Impact bias or underestimation? Outcome specifications predict the direction of affective forecasting errors.
Bibliographic record
Abstract
Affective forecasts are used to anticipate the hedonic impact of future events and decide which events to pursue or avoid. We propose that because affective forecasters are more sensitive to outcome specifications of events than experiencers, the outcome specification values of an event, such as its duration, magnitude, probability, and psychological distance, can be used to predict the direction of affective forecasting errors: whether affective forecasters will overestimate or underestimate its hedonic impact. When specifications are positively correlated with the hedonic impact of an event, forecasters will overestimate the extent to which high specification values will intensify and low specification values will discount its impact. When outcome specifications are negatively correlated with its hedonic impact, forecasters will overestimate the extent to which low specification values will intensify and high specification values will discount its impact. These affective forecasting errors compound additively when multiple specifications are aligned in their impact: In Experiment 1, affective forecasters underestimated the hedonic impact of winning a smaller prize that they expected to win, and they overestimated the hedonic impact of winning a larger prize that they did not expect to win. In Experiment 2, affective forecasters underestimated the hedonic impact of a short unpleasant video about a temporally distant event, and they overestimated the hedonic impact of a long unpleasant video about a temporally near event. Experiments 3A and 3B showed that differences in the affect-richness of forecasted and experienced events underlie these differences in sensitivity to outcome specifications, therefore accounting for both the impact bias and its reversal. (PsycINFO Database Record
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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 | 0.013 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".