Resolving Precise Temporal Processing Properties of the Auditory System Using Continuous Stimuli
Bibliographic record
Abstract
In natural environments complex and continuous auditory stimulation is virtually ubiquitous. The human auditory system has evolved to efficiently process an infinity of everyday sounds, which range from short, simple bursts of noise to signals with a much higher order of information such as speech. Investigation of temporal processing in this system using the event-related potential (ERP) technique has led to great advances in our knowledge. However, this method is restricted by the need to present simple, discrete, repeated stimuli to obtain a useful response. Alternatively the continuous auditory steady-state response is used, although this method reduces the evoked response to its fundamental frequency component at the expense of useful information on the timing of response transmission through the auditory system. In this report, we describe a method for eliciting a novel ERP, which circumvents these limitations, known as the AESPA (auditory-evoked spread spectrum analysis). This method uses rapid amplitude modulation of audio carrier signals to estimate the impulse response of the auditory system. We show AESPA responses with high signal-to-noise ratios obtained using two types of carrier wave: a 1-kHz tone and broadband noise. To characterize these responses, they are compared with auditory-evoked potentials elicited using standard techniques. A number of similarities and differences between the responses are noted and these are discussed in light of the differing stimulation and analysis methods used. Data are presented that demonstrate the generalizability of the AESPA method and a number of applications are proposed.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".