Neural responses to uninterrupted natural speech can be extracted with precise temporal resolution
Bibliographic record
Abstract
The human auditory system has evolved to efficiently process individual streams of speech. However, obtaining temporally detailed responses to distinct continuous natural speech streams has hitherto been impracticable using standard neurophysiological techniques. Here a method is described which provides for the estimation of a temporally precise electrophysiological response to uninterrupted natural speech. We have termed this response AESPA (Auditory Evoked Spread Spectrum Analysis) and it represents an estimate of the impulse response of the auditory system. It is obtained by assuming that the recorded electrophysiological function represents a convolution of the amplitude envelope of a continuous speech stream with the to-be-estimated impulse response. We present examples of these responses using both scalp and intracranially recorded human EEG, which were obtained while subjects listened to a binaurally presented recording of a male speaker reading naturally from a classic work of fiction. This method expands the arsenal of stimulation types that can now be effectively used to derive auditory evoked responses and allows for the use of considerably more ecologically valid stimulation parameters. Some implications for future research efforts are presented.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".