Data from: The neuroscience of Romeo and Juliet: an fMRI study of acting
Bibliographic record
Abstract
The current study represents a first attempt at examining the neural basis of dramatic acting. While all people play multiple roles in daily life – for example “spouse” or “employee” – these roles are all facets of the “self” and thus the first-person (1P) perspective. Compared to such everyday role-playing, actors are required to portray other people and to adopt their gestures, emotions, and behaviors. Consequently, actors must think and behave not as themselves but as the characters they are pretending to be. In other words, they have to assume a “fictional first-person” (Fic1P) perspective. In this functional MRI study, we sought to identify brain regions preferentially activated when actors adopt a Fic1P perspective during dramatic role playing. In the scanner, university-trained actors responded to a series of hypothetical questions from either their own 1P perspective or from that of Romeo (male participants) or Juliet (female participants) from Shakespeare’s drama. Compared to responding as oneself, responding in character produced global reductions in brain activity and, particularly, deactivations in the cortical midline network of the frontal lobe, including the dorsomedial and ventromedial prefrontal cortices. Thus, portraying a character through acting seems to be a negative process, perhaps representing a “loss of self”.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".