Is there a sensitive period for representation of phoneme sequences
Bibliographic record
Abstract
A key issue in development of speech perception and production concerns the possible existence of an early sensitive period that facilitates language acquisition. Spoken language acquisition entails representation of grammatical sequences of phonemes. In a two-phase experiment, 120 participants in three age groups (pre-adolescents, adolescents, young adults) were presented with 64 words, 16 each of English, English pronounceable non-words, Spanish, and Mandarin. The task was to determine if the word was English. All age groups performed well, indicating that the words had been attended. A subsequent surprise recognition phase presented 64 words again, one half of which were old and one half new. Participants judged whether each word had previously been presented. Here performance overall was poorest for Spanish and Mandarin, and children performed relatively more poorly on English non-words as compared to the older groups, but showed no age-relative disadvantage for Chinese. Arguing that evidence for a sensitive period arises if children perform relatively better than older participants on foreign words as compared to English words or English non-words, the recognition results are consistent with the proposal of a sensitive period for phoneme sequence representation. The paradigm provides a new way of examining the sensitive period hypothesis. (supported by NSERC)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".