New Directions in Self-Regulation: The Role of Metamotivational Beliefs
Bibliographic record
Abstract
Research on self-regulation has primarily focused on how people exert control over their thoughts, emotions, and behavior. Less attention has been paid to the ways in which people manage their motivational states in the service of achieving valued goals. In this article, we explore an emerging line of research that focuses on people’s beliefs about their own motivation (i.e., their metamotivational knowledge), as well as the influence these beliefs have on their selection of regulatory strategies. In particular, we review evidence showing that people are often quite sensitive to the fact that distinct motivational states (e.g., eagerness vs. vigilance) are adaptive for different kinds of tasks. We also discuss how other metamotivational beliefs are inaccurate on average (e.g., beliefs about how rewards affect intrinsic motivation). Finally, we consider the implications of metamotivation research for the field of self-regulation and discuss future directions.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.022 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".