Infants Exposed to Fluent Natural Speech Succeed at Cross-Gender Word Recognition
Bibliographic record
Abstract
PURPOSE: To examine the possibility that early signal-to-word form mapping capabilities are robust enough to handle substantial indexical variation in the realization of words. METHOD: Two groups of 7.5-month-olds were tested with the Headturn Preference Procedure. Half of the infants were exposed to words embedded in passages spoken by their mothers and tested on lists of trained and novel isolated words spoken by their fathers. The other half of the infants were yoked pairs listening to unfamiliar speakers. RESULTS: In the test phase, infants listened longer to trained than to novel words, indicating that they successfully segmented the words from the passages. This result was not modulated by infants' familiarity with the speaker. CONCLUSIONS: Under more naturalistic listening conditions, 7.5-month-olds exhibit the ability to recognize words in the face of substantial indexical variation regardless of whether speakers are familiar. This suggests that early word representations are, at least to some extent, independent of the speaker's gender and may reflect sophisticated abstraction capabilities on the part of the infants, which would render extreme episodic models of early speech perception untenable. Additional research using similarly ecologically valid testing methods is called for to elucidate the precise nature of early word representations.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".