Real‐World Correlates of Performance on Heuristics and Biases Tasks in a Community Sample
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the current study, we sought to examine whether performance on several heuristics and biases tasks and thinking dispositions was associated with real‐life correlates in a community sample of adults. We examined performance on five heuristics and biases tasks (ratio bias, belief bias in syllogistic reasoning, cognitive reflection, probabilistic and statistical reasoning, and rational temporal discounting), three thinking dispositions (actively open‐minded thinking, future orientation, and avoidance of superstitious thinking), and a questionnaire assessing real‐world correlates in several domains (substance use, driving behavior, financial behavior, gambling behavior, electronic media use, and secure computing). Our heuristics and biases tasks and thinking disposition measures were modestly associated with several real‐world outcomes, including the domains of secure computing, financial behaviors, and the total scores. That is, better performance on the heuristics and biases measures was associated with fewer negative outcomes. We found that the associations were generally higher in males than in females. Heuristics and biases performance and thinking dispositions were unique predictors of real‐world outcomes after statistically controlling for educational attainment and sex differences. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it