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Record W2047441772 · doi:10.1080/00222890009601377

The Role of Scheduling in Learning Through Observation

2000· article· en· W2047441772 on OpenAlexafffund
Janice Deakin, Luc Proteau

Bibliographic record

VenueJournal of Motor Behavior · 2000
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité de MontréalQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMotor learningCognitionRepresentation (politics)ScheduleTask (project management)Cognitive psychologyComputer sciencePsychologyArtificial intelligenceMachine learningNeuroscience

Abstract

fetched live from OpenAlex

In the 2 experiments reported in the present article, participants (N = 40, Experiment 1; N = 60, Experiment 2) learned to solve complex puzzles under different schedules of physical practice, observation, or a combination of the two. The results of both studies indicated that observation, in the absence of any physical practice, allows the development of an accurate but relatively nonfunctional cognitive representation. The data suggest that, even when the motor demands are minimal, the functional significance of the cognitive representation is not maximally realized until physical interaction with the task is possible. Thus, providing the participant with an interspersed practice schedule during acquisition enables that interaction to occur, thereby allowing the absolute number of physical practice trials to be reduced and replaced by observation trials, but leading to equivalent 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)0.003
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.279
Teacher spread0.240 · 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".

Quick stats

Citations42
Published2000
Admission routes2
Has abstractyes

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