Electrophysiological markers of voice familiarity
Bibliographic record
Abstract
Our ability to discriminate and recognize human voices is amongst the most important functions of the human auditory system. The current study sought to determine whether electrophysiological markers could be used as objective measures of voice familiarity, by looking at the electrophysiological responses [mismatch negativity (MMN) and P3a] when the infrequent stimulus presented is a familiar voice as opposed to an unfamiliar voice. Results indicate that the MMN elicited by a familiar voice is greater than that elicited by an unfamiliar voice at FCz. The familiar voice also produced a greater P3a wave than that triggered by the unfamiliar voice at Fz. As both the MMN and the P3a were elicited as participants were instructed not to pay attention to incoming stimulation, these findings suggest that voice recognition is a particularly potent preattentive process whose neural representations can be objectively described through electrophysiological assessments.
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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) | 0.002 | 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".