Personality and affective forecasting: Trait introverts underpredict the hedonic benefits of acting extraverted.
Bibliographic record
Abstract
People report enjoying momentary extraverted behavior, and this does not seem to depend on trait levels of introversion-extraversion. Assuming that introverts desire enjoyment, this finding raises the question, why do introverts not act extraverted more often? This research explored a novel explanation, that trait introverts make an affective forecasting error, underpredicting the hedonic benefits of extraverted behavior. Study 1 (n = 97) found that trait introverts forecast less activated positive and pleasant affect and more negative and self-conscious affect (compared to extraverts) when asked to imagine acting extraverted, but not introverted, across a variety of hypothetical situations. Studies 2-5 (combined n = 495) found similar results using a between-subjects approach and laboratory situations. We replicated findings that people enjoy acting extraverted and that this does not depend on disposition. Accordingly, the personality differences in affective forecasts represent errors. In these studies, introverts tended to be less accurate, particularly by overestimating the negative affect and self-consciousness associated with their extraverted behavior. This may explain why introverts do not act extraverted more often (i.e., they overestimate hedonic costs that do not actually materialize) and have implications for understanding, and potentially trying to change, introverts' characteristically lower levels of happiness.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".