A common algorithm for confidence judgements across visual, auditory and audio-visual decisions
Bibliographic record
Abstract
Abstract Most studies investigating the computational basis of decision confidence have focused on simple visual perceptual tasks, leaving open questions about how confidence is formed in decisions involving other sensory modalities or those requiring the integration of information across modalities. To address these gaps, we used computational modelling to analyse confidence judgements in perceptual decisions involving visual, auditory, and audio-visual stimuli. Drawing on research into visual confidence, we adapted models from the literature to evaluate their fit to our data, comparing three popular classes: unscaled evidence strength, scaled evidence strength, and Bayesian models. Our results show that the scaled evidence strength models consistently outperformed the other model classes across all tasks and could also be used to predict behaviour in the audio-visual task from the unidimensional auditory and visual model fits. These findings suggest that confidence judgements across different perceptual decisions rely on a shared algorithm that dynamically accounts for both sensory uncertainty and evidence strength, without the computation of posterior probabilities. Additionally, we investigated the algorithms used for multidimensional (audio-visual) confidence judgements specifically, showing that participants integrated both the visual and auditory dimensions of the stimulus, rather than relying solely on the most informative modality, and used a modality-independent measure of sensory uncertainty to adjust their confidence. Overall, our findings provide evidence for a common algorithm underlying confidence judgements across modalities and demonstrate the broad applicability of the scaled evidence strength algorithm, even in tasks requiring the integration of distinct sensory information.
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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 | 0.011 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".