The Neurophysiology of Auditory Perception: From Single Units to Evoked Potentials
Bibliographic record
Abstract
Evoked electric potential and magnetic field studies have the immense benefit that they can be conducted in awake, behaving humans and can be directly correlated with aspects of perception. As such, they are powerful objective indicators of perceptual properties. However, given a set of evoked potential and/or evoked field waveforms and their source locations, obtained for an exhaustive set of stimuli and stimulus contrasts, is it possible to determine blindly, i.e. predict, what the stimuli or stimulus contrasts were? If this can be done with some success, then a useful amount of information resides in scalp-recorded activity for, e.g., the study of auditory speech processing. In this review, we compare neural representations based on single-unit and evoked response activity for vowels and consonant-vowel phonemes with distinctions in formant glides and voice onset time. We conclude that temporal aspects of evoked responses can track some of the dominant response features present in single-unit activity. However, N1 morphology does not reliably predict phonetic identification of stimuli varying in voice onset time, and the reported appearance of a double-peak onset response in aggregate recordings from the auditory cortex does not indicate a cortical correlate of the perception of voicelessness. This suggests that temporal aspects of single-unit population activity are likely not inclusive enough for representation of categorical perception boundaries. In contrast to population activity based on single-unit recording, the ability to accurately localize the sources of scalp-evoked activity is one of the bottlenecks in obtaining an accessible neurophysiological substrate of perception. Attaining this is one of the requisites to arrive at the prospect of blind determination of stimuli on the basis of evoked responses. At the current sophistication level of recording and analysis, evoked responses remain in the realm of extremely sensitive objective indicators of stimulus change or stimulus differences. As such, they are signs of perceptual activity, but not comprehensive representations thereof.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".