Bibliographic record
Abstract
In two experiments we tested the hypothesis that the mechanisms that produce belief bias generalise across reasoning tasks. In formal reasoning (i.e., syllogisms) judgements of validity are influenced by actual validity, believability of the conclusions, and an interaction between the two. Although apparently analogous effects of belief and argument strength have been observed in informal reasoning, the design of those studies does not permit an analysis of the interaction effect. In the present studies we redesigned two informal reasoning tasks: the Argument Evaluation Task (AET) and a Law of Large Numbers (LLN) task in order to test the similarity of the phenomena concerned. Our findings provide little support for the idea that belief bias on formal and informal reasoning is a unitary phenomenon. First, there was no correlation across individuals in the extent of belief bias shown on the three tasks. Second, evidence for belief by strength interaction was observed only on AET and under conditions not required for the comparable finding on syllogistic reasoning. Finally, we found that while conclusion believability strongly influenced assessments of arguments strength, it had a relatively weak influence on the verbal justifications offered on the two informal reasoning tasks.
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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 | 0.013 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".