Learning-Induced Changes in the Cerebral Processing of Voice Identity
Bibliographic record
Abstract
Temporal voice areas showing a larger activity for vocal than non-vocal sounds have been identified along the superior temporal sulcus (STS); more voice-sensitive areas have been described in frontal and parietal lobes. Yet, the role of voice-sensitive regions in representing voice identity remains unclear. Using a functional magnetic resonance adaptation design, we aimed at disentangling acoustic- from identity-based representations of voices. Sixteen participants were scanned while listening to pairs of voices drawn from morphed continua between 2 initially unfamiliar voices, before and after a voice learning phase. In a given pair, the first and second stimuli could be identical or acoustically different and, at the second session, perceptually similar or different. At both sessions, right mid-STS/superior temporal gyrus (STG) and superior temporal pole (sTP) showed sensitivity to acoustical changes. Critically, voice learning induced changes in the acoustical processing of voices in inferior frontal cortices (IFCs). At the second session only, right IFC and left cingulate gyrus showed sensitivity to changes in perceived identity. The processing of voice identity appears to be subserved by a large network of brain areas ranging from the sTP, involved in an acoustic-based representation of unfamiliar voices, to areas along the convexity of the IFC for identity-related processing of familiar voices.
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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 | 0.001 |
| 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".