Investigating Positive Psychology Principles in Affective Robotics
Bibliographic record
Abstract
Positive emotions play a fundamental role in promoting well-being, social bonding, and encouraging people to flourish. We investigated an affective robot's potential to promote positive moods in human groups during a collaborative task. A between-subject experiment (N=39 teams, 78 participants) was conducted to compare the improvement in participants' mood and robot's impression after conducting a collaborative task with an affective robot showing either positive or neutral behaviors. We found that self-reported valence and arousal increased in human participants when interacting with the affective robot regardless of the robot's perceived mood. Additionally, we discovered that participants' likeability of the robot increased when interacting with a positive robot, while likeability decreased when interacting with a neutral robot. These results suggest that even if the evidence for emotional contagion between an affective robot and human group members is not conclusive, participants felt more positive after interacting with robots showing affective behaviors in human-robot team interactions.
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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 | 0.002 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".