Processing of the Same Narrative Stimuli Elicits Common Functional Connectivity Dynamics Between Individuals
Bibliographic record
Abstract
ABSTRACT It has been suggested that the richness of conscious experience can be directly linked to the richness of brain state repertories. Brain states change depending on our environment and activities we engage in by taking both external and internally derived information into account. It has been shown that high-level sensory stimulation changes local brain activity and induces neural synchrony across participants. However, the dynamic interplay of cognitive processes that underlie moment-to-moment information processing remains poorly understood. Using naturalistic movies as an ecological laboratory model of the real world, here we assess how the processing of complex naturalistic stimuli alters the dynamics of brain networks’ interactions, and how these in turn support information processing. Participants underwent fMRI recordings during movie watching, scrambled movie watching, and rest. Measuring phase-synchrony between different brain networks, we computed whole-brain connectivity patterns. We showed that specific connectivity patterns were associated with each experimental condition. We found a higher synchronization of brain patterns across participants during movie watching compared to resting state and scrambled movie conditions. Moreover, synchronization increased during the most engaging parts of the movie. The synchronization dynamics across participants were associated with suspense; more suspenseful scenes induced higher synchronization. These results suggest that processing of the same high-level information elicits common neural dynamics among individuals and that whole-brain functional connectivity tracks variations in the processed information and the subjective experience.
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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 | 0.002 |
| 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".