Are there Nonverbal Cues to Commitment? An Exploratory Study Using the Zero-Acquaintance Video Presentation Paradigm
Why this work is in the frame
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Bibliographic record
Abstract
Altruism is difficult to explain evolutionarily if subtle cheaters exist in a population ( Trivers, 1971 ). A pathway to the evolutionary maintenance of cooperation is nonverbal altruist-detection. One adaptive advantage of nonverbal altruist-detection is the formation of trustworthy division of labour partnerships ( Frank, 1988 ). Three studies were designed to test a fundamental assumption behind altruistic partner preference models. In the first experiment perceivers (blind with respect to target altruism level) made assessments of video-clips depicting self-reported altruists and self-reported non-altruists. Video-clips were designed with attempts to control for attractiveness, expressiveness, role-playing ability, and verbal content. Overall perceivers rated altruists as more “helpful” than non-altruists. In a second experiment manipulating the payoffs for cooperation, perceivers (blind with respect to payoff condition and altruism level) assessed altruists who were helping others as more “concerned” and “attentive” than non-altruists. However perceivers assessed the same altruists as less “concerned” and “attentive” than non-altruists when the payoffs were for self. This finding suggests that perceivers are sensitive to nonverbal indicators of selfishness. Indeed the self-reported non-altruists were more likely than self-reported altruists to retain resources for themselves in an objective measure of cooperative tendencies (i.e. a dictator game). In a third study altruists and non-altruists' facial expressions were analyzed. The smile emerged as a consistent cue to altruism. In addition, altruists exhibited more expressions that are under involuntary control (e.g., orbicularis oculi) compared to non-altruists. Findings suggest that likelihood to cooperate is signaled nonverbally and the putative cues may be under involuntary control as predicted by Frank (1988) .
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 it