Neural correlates of dialect perception in early infancy.
Bibliographic record
Abstract
A wealth of behavioral research suggests that infants become increasingly specialized in their native dialect/language in infancy. In contrast, few studies document how this early specialization is reflected in neural activation, most of which have compared familiar and unfamiliar languages, and none focused on different dialects. This study aimed to fill that gap, focusing on cerebral activation in temporal areas, as measured with near infrared spectroscopy. Audiovisual infant-directed speech was recorded from talkers of either Parisian or Quebecois French. These videos were presented to 5-month-old Parisian infants in blocks within which videos from two talkers alternated in one of two ways. In pure blocks, both talkers were either Parisian (pure-familiar) or Quebecois (pure-unfamiliar). In mixed blocks, the two talkers had different dialects. A robust and mostly bilateral activation was found for both mixed and pure blocks. Follow-up comparisons revealed a stronger activation for mixed blocks than for pure ones, and a trend for more activation in response to the unfamiliar dialect than to the familiar one. These findings are congruent with behavioral studies showing early sensitivity to dialects and extend the brain imaging literature on early neural attunement to the language as spoken in the infant’s environment.
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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 | 0.001 | 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".