Effects of performance feedback on self-efficacy and physical performance are moderated by self-control strength depletion
Bibliographic record
Abstract
Self-control is a key determinant of physical endurance (e.g., Bray et al., 2008). According to control theory (Carver & Scheier, 2011), performance feedback influences self-control such that when people underperform (low feedback) they increase effort, while those who overperform (high feedback) withdraw effort. This perspective clashes with self-efficacy theory (Bandura, 1997) which proposes that positive performance increases self-efficacy and performance. The purpose of this study was to investigate the independent and interactive effects of self-control strength depletion (Baumeister, 2014) and feedback on self-efficacy and endurance performance. Participants (N = 78) performed two isometric endurance handgrip trials separated by a congruent (no depletion) or incongruent (depletion) Stroop task and a normative (high/low/no) feedback manipulation regarding their performance on the first handgrip trial. A 2x3 ANOVA of the change in endurance performance from trial 1-2 produced several significant effects. Of primary interest, there was a significant interaction between depletion and feedback (p < .001). In the no depletion conditions, high feedback led to lower self-efficacy and performance while low feedback led to higher self-efficacy and performance. However, the reverse was seen in the self-control depletion groups. The effects of feedback on performance in the control conditions support control theory as well as self-efficacy theory insofar as self-efficacy was positively associated with performance. The results from the depletion conditions suggest performance feedback information is processed differently when self-control resources are compromised. Researchers and practitioners should be considerate of participants' level of self-control depletion when providing performance feedback to manipulate self-efficacy and exercise performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".