Open-minded and reflective thinking predicts reasoning and meta-reasoning: evidence from a ratio-bias conflict task
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
Dispositional measures of actively open-minded thinking and cognitive reflection both predict reasoning accuracy on conflict problems. Here we investigated their relative impact on meta-reasoning. To this end, we measured reasoning accuracy and two indices of meta-reasoning performance – conflict detection sensitivity and meta-reasoning discrimination – using a ratio-bias task. Our key predictors were actively open-minded thinking and cognitive reflection, and numeracy, cognitive ability, and mindware instantiation were controlled for. Actively open-minded thinking was a better predictor of reasoning accuracy and meta-reasoning discrimination than cognitive reflection, and was the only dispositional measure to significantly predict conflict detection sensitivity. Thus, susceptibility to biased reasoning and meta-reasoning may be better captured by a reasoner’s ability to engage in open-minded thinking than by their ability to engage in reflective thinking.
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.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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