Confidence reports during perceptual decision making dissociate from changes in subjective experience
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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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".