Language Experience and the Organization of Brain Activity to Phonetically Similar Words: ERP Evidence from 14- and 20-Month-Olds
Bibliographic record
Abstract
The ability to discriminate phonetically similar speech sounds is evident quite early in development. However, inexperienced word learners do not always use this information in processing word meanings [Stager & Werker (1997). Nature, 388, 381-382]. The present study used event-related potentials (ERPs) to examine developmental changes from 14 to 20 months in brain activity important in processing phonetic detail in the context of meaningful words. ERPs were compared to three types of words: words whose meanings were known by the child (e.g., ''bear''), nonsense words that differed by an initial phoneme (e.g., ''gare''), and nonsense words that differed from the known words by more than one phoneme (e.g., ''kobe''). These results supported the behavioral findings suggesting that inexperienced word learners do not use information about phonetic detail when processing word meanings. For the 14-month-olds, ERPs to known words (e.g., ''bear'') differed from ERPs to phonetically dissimilar nonsense words (e.g., ''kobe''), but did not differ from ERPs to phonetically similar nonsense words (e.g., ''gare''), suggesting that known words and similar mispronunciations were processed as the same word. In contrast, for experienced word learners (i. e., 20-month-olds), ERPs to known words (e.g., ''bear'') differed from those to both types of nonsense words (''gare'' and ''kobe''). Changes in the lateral distribution of ERP differences to known and unknown (nonce) words between 14 and 20 months replicated previous findings. The findings suggested that vocabulary development is an important factor in the organization of neural systems linked to processing phonetic detail within the context of word comprehension.
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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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.000 |
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".