Word Level Stress and Lexical Processing in 17‐Month‐Old Infants
Bibliographic record
Abstract
Detailed representations enable infants to distinguish words from one another and more easily recognize new words. We examined whether 17-month-old infants encode word stress in their familiar word representations. In Experiment 1, infants were presented with pairs of familiar objects while hearing a target label either properly pronounced with the correct stress (e.g., baby /'beɪbi/) or mis-pronounced with the incorrect stress pattern (e.g., baby /beɪ'bi/). Infants mapped both the correctly stressed and mis-stressed labels to the target objects; however, they were slower to fixate the target when hearing the mis-stressed label. In Experiment 2, we examined whether infants appreciate that stress has a nonproductive role in English (i.e., altering the stress of a word does not typically signal a change in word meaning) by presenting infants with a familiar object paired with a novel object while hearing either correctly stressed or mis-stressed familiar words (Experiment 2). Here, infants mapped the correctly stressed label to the familiar object but did not map the mis-stressed label reliably to either the target or distractor objects. These findings suggest that word stress impacts the processing of familiar words, and infants have burgeoning knowledge that altering the stress pattern of a familiar word does not reliably signal a new referent.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".