Electrophysiological measures of attention during speech perception predict metalinguistic skills in children
Bibliographic record
Abstract
Event-related potential (ERP) evidence demonstrates that preschool-aged children selectively attend to informative moments such as word onsets during speech perception. Although this observation indicates a role for attention in language processing, it is unclear whether this type of attention is part of basic speech perception mechanisms, higher-level language skills, or general cognitive abilities. The current study examined these possibilities by measuring ERPs from 5-year-old children listening to a narrative containing attention probes presented before, during, and after word onsets as well as at random control times. Children also completed behavioral tests assessing verbal and nonverbal skills. Probes presented after word onsets elicited a more negative ERP response beginning around 100 ms after probe onset than control probes, indicating increased attention to word-initial segments. Crucially, the magnitude of this difference was correlated with performance on verbal tasks, but showed no relationship to nonverbal measures. More specifically, ERP attention effects were most strongly correlated with performance on a complex metalinguistic task involving grammaticality judgments. These results demonstrate that effective allocation of attention during speech perception supports higher-level, controlled language processing in children by allowing them to focus on relevant information at individual word and complex sentence levels.
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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 | 0.001 | 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".