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Record W2060297711 · doi:10.1177/000841740106800504

Effects of Cognitive Processes and Task Complexity on Acquisition, Retention, and Transfer of Motor Skills

2001· article· en· W2060297711 on OpenAlexvenueno aff
Tal Jarus, Tzipi Gutman

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2001
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsMotor learningTask (project management)CognitionDreyfus model of skill acquisitionPsychologyMotor skillCognitive psychologyElementary cognitive taskTransfer of trainingComputer scienceDevelopmental psychologyNeuroscienceEngineering

Abstract

fetched live from OpenAlex

This experiment was designed to investigate the effect of cognitive problem-solving operations (termed contextual interference) and complexity of tasks on the acquisition retention and transfer of motor skills. Ninety-six children, ages 7.5-9.5 practiced the task of throwing beanbags under either low contextual interference (blocked practice), high contextual interference (random practice) or medium contextual interference (combined practice). Half of the participants acquired a complex task and the other half a simple task. All participants performed 30 acquisition trials, 9 retention trials and 4 transfer trials. Results indicated that participants who practiced in the blocked practice group did not differ in their performance whether they acquired complex or simple tasks. On the other hand, participants from the random and combined practice groups who acquired simple tasks performed better than those who acquired complex tasks. These findings support the hypothesis that there is a limit to the interference during practice that will benefit retention and transfer, thus creating the contextual interference effect. It seems that the complex-task condition combined with random or combined practice schedule increased the difficulty of acquisition, possibly impeding the cognitive processing during acquisition, thus impairing the learning process.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.311

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 head0.044
GPT teacher head0.317
Teacher spread0.274 · 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 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

Citations53
Published2001
Admission routes1
Has abstractyes

Explore more

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