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Record W2054714155 · doi:10.1037//1076-898x.8.4.222

Models of performance in learning multisegment movement tasks: Consequences for acquisition, retention, and judgments of learning.

2002· article· en· W2054714155 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 Experimental Psychology Applied · 2002
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDreyfus model of skill acquisitionMotor learningComputer scienceLearning curveSequence learningCognitive psychologyMotor skillMatching (statistics)PsychologyArtificial intelligenceMachine learningDevelopmental psychology

Abstract

fetched live from OpenAlex

Participants learned different keystroke patterns, each requiring that a key sequence be struck in a prescribed time. Trials of a given pattern were either blocked or interleaved randomly with trials on the other patterns and before each trial modeled timing information was presented that either matched or mismatched the movement to be executed next. In acquisition, blocked practice and matching models supported better performance than did random practice and mismatching models. In retention, however, random practice and mismatching models were associated with superior learning. Judgments of learning made during practice were more in line with acquisition than with retention performance, providing further evidence that a learner's current ease of access to a motor skill is a poor indicator of learning benefit.

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)0.000
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 score0.030
Threshold uncertainty score0.369

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 head0.063
GPT teacher head0.308
Teacher spread0.245 · 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