An appraisal framework to understand why negative affect is both good and bad for self-control
Bibliographic record
Abstract
Self-control and emotion have often been cast in mutual opposition. However, recent accounts suggest that emotion and cognition overlap so greatly that they are largely inseparable. Despite this overlap, existing perspectives provide a seemingly paradoxical account of the integration of control and emotion, where negative affect is viewed as both good and bad for self-control. Here, we present an appraisal based framework that aims to reconcile these perspectives by closely considering the evaluative and homeostatic principles that unite emotion and self-control. Incorporating diverse appraisal processes from affective science into existing cybernetic models of self-control, we suggest that all variation in self-control is motivated by the overarching goal of the organism to maintain a positive hedonic homeostasis in its environment. Depending on the amount of value that ongoing appraisal processes see in the current goal, this might mean responding to goal threatening, aversive events with increased self-control and vigour, or, alternatively , disengaging from the current goal and pursuing more pleasurable activities. These strategies simultaneously serve the need to flexibly manage priorities between different goals, and, perhaps more fundamentally, maintain affective homeostasis within the immediate environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".