Confidence Can Be Used to Discriminate Between Accurate and Inaccurate Lie Decisions
Bibliographic record
Abstract
There is a long-standing belief that confidence is not useful at discriminating between accurate and inaccurate deception decisions. Historically, this position made sense because people showed little ability to discriminate lie-tellers from truth-tellers. But, it is now widely accepted that, under certain conditions, people can discriminate between lie-tellers and truth-tellers. Nevertheless, belief that confidence does not discriminate between accurate and inaccurate responses persists. This belief is somewhat paradoxical because, to the extent that people can discriminate between lie-tellers and truth-tellers, signal detection theory naturally predicts a positive relationship between confidence and accuracy. In line with our signal-detection-based predictions, we show that, among decisions about whether someone is lying, those made with high confidence are more accurate than those made with low confidence. This important relationship has gone unnoticed in past work because of a reliance on inappropriate measures. Past research examining the confidence-accuracy relationship in deception research relied on correlating average confidence with proportion of correctly identified lies. These correlations provide information on whether more confident judges tend to be more accurate but remain silent on the arguably more important question of whether higher confidence decisions are more accurate than lower confidence decisions. We show that confidence-accuracy characteristic analyses are uniquely suited to measuring the confidence-accuracy relationship in deception research.
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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 | 0.006 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".