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Record W4411343614 · doi:10.3390/brainsci15060645

Sensitivity to Instruction Strategies in Motor Learning Is Predicted by Anterior–Posterior TMS Motor Thresholds

2025· article· en· W4411343614 on OpenAlexafffund
Kylee R. Graham, Jessica E. Vander Vaart, Richard Staines, Sean K. Meehan

Bibliographic record

VenueBrain Sciences · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSensitivity (control systems)Motor learningPsychologyPhysical medicine and rehabilitationNeuroscienceMedicineEngineering

Abstract

fetched live from OpenAlex

Background: The impact of exogenous explicit knowledge on early motor learning is highly variable and may be influenced by excitability within the procedural sensorimotor network. Recent transcranial magnetic stimulation (TMS) studies suggest that variability in interneuron recruitment by anterior–posterior (AP) currents is linked to differences in functional connectivity between premotor and motor regions. Objectives: This study used controllable pulse parameter TMS (cTMS) to assess how AP-sensitive interneuron excitability interacts with explicit knowledge to influence motor learning. Methods: Seventy-two participants were grouped as AP-positive (n = 36) and AP-negative groups (n = 36) based on whether an AP threshold could be obtained before reaching maximal stimulator output. A narrow (30 µs) stimulus was employed to target the longest latency corticospinal inputs selectively. Participants then practiced a continuous visuomotor tracking task and completed a delayed retention test. Half of each group received explicit knowledge of a repeated sequence embedded between random sequences. Random sequence tracking performance assessed general sensorimotor efficiency; repeated sequence performance assessed sequence-specific learning. Results: Both AP30-positive participants, with and without explicit knowledge, and the AP30-negative without explicit knowledge demonstrated similar improvements in sensorimotor efficiency driven by offline consolidation. However, AP30-negative participants given explicit instruction exhibited significantly reduced improvement in sensorimotor efficiency, primarily due to impaired offline consolidation. Conclusions: These findings suggest that individuals with low excitability in long-latency AP-sensitive inputs may be more vulnerable to interference from explicit instruction. The current results highlight the importance of accounting for individual differences in interneuron excitability when developing instructional strategies for motor learning.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
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)0.0030.000

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 head0.026
GPT teacher head0.308
Teacher spread0.282 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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