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
To effectively communicate and collaborate with others, we must monitor not only other people’s cognitive states (e.g., what someone thinks or believes), but also their metacognitive states (e.g., how confident they are in their beliefs). Confidence is however rarely communicated explicitly: instead, we often perceive others’ confidence via implicit signals such as speech prosody or movement dynamics. Recent advances in artificial intelligence (AI) have broadened the scope of these metacognitive inferences: artificial agents often perform similarly to humans yet rarely explicitly signal their confidence in their beliefs, raising the question as to how humans attribute confidence to AI. Here we report five pre-registered experiments in which participants observed human and artificial agents make perceptual choices, and reported how confident they thought the observed agent was in each choice. Overall, attributions of confidence were sensitive to observed variables such as task difficulty, accuracy, and response time. Strikingly, participants attributed higher confidence to AI agents compared to other humans, even though their behaviour was identical. An illusion of greater confidence in artificial agents’ decisions generalised across different behavioural profiles (Experiment 2), agent descriptions (Experiment 3), and choice domains (Experiment 4). Attributions of confidence also influenced advice-taking behaviour, as participants were more willing to accept the advice of artificial systems compared to matched humans (Experiment 5). Overall, our results uncover a systematic illusion of confidence in AI decisions, and highlight the importance of metacognition in guiding human-machine interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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