Testing theories of temporal inferences: Evidence from child language
Bibliographic record
Abstract
Sentences involving past tense verbs, such as “My dogs were on the carpet”, tend to give rise to the inference that the corresponding present tense version, “My dogs are on the carpet”, is false. This inference is often referred to as a cessation or temporal inference, and is generally analyzed as a type of implicature. There are two main proposals for capturing this asymmetry: one assumes a difference in informativity between the past and present counterparts (Altshuler & Schwarzschild 2013), while the other proposes a structural difference between the two (Thomas 2012). The two approaches are similar in terms of empirical coverage, but differ in their predictions for language acquisition. Using a novel animated picture selection paradigm, we investigated these predictions. Specifically, we compared the performance of a group of 4–6-year-old children and a group of adults on temporal inferences, scalar implicatures arising from “some”, and inferences of adverbial modifiers under negation. The results revealed that overall, children computed all three inferences at a lower rate than adult controls; however they were more adult-like on temporal inferences and inferences of adverbial modifiers than on scalar implicatures. We discuss the implications of the findings, both for a developmental alternatives-based hypothesis (e.g., Barner et al. 2011; Singh et al. 2016; Tieu et al. 2016; 2018), as well as theories of temporal inferences, arguing that the finding that children were more (and equally) adult-like on temporal inferences and adverbial modifiers supports a structural theory of temporal inferences along the lines of Thomas (2012).
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".