Evidence map›Paper›PMID 27394151›Full record

ArticleNeuropsychologia2017

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

Jeffrey S Phillips, Corey T McMillan, Edward E Smith, Murray Grossman

Abstract read
In one paragraph

Article in Neuropsychologia, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jeffrey S PhillipsPenn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States. Electronic address: jefphi@mail.med.upenn.edu.
Corey T McMillanPenn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.
Edward E SmithDepartment of Psychology (deceased), Columbia University, New York, NY, United States.
Murray GrossmanPenn Frontotemporal Degeneration Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States.

Funding

Transgenic Mouse Models of Tau PathologiesP01AG017586 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LEE, VIRGINIA M · 2000 to 2021
$37.3M
UPenn ADCC Biomarker CoreP30AG010124 · NIA · UNIVERSITY OF PENNSYLVANIA · PI TROJANOWSKI, JOHN Q. · 1991 to 2020
$35.1M
Age, Hearing Loss, and Sentence Comprehension: Neural CorrelatesR01AG038490 · NIA · UNIVERSITY OF PENNSYLVANIA · PI GROSSMAN, MURRAY · 2012 to 2016
$2.1M
Multimodal Biomarkers in Frontotemporal Lobar DegenerationK01AG043503 · NIA · UNIVERSITY OF PENNSYLVANIA · PI MCMILLAN, COREY T · 2013 to 2017
$642k
NIA NIH HHS K01 AG043503NIA NIH HHS P01 AG017586NIA NIH HHS P30 AG010124NIA NIH HHS R01 AG038490
6 · The paper itself

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.

Indexed as

Cognitive AgingAgedAged, 80 and overAlzheimer DiseaseAnalysis of VarianceBrainCognition DisordersConcept FormationFemaleHumansImage Processing, Computer-AssistedLearning DisabilitiesMagnetic Resonance ImagingMaleMiddle AgedNeuropsychological TestsAlzheimer’s diseaseCategory learningEpisodic memoryHippocampusMedial temporal lobesNeurodegenerative diseasePrototype extractionSemantic memory

Identifiers

PMID27394151
PMCPMC5218992

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.