Scalar inference is supported by Theory of Mind networks in adults and children
Bibliographic record
Abstract
Scalar implicatures, a type of pragmatic inference that relies on the evaluation of alternatives on a logical scale, have been extensively studied in the developmental literature, yet their developmental timeline remains hazy. Furthermore, debates continue over the contributions that potential supporting factors, such as Theory of Mind, executive functions, and language, make to the scalar implicature derivation process in adulthood and during development. We present a novel approach to address these issues: we tested 4- and 5-year-old children (majority white with college-educated mothers and slightly higher than average SES) and adults (undergraduate students) on scalar implicature and theory of mind tasks using spatial neuroimaging techniques (fNIRS). We find evidence that neural networks associated with Theory of Mind, executive functions, and language were active during scalar inference in adults and some preschool-aged children. Moreover, we find that children who pass the scalar inference task show activation of neural networks associated with Theory of Mind and language processing (specifically including the dmPFC and LIFG) during scalar inference and right temporoparietal junction activity during a Theory of Mind task, while children who do not pass the task do not show activation of these regions. This study provides the first exploration into neural correlates of scalar inference in 4- and 5-year-olds using spatial neuroimaging techniques.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".