Location versus task relevance: Not all internal focus of attention instructions are created equal
Bibliographic record
Abstract
After 16 years of study, the finding that an internal focus of attention negatively affects performance relative to an external focus is replicable in many contexts (Wulf 2013). Diminished results during an internal focus of attention are attributed to interference with normal automaticity in the body (Wulf, McNevin & Shea, 2001). As focus is directed internally, the automatic processes which normally produce efficient movements are disturbed, resulting in less efficient movement patterns. When an external focus of attention is used, automaticity is not constrained and improved performance is noted. What remains unclear is whether the specificity of internally focused task instructions may impact task performance. In the present study we applied behavioural, kinematic and neurophysiological outcome measures to assess focus of attention. Novice (N=11) and skilled golfers (N=13) were recruited to perform a putting task to two targets (3m and 5m). Three focus conditions were used: external (target), internal swing (hands on the club and position of elbows) and internal stance (feet evenly balanced). Movement kinematics of the golf club were recorded with an Optotrak 3D Investigator (300Hz). Electromyography (EMG) of tibialis anterior and extensor carpi radialis (ECR) was recorded under all focus conditions. Our findings provide evidence that when novice participants used the internal movement focus of attention while putting to the 5m target, accuracy, kinematics of the putting movement, and variability of EMG activity in ECR were all adversely affected. Instructions which were internal but anatomically distal to the primary movement during the task appeared to have an effect similar to an external focus of attention and did not adversely affect outcomes. Differences in focus of attention findings between novice and skilled golfers are interpreted using the challenge point framework (Guadagnoli and Lee 2003).
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".