Mapping the Changing Neural Architecture of Narrative Processing Using Naturalistic Stimuli: an fMRI Study
Bibliographic record
Abstract
Abstract A narrative is a coherent representation of actual or fictional events designed to connect experiences. Narratives provide a unique opportunity to investigate brain functions in scenarios more closely resembling real-world experiences. However, most neuroimaging studies examining narrative formation have utilized static stimuli that fail to capture the intricacies of narrative construction in everyday life, particularly how cognitive demands change over the course of narrative processing. The current research uses functional magnetic resonance imaging (fMRI) to examine dynamic narrative processing over the course of a full-length audiovisual narrative. We examined changes in neural synchrony (as quantified by intersubject correlations) in areas related to semantic memory, episodic memory, and visuospatial attention between the beginning, middle, and end of the narrative. Results from two experiments identified two core narrative processing networks responsible for constructing coherent representations across extended timescales. The first network is associated with the early narrative construction, and includes the right intraparietal sulcus/superior parietal lobule, bilateral angular gyrus, bilateral precuneus, and left fusiform gyrus. The second network consists of the right ventral frontal cortex and bilateral parahippocampal cortices, and is associated with longer term narrative integration. Together, these regions provide the framework for successful narrative processing during naturalistic stimuli.
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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 | 0.001 |
| 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".