Framing high or low mental construal level improves performance in various sport tasks
Bibliographic record
Abstract
The purpose of this research was to study the effect of mental construal on sport performance in order to closer examine the cognitive processes involved in imagery related interventions. We looked at the interaction of task context (i.e. different sport types) and mental construal as conceptualized by Construal Level Theory (CLT; Liberman et al., 2002). CLT suggests that mental representations are created on different levels of abstraction, dependent on and influenced by the distance of the provided information (e.g. spatial, temporal, social). Previous research has examined distance in relation to task demands: Abstraction seems to undermine analytical problem solving but increase motivation through emphasis on centrality, whereas concreteness can enhance response inhibition and flexibility of mental representations (for review, see Trope & Liberman, 2010). Based on these findings, we conducted a within-between mixed-design experiment. We measured the performance of 29 varsity athletes (15 table tennis players/14 track&field - jumpers and throwers) at baseline, and then again after high and low construal level frames. We also collected information on individual construal level, and the athlete’s perceived satisfaction with both the intervention as well as their performance. The interaction of construal level and task was found to be significant, with ‘fit’ effects in table tennis/low level frame and track&field/high level frame. A Friedman repeated-measures test resulted in X²(2, N=29)=8.36, p=.015. Post-hoc tests revealed a difference in fit/non-fit comparison (p=.008) and in the fit/baseline comparison (p=.012). This means that table tennis players under a low level frame performed better than no/high level frame, and jumpers and throwers improved in the high level condition. With our study, we illustrated the interaction of sport type and imagery construal level, as it affects athletes’ performance compared to baseline. We discuss implications and propose future research for mental construal and its effects on performance.
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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 | 0.002 |
| 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) | 0.003 | 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 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".