Speech perception and the sensorimotor system in Infancy
Bibliographic record
Abstract
The speech that infants perceive and learn from is highly multisensory. Preverbal infants show multisensory speech sensitivities prior to direct associative experience, for instance, even to non-native speech that they have not experienced before. Sensorimotor influences on auditory perception are of increasing interest in the context of speech perception development. In a series of experiments, we explored whether infants’ speech perception is influenced by articulatory-auditory relations. Specifically, we experimentally restricted the movement of infants’ relevant articulators during speech perception tasks. We review a series of studies showing both behavioral (eye-tracking) and neural (EEG) evidence that preverbal infants’ auditory speech perception is influenced by articulatorily specific sensorimotor input at 6- and 3- months of age. To control for the possibility of learning, we tested both native and non-native (hence not visually nor auditorily familiar) phonetic contrasts. To explore whether the auditory-sensorimotor relation is in place even without feedback from self-produced vocalization, we tested consonant discrimination in infants as young as 3-month, who are not yet babbling and are unable to produce consonant sounds. Our results show that the sensorimotor-auditory link is in place prior to specific experience watching, hearing, or producing the relevant sounds.
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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 | 0.001 |
| 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 | 0.001 |
| 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".