The Effects of Deception on Maximal Strength, Goals, and Physical Self-Efficacy
Bibliographic record
Abstract
Deceptive feedback involves offering altered performance results to athletes with the intention of eliciting greater physical output. The use of feedback and feedforward mechanisms used to predict a performance endpoint is referred to as teleoanticipation. The interpretation of physciological and psychological effects of stimuli upon is the basis for current and future performances. The purpose of the present study was to investigate the effects of false positive feedback, of varying percentages, on maximal strength, physical self-efficacy, and strength goals through the lens of teleoanticipation. Recreational lifters (n=17) were tested for one repetition maximum (1RM) leg press scores, future goal weights (G), and physical self-efficacy (SE), over the course of two orientation sessions and five separate test sessions. A baseline of 1RM strength was established during the first test control session (TC). Deceptive feedback was given on the subsequent three sessions and consisted of loads that were 5% (T+5), 10% (T+10) or 15% (T+15) above the loads reported to participants during each session. The full extent of deception was revealed on the final session of testing (TF). There were significant differences between the trials for 1RM measures; TC was significantly different from the T+5, T+10, and TF. Results for G revealed significance for all trials compared to TC but no differences were found in self-esteem. This data suggests that deception may enhance 1 RM measures, negatively impact goal setting, but not affect physical self-efficacy.
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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) | 0.000 | 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 | 0.001 |
| 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".