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Record W3026910751 · doi:10.1017/s1355617720000429

An Incidental Learning Method to Improve Face-Name Memory in Older Adults With Amnestic Mild Cognitive Impairment

2020· article· en· W3026910751 on OpenAlexafffund
Renée K. Biss, Gillian Rowe, Lynn Hasher, Kelly J. Murphy

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of TorontoBaycrest Hospital
FundersCanadian Institutes of Health Research
KeywordsForgettingPsychologyRecallSurpriseCued recallEpisodic memoryCognitive psychologyTask (project management)DistractionAudiologyCognitionFree recallDevelopmental psychologyMedicineNeuroscienceCommunication

Abstract

fetched live from OpenAlex

OBJECTIVE: Forgetting names is a common memory concern for people with amnestic mild cognitive impairment (aMCI) and is related to explicit memory deficits and pathological changes in the medial temporal lobes at the early stages of Alzheimer's disease (AD). In the current experiment, we tested a unique method to improve memory for face-name associations in people with aMCI involving incidental rehearsal of face-name pairs. METHOD: Older adults with aMCI and age- and education-matched controls learned 24 face-name pairs and were tested via immediate cued recall with faces as cues for associated names. During a 25- to 30-min retention interval, 10 of the face-name pairs reappeared as a quarter of the items on a seemingly unrelated 1-back task on faces, with the superimposed names irrelevant to the task. After the delay, surprise delayed cued recall and forced-choice associative recognition tests were administered for the face-name pairs. RESULTS: Both groups showed reduced forgetting of the names that repeated as distraction and enhanced recollection of these pairs. CONCLUSIONS: The results demonstrate that passive methods to prompt automatic retrieval of associations may hold promise as interventions for people with early signs of AD.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.031
GPT teacher head0.337
Teacher spread0.306 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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