Understanding and Improving Emotion Regulation: Lessons from Psychological Science and the Humanities
Bibliographic record
Abstract
Abstract Most psychology researchers define emotion regulation as manipulating the quality, duration, of intensity of emotions. This definition often assumes that the goal of life is to maximize positive emotions and minimize negative ones (hedonism). To understand the limitations of this definition, and the possibility of other definitions of emotion regulation, one must look to the humanities. Philosophy and research can be used to discuss the paradox of hedonism: direct attempts to feel good often lead to feeling bad. Rather than emotion regulation being about feeling good, the authors suggest it can be about doing good. They discuss how people can use the study of the humanities to improve five emotion-regulation skills: (1) the ability to guide behavior based on value and virtue (ethics, moral philosophy); (2) the use of reasoning (e.g., philosophy, logic) in emotional situations, and the ability to recognize the limits of reasoning and to let go of it (e.g., Eastern philosophy focused on mindfulness and paradox); (3) awareness of emotions; (4) the ability to broaden and build one’s emotional responses; and (5) the ability to take perspective of the self and others, a skill that can be improved by reading history and literature. The authors briefly discuss the dangers of a feel-good approach to emotion regulation for society. The humanities allow one to see that most acts of prejudice, discrimination, and indifference to suffering stem from a desire to feel good (safe, guilt free, powerful, prestigious) at the expense of others.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".