Auditory perceptual learning depends on temporal regularity and certainty
Bibliographic record
Abstract
Detecting and learning structure in sounds is fundamental to human auditory perception. Evidence for auditory perceptual learning comes from previous studies where listeners were better at detecting repetitions of a short noise snippet embedded in longer, ongoing noise when the same snippet recurred across trials compared to when the snippet was novel in each trial. However, previous work has mainly used (a) temporally regular presentations of the repeating noise snippet and (b) highly predictable inter-trial onset timings for the snippet sequences. As a result, it is unclear how these temporal features affect perceptual learning. In five online experiments, participants judged whether or not a repeating noise snippet was present, unaware that the snippet could be unique to that trial or used in multiple trials. In two experiments, temporal regularity was manipulated by jittering the timing of noise-snippet repetitions within a trial. In two subsequent experiments, temporal onset certainty was manipulated by varying the onset time of the entire snippet sequence across trials. We found that both temporal jittering and onset uncertainty reduced auditory perceptual learning. In addition, we observed that these reductions in perceptual learning were ameliorated when the same snippet occurred in both temporally manipulated and unmanipulated trials. Our study demonstrates the importance of temporal regularity and onset certainty for auditory perceptual learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".