The Curse of Knowledge in Reasoning About False Beliefs
Why this work is in the frame
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Bibliographic record
Abstract
Assessing what other people know and believe is critical for accurately understanding human action. Young children find it difficult to reason about false beliefs (i.e., beliefs that conflict with reality). The source of this difficulty is a matter of considerable debate. Here we show that if sensitive-enough measures are used, adults show deficits in a false-belief task similar to one used with young children. In particular, we show a curse-of-knowledge bias in false-belief reasoning. That is, adults' own knowledge of an event's outcome can compromise their ability to reason about another person's beliefs about that event. We also found that adults' perception of the plausibility of an event mediates the extent of this bias. These findings shed light on the factors involved in false-belief reasoning and are discussed in light of their implications for both adults' and children's social cognition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it