Examination of the Belief Bias Effect Across Two Domains of Reasoning
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
The belief bias effect – the finding that prior beliefs influence judgments of logic and evidence – has been a topic of much empirical investigation in both deductive and causal reasoning. However, to date, no research has examined the degree to which such biases are the result of common or distinct mechanisms in these two domains. By using common scales of measurement, I examine the degree to which individuals show common biases in these two domains in two experiments. Surprisingly, although the belief bias effect was observed in both paradigms, biases in one domain were unreliably associated with biases in the other domain. Experiment 2 included 6 measures of individual differences in an attempt to uncover the observation of differential biases in these domains. Dogmatism was found to be the single most predictive measure of belief bias, but only in deductive reasoning. These data are discussed in terms of dual process theories of reasoning.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| 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