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Record W2005220178 · doi:10.1080/87565640701361120

Aging Affects Motor Skill Learning When the Task Requires Inhibitory Control

2007· article· en· W2005220178 on OpenAlexafffund
Julie Brosseau, Marie-Julie Potvin, Isabelle Rouleau

Bibliographic record

VenueDevelopmental Neuropsychology · 2007
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité du Québec à MontréalCentre hospitalier de l'Université Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyTask (project management)Cognitive psychologyDevelopmental psychologyTracingMotor learningAssociation (psychology)Motor skillInhibitory controlRepetition (rhetorical device)AudiologyCognitionNeuroscience

Abstract

fetched live from OpenAlex

Few studies have examined the influence of aging on motor skill learning (MSL) tasks involving different skills and conditions. Two tasks, each including two different conditions (repeated and nonrepeated), were used: (a) the Mirror Tracing task, requiring the inhibition of an overlearned response and the learning of a new visuomotor association, and (b) the Pursuit Tracking task, mainly requiring the processing of visuospatial stimuli. We hypothesized that older participants would benefit as much as younger participants from the stimuli repetition and that they would exhibit a slower learning rate exclusively on the Mirror Tracing task. As expected, older and younger participants' MSL were not differentially affected by task conditions. They also showed a similar learning rate on the Pursuit Tracking task and a subgroup of older participants exhibited MSL difficulties on the Mirror Tracing task. Problems in the inhibitory control of competing motor memories could explain these age-related MSL difficulties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2007
Admission routes2
Has abstractyes

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