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Record W2061104977 · doi:10.2466/22.pms.113.4.19-37

Task Characteristics and the Contextual Interference Effect

2011· article· en· W2061104977 on OpenAlexaff
L. Darren Kruisselbrink, Geraldine H. Van Gyn

Bibliographic record

VenuePerceptual and Motor Skills · 2011
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of VictoriaAcadia University
Fundersnot available
KeywordsTask (project management)PsychologyMotor learningInterference (communication)Differential effectsCognitive psychologyRandom effects modelDifferential (mechanical device)Function (biology)Social psychologyComputer scienceMeta-analysisNeuroscience

Abstract

fetched live from OpenAlex

The purpose of this exploratory study was to examine the influence of blocked and random practice on the acquisition and retention of a criterion multisegment motor task practiced alongside either two similar-distractors tasks or two different-distractors tasks. The random-practice similar-distractors group made more decision-making errors and performed the criterion task more slowly than the blocked-practice similar-distractors group during the acquisition phase. Following a brief filled retention interval, the blocked-practice similar-distractors group demonstrated a loss of acquired performance capabilities, whereas the random-practice similar-distractors group did not. The blocked- and random-practice different-distractors groups performed similarly throughout the experiment. Results are interpreted within Glenberg's component-levels theory, in which it was argued that random practice must stimulate the differential storage of multilevel contextual components associated with the multiple motor tasks being learned to produce a contextual interference effect. The theoretical and practical implications of differential storage versus nonrepetition as a function of random practice are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.961
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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