Signal informativeness for sequence structure modulates human auditory cortical responses
Bibliographic record
Abstract
We observed how information about the structure of tone sequences modulates cortical responses in the context of a standard short-term memory (STM) task. Participants heard two sequences of one, three, or five tones (203 ms on, 203 ms off) interspersed by a silent interval (2 s) and decided whether the sequences were the same or different. In experiment 1, sequence length was randomized between trials. During the first sequence, the amplitude of the auditory P2 was larger for the second tone in trials with three tones, and for the second and fourth tone in trials with five tones. We hypothesize the increase in P2 reflected a dynamic disambiguation process because these tones were predictive of a sequence longer than one or three tones. This hypothesis was supported by the absence of P2 amplitude modulation during the second sequence (when sequence length was known). In experiment 2, we blocked trials by sequence length to ensure the effects were not caused by some process related to encoding in STM. There was no P2 amplitude modulation in either the first or second sequences. Thus, tones 2 and 4 had a larger amplitude only when they provided new information about the length of the current tone sequence. To some extent, the auditory N1 also showed those modulations. Independent Component Analysis of the ERPs provided evidence the modulations in P2 amplitude could originate in auditory cortex. These results suggest a rapid dynamic adaptation of auditory cortical responses based on the local informativeness of auditory signals.
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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".