The Ability to Map Differentially Stressed Labels to Objects Predicts Language Development at 24 months in 12‐month‐olds at High Risk for Autism
Bibliographic record
Abstract
Sensitivity to language‐specific stress patterns during infancy facilitates finding, mapping, and recognizing words, and early preferences for the predominate stress pattern of the infant's native language have been argued to facilitate language relevant outcomes (Ference & Curtin, 2013 Journal of Experimental Child Psychology, 116, 891; Weber et al., 2005 Cognitive Brain Research, 25, 180). We examined 12‐month‐old infant siblings of typically developing children (SIBS‐TD) and infant siblings of children diagnosed with autism spectrum disorder (ASD; SIBS‐A) on their ability to map differentially stressed labels to objects. We also examined whether success at this task relates to infants’ vocabulary size at 12 months, and more specifically to SIBS‐A's vocabulary at both 12 and 24 months. SIBS‐TD successfully mapped the word–object pairings, which related to their vocabulary comprehension at 12 months. In contrast, SIBS‐A as a group did not map the word–object pairings, which was unrelated to vocabulary size at 12 months. However, success on this task for SIBS‐A predicted expressive language abilities at 24 months using the Mullen Scales of Early Learning (MSEL; Mullen, 1995 Mullen Scales of Early Learning. Circle Pines, MN: American Guidance) and the MacArthur‐Bates Communicative Development Inventory (MB‐CDI; Fenson et al., 1993 MacArthur Communicative Development Inventory: Users Guide and Technical Manual. San Diego, CA: Singular Publishing Company). Our study is the first to demonstrate that 12‐month‐old SIBS‐A who succeed at word mapping using lexical stress are more likely to have stronger expressive language abilities at 24 months.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".