Infants' lexical comprehension and lexical anticipation abilities are closely linked in early language development
Bibliographic record
Abstract
Theories across cognitive domains propose that anticipating upcoming sensory input supports information processing. In line with this view, prior findings indicate that adults and children anticipate upcoming words during real-time language processing, via such processes as prediction and priming. However, it is unclear if anticipatory processes are strictly an outcome of prior language development or are more entwined with language learning and development. We operationalized this theoretical question as whether developmental emergence of comprehension of lexical items occurs before or concurrently with the anticipation of these lexical items. To this end, we tested infants of ages 12, 15, 18, and 24 months (N = 67) on their abilities to comprehend and anticipate familiar nouns. In an eye-tracking task, infants viewed pairs of images and heard sentences with either informative words (e.g., eat) that allowed them to anticipate an upcoming noun (e.g., cookie), or uninformative words (e.g., see). Findings indicated that infants' comprehension and anticipation abilities are closely linked over developmental time and within individuals. Importantly, we do not find evidence for lexical comprehension in the absence of lexical anticipation. Thus, anticipatory processes are present early in infants' second year, suggesting they are a part of language development rather than solely an outcome of it.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".