Motivation and self‐regulation: The role of want‐to motivation in the processes underlying self‐regulation and self‐control
Bibliographic record
Abstract
Abstract Research on self‐regulation has largely focused on the idea of effortful self‐control, which assumes that exerting willpower will lead to greater success. However, in recent years, research has challenged this perspective and instead proposes that effortless self‐regulation is more adaptive for long‐term goal pursuit. Taking into consideration the burgeoning literature on effortless self‐regulation, here we propose that motivation—or the reasons why we pursue our goals—plays an integral role in this process. The objective of the present paper is to highlight how motivation can play a role in how self‐regulation unfolds. Specifically, we propose that pursuing goals because you want‐to (vs. have‐to ) is associated with better goal attainment as a function of experiencing less temptations and obstacles. While the reason why want‐to motivation relates to experiencing fewer obstacles has yet to be thoroughly explored, here we propose some potential mechanisms drawing from recent research on self‐regulation. We also provide recommendations for future research, highlighting the importance of considering motivation in the study of self‐regulatory processes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".