Vocal processing networks in the human and marmoset brain
Bibliographic record
Abstract
Abstract Understanding the brain circuitry involved in vocal processing across species is crucial for unraveling the evolutionary roots of human communication. While previous research has pinpointed voice-sensitive regions in primates, direct cross-species comparisons using standardized protocols are limited. This study utilizes ultra-high field fMRI to explore vocal processing mechanisms in humans and marmosets. By employing voice-sensitive regions of interest (ROIs) identified via auditory localizers, we analyzed response time courses to species-specific vocalizations and non-vocal sounds using a dynamic auditory-stimulation paradigm. This approach gradually introduced sounds into white noise over 33 seconds. Results revealed that both species have responsive areas in the temporal, frontal, and cingulate cortices, with a distinct preference for vocalizations. Significant differences were found in the response time courses between vocal and non-vocal sounds, with humans displaying faster responses to vocalizations than marmosets. We also identified a shared antero-ventral auditory pathway in both species for vocal processing, originating from the superior temporal gyrus. Conversely, a posterior-dorsal pathway was more prominent in humans, whereas in marmosets, this pathway processed both sound types similarly. This comparative study sheds light on both conserved and divergent auditory pathways in primates, providing new insights into conspecific vocalization processing.
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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 | 0.000 |
| 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".