Referential Context and Executive Functioning Influence Children’s Resolution of Syntactic Ambiguity
Bibliographic record
Abstract
Classic studies reveal two striking differences between preschoolers and adults in online sentence comprehension. Adults (a) recruit referential context cues to guide syntactic parsing, interpreting an ambiguous phrase as a modifier if a modifier is needed to single out the intended referent among multiple options, and (b) use late-arriving information to recover from misinterpretation. Five-year-olds fail on both counts, appearing insensitive to the referential context, and often failing to recover from parsing errors (Trueswell et al., 1999). But other findings suggest that 5-year-olds show delayed rather than absent sensitivity to the referential context, and that individual differences in executive functioning predict children's ability to recover from garden-path errors. In two experiments, we built on these findings, focusing on whether children recruit referential-context cues if given time to do so. Children heard temporarily ambiguous instructions (e.g., Put the frog on the pond into the tent), while we monitored their eye-gaze and actions. We used a slow speech rate, and manipulated referential context between rather than within subjects, to give children time to bring referential context cues into play. Across experiments, eye-movement and action analyses revealed emerging sensitivity to the referential context. Moreover, error rates and eye-movement patterns indicating failures to revise were predicted by individual differences in executive function (scores in Simon Says and Flanker tasks). These data suggest that children, like adults, use referential context information in syntactic processing under some circumstances; the findings are also consistent with a role for domain-general executive function in resolution of syntactic ambiguity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".