Enacted Extraversion as a Well-Being Enhancing Strategy in Everyday Life
Bibliographic record
Abstract
Lab-based experiments and observational data have consistently shown that extraverted behavior is associated with elevated levels of positive affect. This association typically holds regardless of one’s dispositional level of trait extraversion, and individuals who enact extraverted behaviors in laboratory settings do not demonstrate costs associated with acting counter-dispositionally. Inspired by these findings, we sought to test the efficacy of week-long ‘enacted extraversion’ interventions. In three studies, participants engaged in fifteen minutes of assigned behaviors in their daily life for five consecutive days. Studies 1 and 2 compared the effect of adding more introverted or extraverted behavior (or a control task). Study 3 compared the effect of adding social extraverted behavior or non-social extraverted behavior (or a control task). We assessed positive affect and several indicators of well-being during pretest (day 1) and post-test (day 7), as well as ‘in-the-moment’ (days 2-6). Participants who engaged in extraverted behavior reported greater levels of positive affect ‘in-the-moment’ when compared to introverted and control behaviors. We did not observe strong evidence to suggest that this effect was more pronounced for dispositional extraverts. The current research explores the effects of extraverted behavior on other indicators of well-being and examines the effectiveness of acting extraverted (both socially and non-socially) as a well-being strategy.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 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".