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Record W2082356126 · doi:10.1080/00222895.2010.526453

Modularity for Sensorimotor Control: Evidence and a New Prediction

2010· article· en· W2082356126 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Motor Behavior · 2010
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsModular designModularity (biology)Computer scienceMotor controlCurse of dimensionalityCognitive scienceControl engineeringPsychologyArtificial intelligenceNeuroscienceProgramming languageEngineering

Abstract

fetched live from OpenAlex

By combining a few modules, the CNS may learn new control policies quickly and efficiently. Support for a modular organization of the motor system has recently come from the observation of low dimensionality in the motor commands. However, stronger evidence would come from testing the predictions on the effect of an intervention on the mechanisms required to implement a modular controller. Thus, the authors propose to test the predictions of modularity on motor adaptation. They argue that unlike a nonmodular controller, a modular controller must adapt faster to a perturbation that is compatible with the modules (i.e., one that can be compensated by reusing the same modules), than to an incompatible perturbation (i.e., one that requires new modules).

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.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.077
GPT teacher head0.305
Teacher spread0.229 · 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