False belief understanding: The influence of person, grammatical mood, counterfactual reasoning and working memory
Bibliographic record
Abstract
The present study had two major goals. The first goal was to assess the relative difficulty among different versions of the unexpected contents task by systematically varying the dimensions of grammatical mood (indicative vs. subjunctive) and person (self vs. other), and to examine the correlational pattern between these different versions of the unexpected contents task and the unexpected locations task. The second goal was to examine the specificity of the relation between false belief understanding and counterfactual reasoning after controlling for age and working memory ability. One hundred, 3‐ to 6‐year‐old, children were administered two measures of false belief understanding (two versions of the unexpected contents task and two versions of the unexpected locations task), two measures of counterfactual reasoning and a working memory measure. Results showed that performance on the unexpected contents task did not significantly differ across conditions. However, only the conditions of the unexpected contents task that concerned another person's false belief correlated significantly with the unexpected locations task. Moreover, counterfactual reasoning was found to explain a significant amount of variance in the unexpected locations task, even after controlling for age and working memory performance. Findings are discussed in the context of different accounts of the development of theory of mind, and in the context of different interpretations of the relation between false belief understanding and counterfactual reasoning.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".