Emotional Aftermath of the 2020 U.S. Presidential Election: A Study of Hindsight Bias in Younger and Older Adults
Bibliographic record
Abstract
While researchers have linked adult age differences in hindsight bias to shifts in cognitive and motivational processes, the association between hindsight bias, emotion, and aging remains relatively underexplored. We examined emotion and hindsight bias in younger and older adults (N = 272) against the backdrop of the 2020 U.S. Presidential Election. Participants predicted electoral college votes for the two Presidential candidates before the election and were asked to remember their predictions approximately three weeks later, after the election results had been finalized. Republicans, for whom the electoral outcome was negatively tinged, exhibited greater hindsight bias for President Biden’s result compared with Democrats, for whom the electoral outcome was positive. The asymmetry in hindsight bias between Republicans and Democrats was similar for younger and older participants. This study suggests that negative emotions may exacerbate hindsight bias, and that adult age differences in hindsight bias observed in laboratory settings may not translate to real-world contexts.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".