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Record W2161520815 · doi:10.1371/journal.pone.0013330

Neural Coding of Movement Direction in the Healthy Human Brain

2010· article· en· W2161520815 on OpenAlexafffund
Christopher D. Cowper-Smith, Esther Yuet Ying Lau, Carl Helmick, Gail A. Eskes, David A. Westwood

Bibliographic record

VenuePLoS ONE · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health ResearchDalhousie UniversityNova Scotia Health Research FoundationDalhousie Medical Research Foundation
KeywordsNeuroscienceFunctional magnetic resonance imagingSMA*NeurophysiologyCerebellumSupplementary motor areaPrimary motor cortexMotor cortexDorsolateral prefrontal cortexPosterior parietal cortexPsychologyPrefrontal cortexBiologyComputer scienceCognition

Abstract

fetched live from OpenAlex

Neurophysiological studies in monkeys show that activity of neurons in primary cortex (M1), pre-motor cortex (PMC), and cerebellum varies systematically with the direction of reaching movements. These neurons exhibit preferred direction tuning, where the level of neural activity is highest when movements are made in the preferred direction (PD), and gets progressively lower as movements are made at increasing degrees of offset from the PD. Using a functional magnetic resonance imaging adaptation (fMRI-A) paradigm, we show that PD coding does exist in regions of the human motor system that are homologous to those observed in non-human primates. Consistent with predictions of the PD model, we show adaptation (i.e., a lower level) of the blood oxygen level dependent (BOLD) time-course signal in M1, PMC, SMA, and cerebellum when consecutive wrist movements were made in the same direction (0° offset) relative to movements offset by 90° or 180°. The BOLD signal in dorsolateral prefrontal cortex adapted equally in all movement offset conditions, mitigating against the possibility that the present results are the consequence of differential task complexity or attention to action in each movement offset condition.

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)0.000
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 score0.198
Threshold uncertainty score0.162

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.071
GPT teacher head0.277
Teacher spread0.206 · 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

Citations26
Published2010
Admission routes2
Has abstractyes

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