Number agreement processing in adolescents with and without developmental language disorder (DLD): evidence from event-related brain potentials
Bibliographic record
Abstract
In morphologically richer languages, including French, one must learn the specific properties of number agreement in order to understand the language, and this learning process continues into adolescence. This study examined similarities and differences between French-speaking adolescents with and without developmental language disorder (DLD) when processing number agreement, and investigated how morpho-syntactic regularity affected language processing. Using event-related potentials (ERP) and only grammatical sentences with audio-visual mismatches, we studied ERP correlates to three types of number agreement: (1) regular determiner agreement in noun phrases, (2) regular subject-verb plural liaison, and (3) irregular subject-verb agreement. We also included a lexico-semantic mismatch condition to investigate lexico-semantic processing in our participants. 17 adolescents with DLD (M = 14.1 years) and 20 (pre)teens with typical language (TL, M = 12.2 years) participated in the study. Our results suggest three patterns. First, French-speaking teenagers without DLD are still consolidating their neurocognitive processing of morpho-syntactic number agreement and generally display ERP profiles typical of lower language proficiency than adult native speakers. Second, differences in morphosyntactic processing between teenagers with and without DLD seem to be limited to rule-based (regular) number agreement. Third, there is little evidence for corresponding differences in lexico-semantic processing.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".