parallel brain systems for item memory and category knowledge.

Authors:  B J Knowlton; L R Squire
Journal:  Science       Date:  1993-12-10       Impact factor: 47.728

10.  Cortico-striatal contributions to feedback-based learning: converging data from neuroimaging and neuropsychology.

Authors:  D Shohamy; C E Myers; S Grossman; J Sage; M A Gluck; R A Poldrack
Journal:  Brain       Date:  2004-03-10       Impact factor: 13.501

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  10 in total

1.  Activation in the neural network responsible for categorization and recognition reflects parameter changes.

Authors:  Robert M Nosofsky; Daniel R Little; Thomas W James
Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-19       Impact factor: 11.205

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.  Category learning in Alzheimer's disease and normal cognitive aging depends on initial experience of feature variability.

Authors:  Jeffrey S Phillips; Corey T McMillan; Edward E Smith; Murray Grossman
Journal:  Neuropsychologia       Date:  2016-07-06       Impact factor: 3.139

4.  A high-distortion enhancement effect in the prototype-learning paradigm: dramatic effects of category learning during test.

Authors:  Safa R Zaki; Robeir M Nosofsky
Journal:  Mem Cognit       Date:  2007-12

5.  Medial temporal lobe involvement in an implicit memory task: evidence of collaborating implicit and explicit memory systems from FMRI and Alzheimer's disease.

Authors:  Phyllis Koenig; Edward E Smith; Vanessa Troiani; Chivon Anderson; Peachie Moore; Murray Grossman
Journal:  Cereb Cortex       Date:  2008-04-09       Impact factor: 5.357

6.  Syntactic transfer in artificial grammar learning.

Authors:  T Beesley; A J Wills; M E Le Pelley
Journal:  Psychon Bull Rev       Date:  2010-02

7.  The effect of encoding conditions on learning in the prototype distortion task.

Authors:  Jessica C Lee; Evan J Livesey
Journal:  Learn Behav       Date:  2017-06       Impact factor: 1.986

8.  Studies of implicit prototype extraction in patients with mild cognitive impairment and early Alzheimer's disease.

Authors:  Robert M Nosofsky; Stephen E Denton; Safa R Zaki; Anne F Murphy-Knudsen; Frederick W Unverzagt
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2012-07       Impact factor: 3.051

Review 9.  Category learning in the brain.

Authors:  Carol A Seger; Earl K Miller
Journal:  Annu Rev Neurosci       Date:  2010       Impact factor: 12.449

10.  Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study.

Authors:  Jay Hegdé
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-04
  10 in total

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