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Record W2981454211

Cerebellar tDCS alters recalibration but not realignment during visuomotor learning

2019· article· en· W2981454211 on OpenAlexaff
Bryton Cordeiro

Bibliographic record

VenueStudent Research Proceedings · 2019
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsTranscranial direct-current stimulationPrism adaptationMotor learningCerebellumPsychologyNeuroscienceStimulationScalpBrain stimulationMotor areaPhysical medicine and rehabilitationMotor controlAdaptation (eye)AudiologyMedicineAnatomy
DOInot available

Abstract

fetched live from OpenAlex

Previous research suggests that the cerebellum is a brain region that is important for motor control as well as motor learning. To further examine the role of the cerebellum in motor learning we used transcranial direct current stimulation (tDCS) – a non-invasive brain stimulation technique in which a weak electrical current is applied through the scalp to alter the baseline activity of neurons under the stimulating electrode. In the current study, participants (n=60) completed a prism adaptation task in which they pointed to targets on a touch screen with their right hand before, during, and after adaptation to 17° rightward shifting prisms while undergoing either anodal (+), cathodal (-) or sham tDCS. Our results indicate that, during prism adaptation, cathodal (-) stimulation significantly reduced pointing errors (i.e., recalibration) during the first 40 pointing trials compared to both anodal (+) and sham stimulation. However, there were no significant differences in adaptation after-affects (i.e., spatial realignment) between the three groups. Overall, our findings offer further insight into the role of the cerebellum in motor learning and how tDCS might be used to enhance recovery in patients with cerebellar brain damage.   Faculty Mentor: Christopher Striemer Department: Psychology

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)0.001
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 score0.375
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.115
GPT teacher head0.396
Teacher spread0.281 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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