"Pay the piper": Autonomous motivation takes a toll on self-control
Bibliographic record
Abstract
When people are autonomously regulated they experience a heightened sense of vitality, whereas controlled regulation diminishes vitality (Ryan & Deci, 2008). Research shows that autonomous regulation helps people override self-control depletion (Muraven, 2008). However, from a resource allocation perspective (Beetie & Lane, 2012) greater autonomous motivation may prompt immediate access to limited energy reserves, but deplete self-control resources such that performance will suffer more distally. The purpose of this study was to investigate if heightened autonomous motivation would be associated with better self-control performance in the short term, but worse performance in the longer term. Participants (N = 72) completed two, sequential, sub-maximal (50%) endurance isometric handgrip trials. Before the first trial they were provided with autonomously supportive (n = 37) or controlling (n = 35) instructions. As expected, those who received autonomy support prior to the first trial performed better than controls (p = .012, d = 0.62). However, on the subsequent trial, the autonomy support group fared significantly worse than controls (p = .001, d = 0.82). Thus, autonomous motivation may stimulate vitality but it comes at a cost in that motivated utilization of energy reserves eventually leads to greater resource depletion. Results have important implications for self-regulated tasks such as endurance sports that involve protracted investment of self-control over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".