Bibliographic record
Abstract
Abstract Modeling and imagery are distinct but related psychological skills. However, despite sharing similar cognitive processes, they have traditionally been investigated separately. While modeling has shown similar psychological and physical performance benefits as imagery, it remains an understudied technique within applied sport psychology. Social cognitive and direct perception approaches remain often-used explanations for the effectiveness of modeling on skill acquisition; however, emergent neuropsychological explanations provide evidence to support these earlier theories and a link to the imagery literature. With advances in technology and the development of applied frameworks, there is renewed interest in exploring modeling effects and how they parallel imagery use in applied settings. Specifically, modeling research has expanded beyond controlled laboratory settings to explore the effect of various theoretical models on motor performance and related cognitions within practice and competitive settings. The emergence of affordable video editing technology makes it easy for coaches and athletes to incorporate modeling into practice. The accessibility of video technology has sparked applied research on how various forms of modeling influence motor performance and cognitions, such as confidence and motivation. These applied investigations demonstrate the complementary nature of modeling and imagery in enhancing sport performance and skill acquisition, while highlighting the challenges in separating modeling and imagery effects. Both literatures offer possibilities for new methodological approaches and directions for studying these psychological skills in tandem as well as independently. Thus, there is much that imagery and modeling researchers can learn from each other in sport and other performance settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".