Lies, Damn Lies, and Expectations: How Base Rates Inform Lie–Truth Judgments
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
Summary We are biased towards thinking that people are telling the truth. Our study represents the first test of how beliefs about the base rate of truths and lies affect this truth bias. Raters were told either 20, 50 or 80% of the speakers would be telling the truth. As the speaker delivered their statement, participants indicated moment by moment whether they thought the speaker was lying or being truthful. At the end of the statement, they made a final lie–truth judgment and indicated their confidence. While viewing the statement, base rate beliefs had an early influence, but as time progressed, all conditions showed a truth bias. In the final judgment at the end of the statement, raters were truth biased when expecting mostly truths but did not show a lie bias when expecting mostly lies. We conclude base rate beliefs have an early influence, but over time, a truth bias dominates. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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