Inferring Others' Hidden Thoughts: Smart Guesses in a Low Diagnostic World
Bibliographic record
Abstract
Abstract People are biased toward believing that what others say is what they truly think. This effect, known as the truth bias, has often been characterized as a judgmental error that impedes accuracy. We consider an alternative view: that it reflects the use of contextual information to make the best guess when the currently available information has low diagnosticity. Participants learnt the diagnostic value of four cues, which were present during truthful statements between 20% and 80% of the time. Afterwards, participants were given contextual information: either that most people would lie or that most would tell the truth. We found that people were biased in the direction of the context information when the individuating behavioral cues were nondiagnostic. As the individuating cues became more diagnostic, context had less to no effect. We conclude that more general context information is used to make an informed judgment when other individuating cues are absent. That is, the truth bias reflects a smart guess in a low diagnostic world. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".