The erosion of rationality in high vulnerability conditions: A cognitive‐disruption perspective
Bibliographic record
Abstract
Abstract This research examines how a decision‐maker's perceived vulnerability influences their susceptibility to the “anecdotal bias,” a phenomenon where statistical evidence is disregarded in favor of anecdotal information. Across six studies, our research shows that high vulnerability aggravates the anecdotal bias instead of reducing it. Study 1 provides preliminary evidence that high vulnerability exacerbates the anecdotal bias among individuals seeking decision‐relevant information in the context of the COVID‐19 pandemic. Studies 2A and 2B demonstrate that high vulnerability intensifies the anecdotal bias in different decision contexts. Study 3 replicates these findings and identifies negative emotional arousal as a key mechanism underlying this effect. Study 4 examines the moderating role of personal relevance, showing that when individuals make decisions for others (vs. themselves), high vulnerability does not lead to the anecdotal bias. Moreover, it is cognitive disruption and intuitive thinking caused by negative emotional arousal that increases reliance on anecdotal (vs. statistical) information. Finally, Study 5 demonstrates the moderating effect of mindfulness meditation, highlighting its role as a preemptive safeguard against this biased behavior. Theoretical contributions and practical implications of these findings are discussed.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".