Distinct neural temporal architectures encode rapid social expressions and sustained internal mood states
Bibliographic record
Abstract
Affective processing operates across multiple temporal scales, from rapid social signaling through facial expressions to sustained internal mood states, yet the neural computational principles governing these different timescales remain unclear. Understanding how the brain implements distinct temporal architectures for momentary versus persistent affective phenomena is important to comprehending emotional processing and developing objective biomarkers for psychiatric conditions. Here, we introduced a multimodal approach combining automated facial expression monitoring and continuous intracranial electroencephalography in 2,037 electrode contacts across 16 epilepsy patients, over multiple days. Of these, 15 and 12 patients met criteria for facial expression and for mood analysis, respectively. Among patients meeting criteria, we captured 1,396 naturalistic smiles, and 3,746 neutral expressions - separated by at least 10 seconds, alongside 336 periodic mood assessments. This paradigm revealed distinct behavioral and neural computational architectures. Aperiodic neural activity in the lateral temporal cortex (79.5% accuracy) encoded facial expressions with high cross-participant generalizability. Mood states, however, showed different encoding patterns. Facial expressions provided no consistent mood indicators across participants. Critically, low-gamma power dynamics in limbic regions encoded mood states in only a subset of individuals (5 of 12 participants) with expression-mood behavioral correlations, suggesting a distinct encoding phenotype. Cross-domain analysis confirmed computational independence: neural features optimized for facial expression decoding failed to predict sustained mood states, and vice versa. These findings suggest that multiple neural mechanisms may influence underlying affective processing, with variations in their contributions between individuals. The results provide a framework for understanding individual differences in neural mood representation and establish methodological approaches for objective measurement of naturalistic affective behaviors.
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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) | 0.001 | 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".