Motor expertise and performance in sport-specific priming tasks: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective The present study aimed to summarize findings relevant to the influence of motor expertise on performance in sport-specific priming tasks and to examine potential moderators of this effect. Methodology Data were collected from the China National Knowledge Infrastructure (CNKI), PsychInfo, Medline, Google Scholar, Web of Science, Baidu Scholar and Sport Discus and Dissertation Abstracts Online databases from January 1999 to April 2020, supplemented by manual bibliographies and meeting minutes. Stata software was used to perform the meta-analysis. Study quality was evaluated systematically using the Newcastle-Ottawa scale (NOS). Standard mean differences (SMDs) with 95% CIs were calculated with a random-effects model. The Cochrane Q test and I 2 statistic were used to evaluate heterogeneity. Begg funnel plots and Egger tests were conducted to assess publication bias. Results Nine articles (including 12 studies) were ultimately included in the meta-analysis. Significant heterogeneity was observed among these studies ( Q = 44.42, P < 0.001, I 2 = 75.2%) according to random-effects modeling. The results showed an overall advantage in favor of motor experts in sport-specific priming tasks (SMD = −1.01, 95% CI [−1.41 to −0.61]). However, the magnitude of that effect was moderated by sport type (interceptive sports/independent sports) and prime stimulus type (subliminal stimulus/supraliminal stimulus). No publication bias was detected by the Begg and Egger tests. Conclusions In general, compared with those of nonexperts, the responses of motor experts’ responses to a target stimulus are easier and faster when the prime and target stimuli are consistent. However, the magnitude of this effect is moderated by sport type and prime stimulus type.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".