Cognitive General Imagery: The Forgotten Imagery Function?
Bibliographic record
Abstract
Abstract It is well known that athletes use mental imagery for five different functions; motivational general-arousal (MG-A; arousal and stress) motivational general-mastery (MG-M; control, mental toughness, and self-confidence), motivational specific (MS; goal-oriented responses), cognitive general (CG; sport-specific strategies), and cognitive specific (CS; sport-specific skills; Hall et al., 1998; Paivio, 1985). While much research has been conducted on the MG-A, MG-M, MS, and CS imagery functions, there has not been as much focus on CG imagery. This is somewhat disheartening since various researchers have pointed out this issue many times (e.g., Hall, 2001). The purpose of this review was to examine the research conducted on CG imagery since the publication of Martin and colleagues’ (1999) applied model of imagery use. A literature search was conducted of published peer-reviewed journal articles using Proquest to identify all studies that have examined CG imagery. Forty-three articles were identified as relevant towards understanding the role of CG imagery in sport. The research findings were discussed in one of two sections depending on the type of study design used (e.g., descriptive/correlational study or imagery intervention). The strengths and weaknesses of the CG imagery studies are discussed. From this review, the authors hope to make researchers aware of the avenues that still need to be explored in regards to CG imagery, as well as provide researchers with potential approaches to answer such questions.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".