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Record W4385551265 · doi:10.31234/osf.io/x4652

Fundamental processes in sensorimotor learning: Reasoning, Refinement, and Retrieval

2023· preprint· en· W4385551265 on OpenAlexaff
Jonathan S. Tsay, Hyosub E. Kim, Samuel D. McDougle, Jordan A. Taylor, Adrian M. Haith, Guy Avraham, John W. Krakauer, Anne Collins, Richard B. Ivry

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerspective (graphical)Process (computing)CognitionAction (physics)Computer scienceContext (archaeology)Cognitive scienceMotor learningIntersection (aeronautics)Unitary stateCognitive psychologyPsychologyArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

[Now published in eLife: https://elifesciences.org/articles/91839] Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: Reasoning, the process of understanding action-outcome relationships; Refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and Retrieval, the process of inferring the context and recalling a control policy. We anticipate that this “3R” framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.998

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 integrity0.0000.001
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 head0.080
GPT teacher head0.358
Teacher spread0.278 · 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.

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".

Quick stats

Citations21
Published2023
Admission routes1
Has abstractyes

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