Does swallowing bootstrap speech learning
Bibliographic record
Abstract
Learning speech movements can be modeled as searching for sets of muscle activations in a high-dimensional space that satisfy task-specific criteria relevant to the language learner. An unstructured search of such a space is problematic: the high dimensionality makes non-heuristic search inefficient, and the number of redundant solutions for a given task makes predicting muscle activation difficult. Although most speech movements must be learned, humans can produce many of our most complex oral motor behaviors (swallowing, suckling, vocalizing, smiling, etc.) at birth. This indicates a degree to which the biomechanical and neural structures needed for complex action in the vocal tract appear to be built-in. A model is described, based on neurophysiological and computational motor research, in which action is governed by neuromuscular modules which can emerge and/or change ontogenetically through use. Simulations of tongue movements were conducted using a realistic 3D biomechanical model in ArtiSynth (www.artisynth.org; e.g., Stavness et al., 2012, Gick et al. 2014). Results of these simulations demonstrate that the neuromuscular activations leading to full oral swallowing closure are a subset of those that result in tongue bracing. This implies that submodules of the swallowing gesture can be recruited for use in tongue bracing, and makes a search of this space much simpler when the swallowing activation is used as a starting point. These findings are consistent with the view that phylogenetically encoded structures such as those needed for swallowing (e.g., MacNeilage 2008; Studdert-Kennedy & Goldstein 2003) bootstrap speech learning.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".