Literature DB >> 27394151

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

Jeffrey S Phillips1, Corey T McMillan2, Edward E Smith3, Murray Grossman2.   

Abstract

Semantic category learning is dependent upon several factors, including the nature of the learning task, as well as individual differences in the quality and heterogeneity of exemplars that an individual encounters during learning. We trained healthy older adults (n=39) and individuals with a diagnosis of Alzheimer's disease or Mild Cognitive Impairment (n=44) to recognize instances of a fictitious animal, a "crutter". Each stimulus item contained 10 visual features (e.g., color, tail shape) which took one of two values for each feature (e.g., yellow/red, curly/straight tails). Participants were presented with a series of items (learning phase) and were either told the items belonged to a semantic category (explicit condition) or were told to think about the appearance of the items (implicit condition). Half of participants saw learning items with higher similarity to an unseen prototype (high typicality learning set), and thus lower between-item variability in their constituent features; the other half learned from items with lower typicality (low typicality learning set) and higher between-item feature variability. After the learning phase, participants were presented with test items one at a time that varied in the number of typical features from 0 (antitype) to 10 (prototype). We examined between-subjects factors of learning set (lower or higher typicality), instruction type (explicit or implicit), and group (patients vs. elderly control). Learning in controls was aided by higher learning set typicality: while controls in both learning set groups demonstrated significant learning, those exposed to a high-typicality learning set appeared to develop a prototype that helped guide their category membership judgments. Overall, patients demonstrated more difficulty with category learning than elderly controls. Patients exposed to the higher-typicality learning set were sensitive to the typical features of the category and discriminated between the most and least typical test items, although less reliably than controls. In contrast, patients exposed to the low-typicality learning set showed no evidence of learning. Analysis of structural imaging data indicated a positive association between left hippocampal grey matter density in elderly controls but a negative association in the patient group, suggesting differential reliance on hippocampal-mediated learning. Contrary to hypotheses, learning did not differ between explicit and implicit conditions for either group. Results demonstrate that category learning is improved when learning materials are highly similar to the prototype. Published by Elsevier Ltd.

Entities:  

Keywords:  Alzheimer’s disease; Category learning; Episodic memory; Hippocampus; Medial temporal lobes; Neurodegenerative disease; Prototype extraction; Semantic memory

Mesh:

Year:  2016        PMID: 27394151      PMCID: PMC5218992          DOI: 10.1016/j.neuropsychologia.2016.07.003

Source DB:  PubMed          Journal:  Neuropsychologia        ISSN: 0028-3932            Impact factor:   3.139


  57 in total

1.  Deferred feedback sharply dissociates implicit and explicit category learning.

Authors:  J David Smith; Joseph Boomer; Alexandria C Zakrzewski; Jessica L Roeder; Barbara A Church; F Gregory Ashby
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Review 2.  Basal ganglia and dopamine contributions to probabilistic category learning.

Authors:  D Shohamy; C E Myers; J Kalanithi; M A Gluck
Journal:  Neurosci Biobehav Rev       Date:  2007-08-10       Impact factor: 8.989

3.  Feedback timing modulates brain systems for learning in humans.

Authors:  Karin Foerde; Daphna Shohamy
Journal:  J Neurosci       Date:  2011-09-14       Impact factor: 6.167

4.  Finding faults: analogical comparison supports spatial concept learning in geoscience.

Authors:  Benjamin D Jee; David H Uttal; Dedre Gentner; Cathy Manduca; Thomas F Shipley; Bradley Sageman
Journal:  Cogn Process       Date:  2013-02-24

5.  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

6.  The topography of grey matter involvement in early and late onset Alzheimer's disease.

Authors:  Giovanni B Frisoni; Michela Pievani; Cristina Testa; Francesca Sabattoli; Lorena Bresciani; Matteo Bonetti; Alberto Beltramello; Kiralee M Hayashi; Arthur W Toga; Paul M Thompson
Journal:  Brain       Date:  2007-02-09       Impact factor: 13.501

7.  Dissociable properties of memory systems: differences in the flexibility of declarative and nondeclarative knowledge.

Authors:  P J Reber; B J Knowlton; L R Squire
Journal:  Behav Neurosci       Date:  1996-10       Impact factor: 1.912

8.  Cooperation between the hippocampus and the striatum during episodic encoding.

Authors:  Talya Sadeh; Daphna Shohamy; Dana Rubi Levy; Niv Reggev; Anat Maril
Journal:  J Cogn Neurosci       Date:  2010-07-28       Impact factor: 3.225

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.  Declarative and procedural learning, quantitative measures of the hippocampus, and subcortical white alterations in Alzheimer's disease and ischaemic vascular dementia.

Authors:  D J Libon; B Bogdanoff; B S Cloud; S Skalina; T Giovannetti; H L Gitlin; J Bonavita
Journal:  J Clin Exp Neuropsychol       Date:  1998-02       Impact factor: 2.475

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