Storytelling Is Intrinsically Mentalistic: A Functional Magnetic Resonance Imaging Study of Narrative Production across Modalities
Bibliographic record
Abstract
People utilize multiple expressive modalities for communicating narrative ideas about past events. The three major ones are speech, pantomime, and drawing. The current study used functional magnetic resonance imaging to identify common brain areas that mediate narrative communication across these three sensorimotor mechanisms. In the scanner, participants were presented with short narrative prompts akin to newspaper headlines (e.g., "Surgeon finds scissors inside of patient"). The task was to generate a representation of the event, either by describing it verbally through speech, by pantomiming it gesturally, or by drawing it on a tablet. In a control condition designed to remove sensorimotor activations, participants described the spatial properties of individual objects (e.g., "binoculars"). Each of the three modality-specific subtractions produced similar results, with activations in key components of the mentalizing network, including the TPJ, posterior STS, and posterior cingulate cortex. Conjunction analysis revealed that these areas constitute a cross-modal "narrative hub" that transcends the three modalities of communication. The involvement of these areas in narrative production suggests that people adopt an intrinsically mentalistic and character-oriented perspective when engaging in storytelling, whether using speech, pantomime, or drawing.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".