Ignorance and Inference: Do Problems with Gricean Epistemic Reasoning Explain Children’s Difficulty with Scalar Implicature?
Bibliographic record
Abstract
Unlike adults, children as old as 9 years of age often fail to infer that a sentence like, ‘Some of the children slept’ implies the falsity of its stronger alternative, ‘All of the children slept’—an inference referred to as a ‘scalar implicature’. Several explanations have been proposed to account for children’s failures with scalar implicature, including domain-general processing limitations, pragmatic deficits or an inability to access the relevant alternatives in a lexical scale (e.g. all as an alternative to some ). Our study focused on the role of Gricean epistemic reasoning in children’s failures by testing their ability to compute ‘ignorance implicatures’, which require reasoning about speaker knowledge and informativeness but which differ from scalar implicature with respect to the alternative statements that are involved. We administered two matched tasks to 4- and 5-year-old children: one that assessed their ability to compute ignorance implicatures, and another that assessed their ability to compute scalar implicatures. Five-year-olds successfully computed ignorance implicatures despite failing to compute scalar implicatures, while 4-year-olds failed at both types of inference. These results suggest that 5-year-olds are able to reason about speaker knowledge and informativeness, and thus that it is difficult to explain their deficit with scalar implicature via these factors. We speculate about other possible sources of their difficulties, including processing limits and children’s access to the specific scalar alternatives required by scalar implicature.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".