Happiness depletes me: Seeking happiness impairs limited resources and self‐regulation
Bibliographic record
Abstract
People seek happiness when they try to experience as much positive emotion (and as little negative emotion) as possible. A growing body of research suggests that seeking happiness, rather than resulting in yet more happiness, often leads to negative consequences, like less happiness and less available time. Adding to this happiness paradox, the current research examines whether seeking happiness leads to the impairment of self-regulation due to the depletion of regulatory resources. We first demonstrate that trait-level happiness-seeking is associated with worse self-regulation both via self-report (Study 1) and actual behavior (Study 2). This result is corroborated in subsequent experiments that manipulate the pursuit of happiness and find that it, versus a control condition, makes people more vulnerable to lapses in self-control behavior (Study 3) and, versus an accuracy-seeking condition, makes people persist less in a challenging task (Study 4). Our findings suggest that continuous acts of happiness-seeking may cause a chronic depletion of resources, which leads to daily self-regulation failures, a critical component in a cycle of reduced personal happiness and well-being.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".