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Record W2315850132 · doi:10.1080/13825585.2016.1158235

The effectiveness of unitization in mitigating age-related relational learning impairments depends on existing cognitive status

2016· article· en· W2315850132 on OpenAlexafffundabout
Maria C. D’Angelo, Victoria M. Smith, Arber Kacollja, Felicia Zhang, Malcolm A. Binns, Morgan D. Barense, Jennifer D. Ryan

Bibliographic record

VenueAging Neuropsychology and Cognition · 2016
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of TorontoBaycrest Hospital
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsPsychologyNeuropsychologyCognitionAmnesiaDevelopmental psychologyTask (project management)Hippocampal formationYoung adultCognitive impairmentMontreal Cognitive AssessmentAudiologyCognitive psychologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Binding relations among items in the transverse patterning (TP) task is dependent on the integrity of the hippocampus and its extended network. Older adults have impaired TP learning, corresponding to age-related reductions in hippocampal volumes. Unitization is a training strategy that can mitigate TP impairments in amnesia by reducing reliance on hippocampal-dependent relational binding and increasing reliance on fused representations. Here we examined whether healthy older adults and those showing early signs of cognitive decline would also benefit from unitization. Although both groups of older adults had neuropsychological performance within the healthy range, their TP learning differed both under standard and unitized training conditions. Healthy older adults with impaired TP learning under standard training benefited from unitized training. Older adults who failed the Montreal Cognitive Assessment (MoCA) showed greater impairments under standard conditions, and showed no evidence of improvement with unitization. These individuals' failures to benefit from unitization may be a consequence of early deficits not seen in older adults who pass the MoCA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.332
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2016
Admission routes3
Has abstractyes

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