Are You Hiding Something from Me?: Uncertainty and Judgments About the Intentions of Others
Bibliographic record
Abstract
We are skilled at reading other’s intentions – until they try to hide them. We are biased towards taking at face value what others say, but it is not clear why. One possibility is that we are uncertain, and make the decision by relying on heuristics. Half of our participants judged whether speakers were lying or telling the truth. The other half did not have to commit to a judgment: they were allowed to say they were unsure. We expected these participants would no longer need to rely on simplified heuristics and so show a reduced bias compared to the forced choice condition. Surprisingly, those who could say they were unsure were more biased towards believing people. We consider two possible accounts, both highlighting the importance of examining raters’ uncertainty, which have so far been undocumented. Allowing raters to abstain from judgment gives new insights into the judgment-forming process.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".