Bibliographic record
Abstract
The ability to judge what information other people are likely to know is vital to successful communication and social interaction. The curse of knowledge is the tendency to be biased by one’s knowledge when attempting to reason about a more naïve perspective. The current study sought to determine the role fluency misattribution plays in the curse of knowledge bias in children. Fluency misattribution occurs when the subjective feeling of ‘fluency’ associated with familiar, or easy-to-process, information gets misattributed when making various judgments. Applied to the curse of knowledge, fluency misattribution occurs when one’s feeling of fluency is misinterpreted as the information being objectively obvious or widespread. In the current within-subjects design 115 children aged four to seven were read stories involving two groups of animals, and were asked to judge whether their peers would know more about one group or the other. I tested fluency misattribution by manipulating the frequency with which participants heard about the animals, frequently throughout or only once. The results revealed that increasing the frequency with which the information was presented lead children to over-attribute how common that knowledge was among their peers. I also tested participants on curse of knowledge and source monitoring tasks, revealing a positive correlation between children’s fluency misattribution and source monitoring, and no relationship between fluency misattribution and performance on the curse of knowledge task. I discuss how these findings contribute to the field of social cognition, especially our understanding of the mechanisms involved in reasoning about what others know.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 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".