Changes in Auditory Cortex Parallel Rapid Perceptual Learning
Bibliographic record
Abstract
Learning perceptual skills is characterized by rapid improvements in performance within the first hour of training (fast perceptual learning) followed by more gradual improvements that take place over several daily practice sessions (slow perceptual learning). Although it is widely accepted that slow perceptual learning is accompanied by enhanced stimulus representation in sensory cortices, there is considerable controversy about the neural substrates underlying early and rapid improvements in learning perceptual skills. Here we measured event-related brain potentials while listeners were presented with 2 phonetically different vowels. Listeners' ability to identify both vowels improved gradually during the first hour of testing and was paralleled by enhancements in an early evoked response ( approximately 130 ms) localized in the right auditory cortex and a late evoked response ( approximately 340 ms) localized in the right anterior superior temporal gyrus and/or inferior prefrontal cortex. These neuroplastic changes depended on listeners' attention and were preserved only if practice was continued; familiarity with the task structure (procedural learning) was not sufficient. We propose that the early increases in cortical responsiveness reflect goal-directed changes in the tuning properties of auditory neurons involved in parsing concurrent speech signals. Importantly, the neuroplastic changes occurred rapidly, demonstrating the flexibility of human speech segregation mechanisms.
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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 | 0.001 |
| 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 | 0.001 |
| 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".