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Record W2105279348 · doi:10.1002/hbm.20605

Age‐related changes in the cerebral substrates of cognitive procedural learning

2008· article· en· W2105279348 on OpenAlexaboutno aff
Valérie Hubert, Hélène Beaunieux, Gaël Chételat, Hervé Platel, Brigitte Landeau, Fausto Viader, Béatrice Desgranges, Francis Eustache

Bibliographic record

VenueHuman Brain Mapping · 2008
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionPsychologyAssociative learningNeuroscienceCerebellumPosterior cingulatePsychomotor learningCognitive psychologyEffects of sleep deprivation on cognitive performanceAudiologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Cognitive procedural learning occurs in three qualitatively different phases (cognitive, associative, and autonomous). At the beginning of this process, numerous cognitive functions are involved, subtended by distinct brain structures such as the prefrontal and parietal cortex and the cerebellum. As the learning progresses, these cognitive components are gradually replaced by psychomotor abilities, reflected by the increasing involvement of the cerebellum, thalamus, and occipital regions. In elderly subjects, although cognitive studies have revealed a learning effect, performance levels differ during the acquisition of a procedure. The effects of age on the learning of a cognitive procedure have not yet been examined using functional imaging. The aim of this study was therefore to characterize the cerebral substrates involved in the learning of a cognitive procedure, comparing a group of older subjects with young controls. For this purpose, we performed a positron emission tomography activation study using the Tower of Toronto task. A direct comparison of the two groups revealed the involvement of a similar network of brain regions at the beginning of learning (cognitive phase). However, the engagement of frontal and cingulate regions persisted in the older group as learning continued, whereas it ceased in the younger controls. We assume that this additional activation in the older group during the associative and autonomous phases reflected compensatory processes and the fact that some older subjects failed to fully automate the procedure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.429

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 science0.0000.000
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 head0.145
GPT teacher head0.310
Teacher spread0.164 · 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

Citations18
Published2008
Admission routes1
Has abstractyes

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