Theta oscillations linked to auditory informativeness and context disambiguation
Bibliographic record
Abstract
Accurate predictions and the processing of prediction error signals can be important for efficient interaction with the auditory environment. In a reanalysis of data from Simal et al . (2021), who found that informative tones elicited increased N1 and P2 event-related potential components, we sought to identify electrophysiological indicators in the time-frequency domain associated with disambiguation of the hearing context and prediction of forthcoming stimulation. Participants heard two isochronous sequences of pure tones separated by a silent retention interval. A sequence could contain one, three, or five tones. Fifteen participants heard the three load conditions randomly intermixed. In this case, when sequence length was unknown, the second and fourth tone during encoding contained information allowing the prediction of another tone. Other participants heard the sequences blocked by sequence length, and the second and fourth tone of the sequences provided no new information (and hence were not informative). We used wavelet analysis and Hilbert transform methods to analyse the oscillatory activity related to tone informativeness. We found a significant increase in theta (4-7 Hz) amplitude following a tone that was informative and allowed prediction, in comparison with a tone that carried no predictive information. Previous work suggests increased theta amplitude is linked with task switching and an increase in cognitive control. We suggest informative tones recruit higher-level control processes involved in prediction of upcoming auditory events.
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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 | 0.003 |
| 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".