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Record W3209073928 · doi:10.1080/13825585.2021.1998320

Implicit processes enhance cognitive abilities in mild cognitive impairment

2021· article· en· W3209073928 on OpenAlexaff
Gillian Rowe, Angela K. Troyer, Kelly J. Murphy, Renée K. Biss, Lynn Hasher

Bibliographic record

VenueAging Neuropsychology and Cognition · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of WindsorBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychologyCognitionTask (project management)Priming (agriculture)Cognitive psychologyCognitive impairmentImplicit memoryElementary cognitive taskPopulationWord (group theory)Developmental psychologyNeuroscienceLinguisticsMedicine

Abstract

fetched live from OpenAlex

Previous work has shown that older adults with typical age-related memory changes (i.e., without cognitive impairment) pick up irrelevant information implicitly, and unknowingly use that information when it becomes relevant to a later task. Here, we address the possibility that implicit processes play a similarly beneficial role in the cognitive abilities of individuals with amnestic mild cognitive impairment (aMCI). Twenty-two individuals with aMCI and 22 matched controls participated in a picture judgment task while instructed to ignore distractions in the form of word/non-word letter strings. Memory for the distracting words was later tested with a word-fragment completion task. Both groups showed a priming effect, that is, they were significantly more likely to solve fragments of previously presented than non-presented words. However, the aMCI group had significantly higher scores than the older adults without cognitive impairment, t(42) = 2.16, p < .05, Cohen’s d = 0.67. Our findings suggest that individuals with aMCI can enhance their performance on an explicit cognitive task, in this case, word-fragment completion, if previously exposed to the relevant information implicitly, opening up possible interventions aimed at this population.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.350
Teacher spread0.331 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAging Neuropsychology and CognitionSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207