An entropy model of decision uncertainty reveals that attention influences decisions but does not alter appearance
Bibliographic record
Abstract
Metacognition, the introspection of one’s own decisions, can inform our understanding of decision mechanisms. Here we show that decision uncertainty reflects the entropy of the psychometric function. To test this novel model, we measured uncertainty in 11 participants as they judged the relative contrast appearance of two stimuli in three experiments: one stimulus was either preceded by an attentional cue and participants performed comparative (Experiment 1) or equality (Experiment 3) judgments; or one stimulus was post-cued and participants were instructionally biased to preferably choose it in a comparative judgment (Experiment 2). The entropy model permitted an otherwise intractable quantitative analysis of participants’ uncertainty, which distinguished the two comparative judgments that produced nearly identical psychometric functions, but not the comparative and equality judgments where the subjective reports revealed different effects of attention. These results show that attention can manipulate the decision mechanism without interfering with veridical perception.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".