Through your partner’s eyes: Perspective taking tempers optimism in behavior predictions
Bibliographic record
Abstract
People tend to be overly optimistic when predicting their future behaviors. This research examines how taking someone else’s perspective affects predictions of relationship behaviors. Study 1 ( N = 82) showed that taking the partner’s perspective when predicting how many relationship-enhancing behaviors one might perform over the next week reduced the number of predicted behaviors and consequently reduced optimistic bias. Study 2 ( N = 244) replicated the reduction in predicted behaviors when taking the partner’s or a friend’s perspective. Study 2 also showed that predictions from another person’s view are similar to predictions for another person’s behavior. Study 3 ( N = 149) replicated the reduction in predicted behaviors and the similarity to predictions for other people’s behavior. Furthermore, Study 3 suggests that one reason why adopting another’s perspective affects predictions is an attenuation of the link between forecasts and relationship quality and increase of the link of forecasts with conscientiousness, which tends to be a better predictor of behavior.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".