Functional organization of voice patches in marmosets and cross-species comparisons with macaques and humans
Bibliographic record
Abstract
We recently identified voice-selective patches in the marmoset auditory cortex, but whether these regions specifically encode conspecific vocalizations over heterospecific ones-and whether they share a similar functional organization with those of humans and macaques-remains unknown. In this study, we used ultra-high-field functional magnetic resonance imaging (fMRI) in awake marmosets to characterize the cortical organization of vocalization processing and directly compare it with prior human and macaque data. Using an established auditory stimulus set designed for cross-species comparisons-including conspecific, heterospecific (macaque and human), and non-vocal sounds-we identified voice-selective patches showing preferential responses to conspecific calls. Robust responses were found in three temporal voice patches (anterior, middle, and posterior) and in the pregenual anterior cingulate cortex (pgACC), all showing significantly stronger responses to conspecific vocalizations than to other sound categories. A key finding was that, while the temporal patches also showed weak responses to heterospecific calls, the pgACC responded exclusively to conspecific vocalizations. Representational similarity analysis (RSA) revealed that dissimilarity patterns across these patches aligned exclusively with the marmoset-specific categorical model, indicating species-selective representational structure. Cross-species RSA comparisons revealed conserved representational geometry in the primary auditory cortex (A1) but species-specific organization in anterior temporal areas. These findings highlight shared principles of vocal communication processing across primates.
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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 | 0.001 | 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) | 0.001 | 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".