Sustained neural activity correlates with rapid perceptual learning of auditory patterns
Bibliographic record
Abstract
Repeating structures forming regular patterns are common in sounds. Learning such patterns may enable accurate perceptual organization. In five experiments, we investigated the behavioral and neural signatures of rapid perceptual learning of regular sound patterns. We show that recurring (compared to novel) patterns are detected more quickly and increase sensitivity to pattern deviations and to the temporal order of pattern onset relative to a visual stimulus. Sustained neural activity reflected perceptual learning in two ways. Firstly, sustained activity increased earlier for recurring than novel patterns when participants attended to sounds, but not when they ignored them; this earlier increase mirrored the rapid perceptual learning we observed behaviorally. Secondly, the magnitude of sustained activity was generally lower for recurring than novel patterns, but only for trials later in the experiment, and independent of whether participants attended to or ignored sounds. The late manifestation of sustained activity reduction suggests that it is not directly related to rapid perceptual learning, but to a mechanism that does not require attention to sound. In sum, we demonstrate that the latency of sustained activity reflects rapid perceptual learning of auditory patterns, while the magnitude may reflect a result of learning, such as better prediction of learned auditory patterns.
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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 | 0.000 |
| 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".