Examining the relationship between metacognitive trust in thinking styles and supernatural beliefs
Bibliographic record
Abstract
Conflicting findings have emerged from research on the relationship between thinking styles and supernatural beliefs. In two studies, we examined this relationship through meta-cognitive trust and developed a new: (1) experimental manipulation, a short scientific article describing the benefits of thinking styles: (2) trust in thinking styles measure, the Ambiguous Decisions task; and (3) supernatural belief measure, the Belief in Psychic Ability scale. In Study 1 (N = 415) we found differences in metacognitive trust in thinking styles between the analytical and intuitive condition, and overall greater trust in analytical thinking. We also found stronger correlations between thinking style measures (in particular intuitive thinking) and psychic ability and paranormal beliefs than with religious beliefs, but a mixed-effect linear regression showed little to no variation in how measures of thinking style related to types of supernatural beliefs. In Study 2, we replicated Study 1 with participants from the United States, Canada, and Brazil (N = 802), and found similar results, with the Brazilian participants showing a reduced emphasis on analytical thinking. We conclude that our new design, task, and scale may be particularly useful for dual-processing research on supernatural belief.
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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".