The effects of cognitive general imagery training on decision making abilities in curling: A single-subject multiple baseline approach
Bibliographic record
Abstract
The purpose of the present study was to examine the effects of an 8-week cognitive general (CG) imagery training program on the decision making abilities of novice curlers. While previous research has examined the effects of CG imagery on sport performance, results have been mixed (e.g., Guillot et al., 2009; Munroe-Chandler et al., 2005). A single-subject multiple baseline design was used to track curling strategy performance of three novice curlers (Mage = 19.33, SD = 3.06) across the 18-week study period. The study consisted of three phases: baseline, intervention and post-intervention. All athletes completed a minimum of four weeks of baseline testing, in which they completed the Curling Strategy Assessment Tool once a week. This tool measures the accuracy and speed with which curlers make strategic decisions. Starting at Week 5, participants sequentially began an 8-week guided imagery intervention, taking place once a week for approximately 10 minutes. Following the eight weeks of imagery training, participants simply completed the assessment tool once a week until Week 18. Strategy performance data was analyzed using visual analysis (Kratochwill et al., 2010) and statistical analyses (Gage & Lewis, 2013). Results from both analyses revealed that one out of three participants showed a meaningful improvement in their percentage of correct responses. In terms of the time taken to select the correct response, two of the three participants showed meaningful improvements in their strategy performance through a decrease in response time. Social validation data collected from the participants following the completion of the study provided further support for these findings. Results are discussed in terms of the applicability of the Curling Strategy Assessment Tool as a training instrument to develop strategic decision making abilities in novice curlers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".