Are there Nonverbal Cues to Commitment? An Exploratory Study Using the Zero-Acquaintance Video Presentation Paradigm
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) .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".