Belief bias is stronger when reasoning is more difficult
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
Three studies examine the influence of varying the difficulty of reasoning on the extent of belief bias, while minimising the possibility that the manipulation would influence the way participants approach the task. Specifically, reasoning difficulty was manipulated by making variations in problem content, while maintaining all other aspects of the problems constant. In Study 1, 191 participants were presented with consistent and conflict problems varying in two levels of difficulty. The results showed a significant influence of problem difficulty on the extent of the belief bias, such that the effect of belief was more pronounced for difficult problems. This effect was stronger in Study 2 (73 participants) where the difference in the difficulty of the problems was purposely accentuated. The results of both studies stress the importance of controlling for problem difficulty when studying belief bias. Study 3 examined one consequence of this, i.e., the classic belief vs. logic interaction could be eliminated by manipulating problem difficulty. Theoretical implications for dual-process accounts of belief bias are also discussed.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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