Examining the Relationship Between Exercise-Related Cognitive Errors, Exercise Schema, and Implicit Associations
Bibliographic record
Abstract
To better understand exercise-related cognitive errors (ECEs) from a dual processing perspective, the purpose of this study was to examine their relationship to two automatic exercise processes. It was hypothesized that ECEs would account for more variance than automatic processes in predicting intentions, that ECEs would interact with automatic processes to predict intentions, and that exercise schema would distinguish between different levels of ECEs. Adults (N = 136, Mage = 29 years, 42.6% women) completed a cross-sectional study and responded to three survey measures (ECEs, exercise self-schema, and exercise intentions) and two computerized implicit tasks (the approach/avoid task and single-category Implicit Association Test). ECEs were not correlated with the two implicit measures; however, ECEs moderated the relationship between approach tendency toward exercise stimuli and exercise intentions. Exercise self-schema were differentiated by ECE level. This study expands our knowledge of ECEs by examining their relationship to different automatic and reflective processes.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".