Domain-general representations of confidence throughout development
Bibliographic record
Abstract
We routinely make decisions that combine independent representations, such as combining vision and audition to decipher speech (McGurk & MacDonald, 1976). Recent work has argued that confidence representations exist in a domain-general format that could facilitate this integration: adult observers can compare their confidence across independent perceptual dimensions (orientation and frequency; de Gardelle & Mamassian, 2014), between auditory and visual stimuli (de Gardelle et al., 2016), and some work hints that this domain-generality might extend to non-perceptual tasks like memory and executive functioning (Mazancieux et al., under review). Here, we test a domain-general confidence hypothesis throughout development, investigating whether confidence is by nature domain-general, or if this emerges with experience. In Experiment 1, 6-7-year-olds compared their confidence in two decisions from the same visual dimension (e.g., number and number) and from two distinct visual dimensions (e.g., number and emotion) with equivalent ability (Fig. 1 and 2), supporting the hypothesis that visual confidence is domain-general in childhood. In Experiment 2, we similarly found that individual differences in certainty comparison are strongly correlated in 6-9-year-olds across otherwise uncorrelated visual dimensions (number, area, and emotion, Fig. 3). In Experiment 3, we extend this work to examine whether this domain-generality also exists between perception and memory in both children and adults. Finally, in Experiment 4, we attempt to identify what the common currency of confidence might be that allows for these cross-domain comparisons, examining whether response times, probability of accuracy, or objective difficulty (the ratio of two quantities) underlie these decisions. Together, our findings support the idea that confidence, particularly perceptual confidence, is represented in a common format even in childhood, providing one account for how independent representations could be compared.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".