A Context-Sensitive Neural Hierarchy for Evaluating Temporal Structure in Primate Vocalizations
Bibliographic record
Abstract
Abstract Understanding how the brain encodes temporal order in communication is central to explaining how complex interactions are perceived as coherent events. In humans, disrupting the sequence of words or scenes abolishes characteristic activity in higher-order networks, but whether similar mechanisms exist in nonhuman primates remains unknown. Here we used ultra–high-field fMRI (9.4 T) in awake marmosets to test how the marmoset brain evaluates temporal structure in natural conspecific vocalizations. Animals heard vocal sequences from three social contexts (angry, conversational, food-related) presented in intact, reversed, or randomized order, with call identity held constant. Disrupting sequence order altered responses across a distributed cortical–subcortical network. Contrast to reversed order, intact sequences drove stronger activation in prefrontal, cingulate, parietal, and somatosensory regions, whereas randomization produced the most widespread disruptions, additionally recruiting motor, insular, hippocampal, and thalamic territories. Uni- and multivariate analyses revealed a core network—including prefrontal area 8, cingulate areas 24/32, somatosensory cortex, and parietal Tpt—consistently sensitive to temporal coherence, with broader recruitment under severe disruption. Network-level dynamics further varied by context: conversation elicited earlier sensitivity to sequence disruptions, angry peaked later, and food built more gradually. These findings provide the first whole-brain evidence that marmosets engage hierarchically organized, context-sensitive networks to evaluate multi-agent vocal sequence structure, establishing a cross-species bridge to human narrative 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 | 0.000 |
| 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) | 0.001 | 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".