Theory of mind and preschoolers’ understanding of misdeed and politeness lies.
Bibliographic record
Abstract
We told ninety-nine 4- and 5-year-olds stories in which speakers told lies and truths in two contexts: those told to deny a transgression (misdeeds) and those told to spare another's feelings (politeness). Participants identified each statement as a lie or as the truth, morally judged it as good or bad, and decided whether or not to assign punishment to the speaker. All children received measures of first- and second-order false-belief understanding. Although 4-year-olds were above chance in their identification accuracy, they did not differentiate between lies and truths in their moral judgments or punishments. Five-year-olds outperformed 4-year-olds in their identification accuracy, morally judged lies more negatively than truths, and assigned speakers of lies more punishment than those who told truths. Five-year-olds also treated lies about misdeeds more negatively than politeness lies in their moral judgments and punishments. Children's identification of both lies and truths related to their first-order false-belief understanding, while their moral judgments and punishment of lies related to their second-order false-belief understanding. These findings suggest that different considerations are made when children reason about the conceptual and moral differences between lies and truths. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".