Generalization of perceptual learning of vocoded speech.
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).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".