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Record W2120725947 · doi:10.1123/jsep.25.4.484

Motor Performance as a Function of Audience Affability and Metaknowledge

2003· article· en· W2120725947 on OpenAlexaff
Jon Law, Rich S.W. Masters, Steven R. Bray, Frank F. Eves, Isabella Bardswell

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2003
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsAdversarial systemPsychologyTask (project management)Function (biology)Control (management)Social psychologyTest (biology)Style (visual arts)Cognitive psychologyArtificial intelligenceComputer scienceVisual arts

Abstract

fetched live from OpenAlex

Butler and Baumeister (1998) suggested that performance decrement of a difficult skill-based task occurring only in the presence of a supportive audience could be explained by “a cautious performance style” (p. 1226). A potential alternative explanation stems from Masters’ (1992) contention that skill failure under pressure occurs when performers attempt to control motor performance using explicit knowledge. It was proposed that a skill acquired with minimal metaknowledge (i.e., a limited explicit knowledge base) would remain robust regardless of audience type. To test this hypothesis, a table tennis shot was learned with either a greater or a lesser bank of explicit task knowledge. Performance was subsequently assessed in the presence of observation-only audiences, supportive audiences, and adversarial audiences. Consistent with hypotheses, supportive audiences induced performance decrement in the explicit-learning group only. It was argued that supportive audiences engender higher levels of internally focused attention than do adversarial or observation-only audiences, increasing the chance of disruption to skill execution when performance characteristics involve a large amount of explicit processing.

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)0.001
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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.314
Teacher spread0.294 · 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

Citations62
Published2003
Admission routes1
Has abstractyes

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