Changes in Sensory Evoked Responses Coincide with Rapid Improvement in Speech Identification Performance
Bibliographic record
Abstract
Perceptual learning is sometimes characterized by rapid improvements in performance within the first hour of training (fast perceptual learning), which may be accompanied by changes in sensory and/or response pathways. Here, we report rapid physiological changes in the human auditory system that coincide with learning during a 1-hour test session in which participants learned to identify two consonant vowel syllables that differed in voice onset time. Within each block of trials, listeners were also presented with a broadband noise control stimulus to determine whether changes in auditory evoked potentials were specific to the trained speech cue. The ability to identify the speech sounds improved from the first to the fourth block of trials and remained relatively constant thereafter. This behavioral improvement coincided with a decrease in N1 and P2 amplitude, and these learning-related changes differed from those observed for the noise stimulus. These training-induced changes in sensory evoked responses were followed by an increased negative peak (between 275 and 330 msec) over fronto-central sites and by an increase in sustained activity over the parietal regions. Although the former was also observed for the noise stimulus, the latter was specific to the speech sounds. The results are consistent with a top-down nonspecific attention effect on neural activity during learning as well as a more learning-specific modulation, which is coincident with behavioral improvements in speech identification.
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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 | 0.002 |
| 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".