A pandemic-related affect gap in risky decisions for self and others
Bibliographic record
Abstract
The early stages of the COVID-19 pandemic exposed large portions of the global populations to increased daily stressors. Research on risky choice in medical contexts suggests that affect-rich choice options promote less-advantageous decision strategies compared with affect-poor options, causing an “affect gap” in decision making. The current experiments (total N = 437, age range: 21–82) sought to test whether negative pandemic-related affect would lower expected-value (EV) maximisation within individuals. In Experiment 1, participants indicated how much they would be willing to pay to avoid specific pandemic experiences (e.g. “not being able to gather in groups”), and then chose among pairs of risky prospects that involved pandemic experiences or subjectively-equivalent monetary losses. EV maximising was lower for pandemic experiences than for equivalent monetary losses. Experiment 2 replicated this finding, and further demonstrated a moderating role of decision perspective. EV maximising was greater in decisions made for another person than in decisions made for oneself. These findings highlight potential strategies for boosting decision making under affect-rich real-world conditions.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".