Real‐World Correlates of Performance on Heuristics and Biases Tasks in a Community Sample
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".