Correlated expression of the body, face, and voice during character portrayal in actors
Bibliographic record
Abstract
Actors are required to engage in multimodal modulations of their body, face, and voice in order to create a holistic portrayal of a character during performance. We present here the first trimodal analysis, to our knowledge, of the process of character portrayal in professional actors. The actors portrayed a series of stock characters (e.g., king, bully) that were organized according to a predictive scheme based on the two orthogonal personality dimensions of assertiveness and cooperativeness. We used 3D motion capture technology to analyze the relative expansion/contraction of 6 body segments across the head, torso, arms, and hands. We compared this with previous results for these portrayals for 4 segments of facial expression and the vocal parameters of pitch and loudness. The results demonstrated significant cross-modal correlations for character assertiveness (but not cooperativeness), as manifested collectively in a straightening of the head and torso, expansion of the arms and hands, lowering of the jaw, and a rise in vocal pitch and loudness. These results demonstrate what communication theorists refer to as "multichannel reinforcement". We discuss this reinforcement in light of both acting theories and theories of human communication more generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.005 | 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 teacher head, 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".