Domain-general individual and developmental differences in confidence acuity
Bibliographic record
Abstract
To appropriately interact with the world, we must always consider how certain or confident we are in our thoughts and actions. Here, we examine whether individual and developmental differences in our sense of confidence – the perceived certainty of our decisions – is domain-general or domain-specific across three dimensions: number, area, and emotion perception. In two experiments, we measured observers' confidence acuity - their ability to discriminate between two internal confidence states – by asking them to choose which of two presented trials they are more confident in. By varying the difference in the difficulty between the two trials, we identify participants who can only detect very large differences in confidence (e.g., not at all sure vs. very sure) and participants who can detect even small differences in their confidence (e.g., sure vs. very sure). In Experiment 1, participants first completed three discrimination tasks: in the Number Task, participants saw groups of blue and yellow dots and indicated which was more numerous; in the Area Task, participants saw a blue and a yellow amorphous blob, and indicated which one is bigger; in the Emotion Task, participants saw two faces side-by-side, and indicated which face is happier (Fig1). Participants then complete a Confidence Discrimination version of these three games. In Experiment 2, 5-8 year-old children completed child-friendly versions of these tasks. Replicating previous results, we found little-to-no correlation between the three discrimination tasks. In strong contrast, however, we found very high correlations in the confidence discrimination tasks for all three dimensions – i.e., participants who could detect fine differences in confidence in the Number task also could detect fine differences in the Area and Emotion tasks, and vice-versa. These results held developmentally, and suggests that the ability to evaluate confidence is part of a domain-general system for representing confidence. Meeting abstract presented at VSS 2017
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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 | 0.001 | 0.009 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".