Detecting auditory temporal regularities: electrophysiological index of tracking and identification of disambiguating information
Bibliographic record
Abstract
Learning, and detection of regularities allows us to make predictions about our environment and process stimuli more efficiently. Using EEG, we found an electrophysiological signature linked to how the brain uses and interprets auditory information in the time domain. We used sequences of five tones with different pitches, with one of three distinct temporal regularities, using a short-long-short-long, long-short-long-short, or isochronous ISI pattern. They were designed so the second tone carried temporal-sequence information, by being presented after a short, medium, or long ISI, allowing recognition of the pattern. Participants heard two tone sequences with the same temporal regularity and had to indicate if the tone pitches were identical. In one experiment, the three types of regularities were randomly intermixed, whereas they were blocked in a control experiment. A frontal and frontocentral positivity increased for the first set of the first experiment (when temporal pattern was not previously known), compared to that same set in the control experiment (pattern known), starting around the earliest time the second tone could be presented, and peaking shortly after actual tone onset. Although these temporal patterns were task irrelevant, and most participants were unaware of them when asked, our results suggest the brain disambiguates its variable environment based on the earliest available information, and that it does so rapidly, pre-attentively, and automatically.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".