Can knowledge of election results change recall of our predictions? Neural correlates of political hindsight bias
Bibliographic record
Abstract
Hindsight bias (HB) is the tendency to retrospectively exaggerate one's foresight knowledge about the outcome of an event. Cognitive processes influenced by newly obtained outcome information are used to explain the HB phenomenon, but the neural correlates remain unknown. This study investigated HB in the context of election results using a memory design and functional magnetic resonance imaging for the first time. Participants were asked to predict and recall the percentage of votes obtained by (pairs of) candidates before and after an election. The results revealed that 88% of participants showed HB by recalling that their predictions were closer to the actual outcomes than they really were; and participants had HB for 38% of the events. The HB effect was associated with activation in the medial superior frontal gyrus and bilateral inferior frontal gyrus (IFG), which have been implicated in updating an old belief due to new information and is similar to the process of reconstruction bias. Furthermore, participants with a greater HB effect showed greater activation of the left IFG. In conclusion, we successfully observed the HB phenomenon in election results, and our imaging results suggested that the HB phenomenon might involve reconstruction bias.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".