Intentionality and categories in speech motor control
Bibliographic record
Abstract
Actions are organized around goals or intentions. In speech production, there has been no agreement on how best to discuss speech goals. However, the auditory feedback perturbation methodology provides a window into the nature of speech goals. To the extent that subjects are sensitive to variation in an acoustic attribute, this attribute must be part of the controlled intention of articulation. In this presentation, we will review a series of studies that speak to this issue. In one study, we examined how intentionality of speech production influences compensatory formant production by instructing subjects to use a cognitive strategy in order to make the feedback sound consistent with the intended vowel. In other studies, we have explored the specificity of vowel formant compensation by comparing cross-language differences. The results indicate that speech goals are (1) very specific, defined by a phonemic category and its relationship with neighboring categories, and (2) multivariate. We will discuss these results by contrasting compensatory behaviors in reaching and limb movements to those observed in speech studies. The presence of a system of categories in speech may result in differences in the way speech goals are represented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| 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".