Putting Attention on the Spot in Coaching: Shifting to an External Focus of Attention With Imagery Techniques to Improve Basketball Free-Throw Shooting Performance
Bibliographic record
Abstract
Attentional focus is an area that has garnered considerable attention in the sport psychology and motor performance literature. This is unsurprising given that attentional focus has been directly linked to performance outcomes and is susceptible to coaching input. While research has amassed supporting benefits of an external focus of attention (EFA) on motor performance using verbal instruction, other studies have challenged the notion that an EFA is more beneficial than an internal focus of attention (IFA) for sport-related performance. Further, it is unclear what type of instructions may serve to direct an athlete to an EFA and, in particular, if coaching can utilize imagery to orient an athlete toward an EFA. In the present exploratory study, we evaluate the effectiveness of instruction to improve free-throw shooting performance with an emphasis on an EFA brought about by implementing techniques borrowed from the imagery literature. This was tested relative to an alternate approach with an IFA induced through an emphasis on technique, devised to more closely resemble input typical of coach-to-athlete instruction. Twenty-five male and female university basketball players completed both conditions in a fully counterbalanced within-subject design. Results confirmed that participants in the EFA imagery condition had greater shooting accuracy than in the IFA technique condition. The study provides initial evidence that EFA coaching can borrow from imagery techniques, though future research should elucidate the underlying mechanisms of the effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".