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Record W1968894651 · doi:10.1080/13803395.2014.941790

Dynamic working memory performance in individuals with single-domain amnestic mild cognitive impairment

2014· article· en· W1968894651 on OpenAlexafffund
Emma B. Guild, Brandon P. Vasquez, Andrea Maione, Linda Mah, Jon Erik Ween, Nicole D. Anderson

Bibliographic record

VenueJournal of Clinical and Experimental Neuropsychology · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of TorontoBaycrest Hospital
FundersCanadian Institutes of Health Research
KeywordsWorking memoryPsychologyDementiaCognitionCognitive psychologyTask (project management)Episodic memoryAmnesiaAudiologyDevelopmental psychologyNeuroscienceMedicineDisease

Abstract

fetched live from OpenAlex

Previous studies have observed poorer working memory performance in individuals with amnestic mild cognitive impairment than in healthy older adults. It is unclear, however, whether these difficulties are true only of the multiple-domain clinical subtype in whom poorer executive functioning is common. The current study examined working memory, as measured by the self-ordered pointing task (SOPT) and an n-back task, in healthy older adults and adults with single-domain amnestic mild cognitive impairment (aMCI). Individuals with single-domain aMCI committed more errors and required longer to develop an organizational strategy on the SOPT. The single-domain aMCI group did not differ from healthy older adults on the 1-back or 2-back, but had poorer discrimination on the 3-back task. This is, to our knowledge, the first characterization of dynamic working memory performance in a single-domain aMCI group. These results lend support for the idea that clinical amnestic MCI subtypes may reflect different stages on a continuum of progression to dementia and question whether standardized measures of working memory (span tasks) are sensitive enough to capture subtle changes in performance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.395
Teacher spread0.352 · 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 designObservational
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

Citations15
Published2014
Admission routes2
Has abstractyes

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