Real-life and posed vocalizations to lottery wins differ fundamentally in their perceived valence.
Bibliographic record
Abstract
A basic premise of classic emotion theories is that distinct emotional experiences yield distinct emotional vocalizations-each informative of its situational context. Furthermore, it is commonly assumed that emotional vocalizations become more distinct and diagnostic as their intensity increases. Critically, these theoretical assumptions largely rely on research utilizing posed vocal reactions of actors, which may be overly simplified and stereotypical. While recent work suggests that intense, real-life vocalizations may be nondiagnostic, the exact way in which increasing degrees of situational intensity affect the perceived valence of real-life versus posed expressions remains unknown. Here we compared real-life and posed vocalizations to winning increasing amounts of money in the lottery. Results show that while posed vocalizations are perceived as positive for both low- and high-sum wins, real-life vocalizations are perceived as positive only for low-sum wins, but as negative for high-sum wins. These findings demonstrate the potential gaps between real-life and posed expressions and highlight the role of situational intensity in driving perceptual ambiguity for real-life emotional expressions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 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) | 0.003 | 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".