Sensorimotor experience in speech acquisition : behavioural and neural evidence from preverbal infants
Bibliographic record
Abstract
An infant’s ability to acquire the speech of their native language is a foundational developmental milestone. Human speech is multisensory, such that speaking produces highly correlated changes in signals across modalities (auditory, visual and sensorimotor) that listeners can detect. The root of multisensory speech perceptual capacities – in particular, the role of sensorimotor influences on auditory speech perception – can provide immense insights to the development of the human speech and language systems. In this dissertation, I advance understanding of the role of sensorimotor information in speech acquisition in three domains: methodological, empirical and theoretical. On the methodological front, I develop a combined electroencephalogram (EEG) articulator-inhibition paradigm to examine the neural mechanisms underlying sensorimotor-auditory speech interactions in infants. The novel EEG methodology enabled me to examine how sensorimotor input influences phonetic processing. Empirically, in four experiments in Chapter 2, I provide new behavioural evidence that preventing the movement of the articulator required to produce particular phones disrupts infants’ auditory discrimination, and show that this applies not only to an acoustically difficult non-native but also to a native phonetic contrast that is more acoustically distinct. In Chapter 3, I present the first neuroimaging evidence that oral-motor inhibition disrupts a phonetic discriminative response to a contrast with a place-of-articulation concordant to the inhibited articulator movement. The timing of the response shows the disruption to be simultaneous to phonetic processing. These empirical contributions with infants at two developmental timepoints support the hypothesis that sensitivity to the sensorimotor features corresponding to the articulatory dimensions that results in a particular acoustic speech output exist prior to relevant production experience. In Chapter 4, I propose a novel framework for understanding these effects. While preverbal infants have yet to accrue experience-based knowledge of the sensorimotor consequences of speech, sensorimotor mapping of the articulatory space that corresponds to the acoustic dimensions of speech may be available to young infants through early emerging, some endogenous, motor movements. Combined with the anatomical and functional human speech and language network present from the third-trimester, the mapping of this articulatory space may be an important foundation for acquisition of speech and language.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".