Confidence reports during perceptual decision making dissociate from changes in subjective experience
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
In noisy perceptual environments, people frequently make decisions based on non-perceptual information to maximize rewards. Therefore, a central problem in psychophysics, metacognition and consciousness research is to distinguish between decisions resulting from changes in subjective experience and those arising from non-perceptual information. It has recently been proposed that confidence reports can be used to discriminate between changes in subjective experience and those arising from non-perceptual information. Here we use a Bayesian ordinal modelling framework combined with an explicit measure of subjective experience to show across two experiments (N = 204) and three bias manipulations that confidence during perceptual decision-making does not uniquely reflect subjective experience. Instead, non-perceptual manipulations affecting response bias 'leak' into perceptual confidence reports. This occurs not only for biases resulting from changes in the base rate of stimuli ('cognitive' priors), but also when biasing information does not inform decision correctness (asymmetric payoff matrix). The relative strength of biases in first-order responses and confidence may help disentangle whether a given bias manipulation is perceptual in nature or not.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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