Online Attentional-Focus Manipulations in a Soccer-Dribbling Task: Implications for the Proceduralization of Motor Skills
Bibliographic record
Abstract
A focus of attention on the step-by-step control of a skill has been shown to be detrimental to experts' performance but to have no significant effect on novices' performance (e.g., S. L. Beilock, T. H. Carr, C. MacMahon, & J. L. Starkes, 2002), contrary to the results of manipulations of the direction of attentional focus (e.g., G. Wulf, M. Höss, & W. Prinz, 1998). In previous studies, researchers have not separated the focus of attention from the nature of the instruction provided or the skill level of the participants. In the present experiment, 10 skilled and 10 less skilled soccer players dribbled a ball after receiving instructions directing attention to an internal, skill-relevant feature (foot); an internal, skill-irrelevant feature (arm); or a skill-irrelevant task (word-monitoring). Performance was evaluated in relation to a no-attentional-focus control condition. For skilled performers, an internal focus on the arms and feet interfered with performance. For less skilled performers, an internal, yet skill-relevant, focus of attention (foot) did not degrade performance, whereas attention to the arms and word monitoring had a detrimental effect. No significant differences were observed across the three attentional manipulations when the skilled performers used the nondominant foot. The results generally supported the deautomization of skills hypothesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".