How should I regulate my emotions if I want to run faster?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study investigated the effects of emotion regulation strategies on self-reported emotions and 1600 m track running performance. In stage 1 of a three-stage study, participants (N = 15) reported emotional states associated with best, worst and ideal performance. Results indicated that a best and ideal emotional state for performance composed of feeling happy, calm, energetic and moderately anxious whereas the worst emotional state for performance composed of feeling downhearted, sluggish and highly anxious. In stage 2, emotion regulation interventions were developed using online material and supported by electronic feedback. One intervention motivated participants to increase the intensity of unpleasant emotions (e.g. feel more angry and anxious). A second intervention motivated participants to reduce the intensity of unpleasant emotions (e.g. feel less angry and anxious). In stage 3, using a repeated measures design, participants used each intervention before running a 1600 m time trial. Data were compared with a no treatment control condition. The intervention designed to increase the intensity of unpleasant emotions resulted in higher anxiety and lower calmness scores but no significant effects on 1600 m running time. The intervention designed to reduce the intensity of unpleasant emotions was associated with significantly slower times for the first 400 m. We suggest future research should investigate emotion regulation, emotion and performance using quasi-experimental methods with performance measures that are meaningful to participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it