I like the way you move: Personality perception in animated talking heads
Bibliographic record
Abstract
Humans form stable impressions of others' personalities, even after only a brief social exchange. But on what dynamic features are these impressions based? Previous research has focused on ratings of human actors trying to convey different personalities. In the present research we examined this question with three-dimensional animated heads that were programmed to display various emotions and dynamic movements during the delivery of an approximately 15-second voice-track that was the same in all conditions. These heads can be programmed independently at the level of morphology, emotional expression, and dynamic movement (e.g., nodding, blinking, turning). We designed four different personality types by combining two levels of affiliation (low, high) and two levels of dominance (low, high) (Wiggins et al., 1988). As a first approximation, we associated two basic emotions with each of these personalities (e.g., surprise and joy with high-affiliation, high-dominance; fear and sadness with low-affiliation, low-dominance) and we associated two dynamic head moves with each level of dominance and affiliation (e.g., frequent blinking and nodding with low dominance). Participants in Experiment 1 watched 8 different head morphologies acting out each of these 4 personalities and rated them using a standard personality adjective scales. Other participants in Experiment 2 rated the strength of the emotions. The results indicated that it is possible to implement plausible and stable personality differences in animated heads using this combination of emotional expressions and dynamic head movement.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".