Feature-specific cortical plasticity after rapid perceptual learning during speech segregation: a MEG study
Bibliographic record
Abstract
This study examined whether 1-hour perceptual training could elicit feature-specific improvement of performance and corresponding cortical plasticity in humans during speech segregation by using magnetoencephalography (MEG). One group of participants learned to segregate concurrent vowels by using difference in fundamental frequency (f0) while the other group learned to use difference in sound location. MEG recordings were conducted after the training and required participants to identify the two different vowels, which may have the same f0 and location or differ in f0 only, location only or both f0 and location. Compared to Control Group who didn't receive pre-scan training, the trained groups showed behavioral improvements specific to the trained cues which were paralleled by feature-specific changes on brain activities. That is, f0-difference-induced changes in dipole source-waveforms in auditory cortex were only modulated in Frequency Group, while location-difference-induced changes were only modulated in Location Group. Furthermore, Frequency Group showed stronger activations in auditory “what” pathway than Location Group when processing f0-difference, while Location Group revealed stronger activation in auditory “where” pathway than Frequency Group when processing location-difference. The double-disassociation in both behaviors and neuromagnetic responses indicates that rapid perceptual learning could elicit highly feature-specific plasticity in human cortex during speech segregation.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".