The effects of facial expressions on cycling performance: An embodied cognition approach
Bibliographic record
Abstract
Embodied Cognition (EC) refers to how the mind is understood in the context of its relationship to a physical body that interacts with the world (Wilson, 2002) and has been applied across various domains. The impact of EC on Ratings of Perceived Exertion (RPE) and athletic performance have been examined, however, to the best of our knowledge, no study has examined the link between EC, RPE, actual exertion, and performance during intense physical activity via the inducement of facial expressions. The aim of the present study was to investigate whether the embodiment of specific facial expressions had an effect on participants' RPE, actual exertion (heart rate), and performance (kilometers travelled) during a 20 minute cycling task on a stationary bicycle. In this ongoing research, introductory psychology students from the University of New Brunswick participated in a repeated measures design, which involved the completion of three 20-minute cycling sessions in three conditions (i.e., smiling, grimacing, and neutral face) within a two-week period. Participants were randomized to an order and depending on their condition were 1) prompted to produce a smile, a grimace, or a neutral facial expression; 2) asked to rate their perceived exertion, and 3) had their heart rate measured at various time intervals. The distance cycled at a fixed resistance over 20 minutes was the dependent variable. Through the implementation of three separate repeated measures ANOVA's, no significant differences were found across conditions for RPE, heart rate, or distance.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".