Bibliographic record
Abstract
Auditory input is essential for normal speech development and plays a key role in speech production throughout the life span. In traditional models, auditory input plays two critical roles: 1) establishing the acoustic correlates of speech sounds that serve, in part, as the targets of speech production, and 2) as a source of feedback about a talker's own speech outcomes. This talk will focus on both of these roles, describing a series of studies that examine the capacity of children and adults to adapt to real-time manipulations of auditory feedback during speech production. In one study, we examined sensory and motor adaptation to a manipulation of auditory feedback during production of the fricative “s”. In contrast to prior accounts, adaptive changes were observed not only in speech motor output but also in subjects' perception of the sound. In a second study, speech adaptation was examined following a period of auditory–perceptual training targeting the perception of vowels. The perceptual training was found to systematically improve subjects' motor adaptation response to altered auditory feedback during speech production. The results of both studies support the idea that perceptual and motor processes are tightly coupled in speech production learning, and that the degree and nature of this coupling may change with development.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".