Scalar inference is supported by Theory of Mind networks in adults and children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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