Self-determination, self-regulation, and the brain: Autonomy improves performance by enhancing neuroaffective responsiveness to self-regulation failure.
Bibliographic record
Abstract
The importance of autonomous motivation in improving self-regulation has been a focal topic of motivation research for almost 3 decades. Despite this extensive research, however, there has not yet been a mechanistic account of how autonomous motivation works to boost self-regulatory functioning. To address this issue, we examined the role of autonomy in 2 basic self-regulation tasks while recording a neural signal of self-regulation failure (i.e., the error-related negativity; ERN). Based on the notion that autonomy improves self-regulation, we anticipated that autonomous motivation would enhance neuroaffective responsiveness to self-regulatory failure and thus improve performance relative to controlled motivation. In Study 1 (N = 43), we found that trait autonomy was positively associated with self-regulatory performance and that this effect was mediated by increased brain-based sensitivity to self-regulation failure, as demonstrated by a larger ERN. Study 2 (N = 55) replicated and extended this pattern using an experimental manipulation of autonomy; when autonomous motivation was contextually supported, task performance increased relative to those for whom autonomy was undermined and those in a neutral condition. In addition, this effect was mediated by both increased perceptions of autonomy and larger ERN amplitudes. These findings offer deeper insight into the links among motivational orientation, brain-based performance monitoring, and self-regulation.
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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 | 0.001 |
| 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".