Generalization of perceptual learning of vocoded speech.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent work demonstrates that learning to understand noise-vocoded (NV) speech alters sublexical perceptual processes but is enhanced by the simultaneous provision of higher-level, phonological, but not lexical content (Hervais-Adelman, Davis, Johnsrude, & Carlyon, 2008), consistent with top-down learning (Davis, Johnsrude, Hervais-Adelman, Taylor, & McGettigan, 2005; Hervais-Adelman et al., 2008). Here, we investigate whether training listeners with specific types of NV speech improves intelligibility of vocoded speech with different acoustic characteristics. Transfer of perceptual learning would provide evidence for abstraction from variable properties of the speech input. In Experiment 1, we demonstrate that learning of NV speech in one frequency region generalizes to an untrained frequency region. In Experiment 2, we assessed generalization among three carrier signals used to create NV speech: noise bands, pulse trains, and sine waves. Stimuli created using these three carriers possess the same slow, time-varying amplitude information and are equated for naïve intelligibility but differ in their temporal fine structure. Perceptual learning generalized partially, but not completely, among different carrier signals. These results delimit the functional and neural locus of perceptual learning of vocoded speech. Generalization across frequency regions suggests that learning occurs at a stage of processing at which some abstraction from the physical signal has occurred, while incomplete transfer across carriers indicates that learning occurs at a stage of processing that is sensitive to acoustic features critical for speech perception (e.g., noise, periodicity).
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.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it