Simultaneously evoked auditory potentials: A novel paradigm for measuring auditory-evoked electroencephalographic activity at successive levels of the auditory neuraxis
Bibliographic record
Abstract
Recent electrophysiological work has evinced a capacity for plasticity in subcortical auditory nuclei in adults [Skoe and Kraus (2010)]. Similar plastic effects have been measured in cortically generated auditory potentials [e.g., Bosnyak et al. (2007); Näätänen (2008)], but it is unclear how the two interact. Here we present simultaneously evoked auditory potentials (SEAP), a novel paradigm designed to concurrently elicit electrophysiological brain potentials (EEG) from inferior colliculus (IC), thalamus, and primary and secondary auditory cortices. We use a specially designed stimulus consisting of a carrier frequency (500 Hz), amplitude-modulated at the sum of 37 and 81 Hz (depth 100%). We have shown that it elicits a 500 Hz frequency-following response (FFR; generated in IC), 81 (subcortical), and 37 (primary auditory cortex) Hz steady state responses, and mismatch negativity (when there is an occasional change in carrier frequency; secondary auditory cortex). Furthermore, cortical and subcortical processes are linked as the amplitude of the FFR predicts the amplitudes of the 37 and 81 Hz responses. SEAP offers a new window into the dynamics of encoding along the auditory pathway as well as a new and inexpensive tool with which to measure plasticity at multiple levels of the auditory neuraxis in individuals.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".