Overcorrection for Social-Categorization Information Moderates Impact Bias in Affective Forecasting
Bibliographic record
Abstract
Plural societies require individuals to forecast how others-both in-group and out-group members-will respond to gains and setbacks. Typically, correcting affective forecasts to include more relevant information improves their accuracy by reducing their extremity. In contrast, we found that providing affective forecasters with social-category information about their targets made their forecasts more extreme and therefore less accurate. In both political and sports contexts, forecasters across five experiments exhibited greater impact bias for both in-group and out-group members (e.g., a Democrat or Republican) than for unspecified targets when predicting experiencers' responses to positive and negative events. Inducing time pressure reduced the extremity of forecasts for group-labeled but not unspecified targets, which suggests that the increased impact bias was due to overcorrection for social-category information, not different intuitive predictions for identified targets. Finally, overcorrection was better accounted for by stereotypes than by spontaneous retrieval of extreme group exemplars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".