Basic word segmentation abilities emerge earlier in infancy than previously thought.
Bibliographic record
Abstract
English-learning 7.5- but not 6-month-olds extract word forms from fluent speech (Jusczyk et al., 1999). Thus, English learners are thought to begin segmenting words from speech by 7.5 months. However, recent research has shown that when target words are flanked by a frequent and emotionally salient word (e.g., the infant’s name), even 6-month-olds can extract words from speech (e.g., Bortfeld et al., 2005). This suggests that basic word segmentation capabilities may emerge earlier than past studies have suggested. Using the Headturn Preference Procedure, we tested 6-month-olds’ ability to segment utterance-flanked words from speech, e.g., “geff” from “At the circus we met a silly geff.” Infants were familiarized with passages containing target words in utterance initial and final position, and then tested on their recognition of these words in isolation. A significant looking time difference to familiar versus unfamiliar words was found, indicating that 6-month-olds segmented the target words from speech. Six-month-olds’ success at segmenting utterance-flanked words from speech is particularly interesting because infant-directed speech consists of short utterances containing many utterance-flanked words. Segmentation of utterance-flanked words could help infants learn the cues needed to extract harder utterance-medial words from speech (see Seidl and Johnson, 2006).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".