Robot humor: How self-irony and Schadenfreude influence people's rating of robot likability
Bibliographic record
Abstract
Humor in robotics is a promising, though not yet significantly researched topic. We performed a user study exploring two different kinds of laughter. In our study, participants observed a robot-robot interaction where an iCat and a NAO robot exhibited different laughing behavior. While NAO laughed at itself (self-irony), the iCat laughed at NAO (Schadenfreude1). Our participants watched four turns of the same robot-robot interaction, with either NAO or the iCat laughing, both robots laughing, or no robot laughing (baseline). After each turn we asked the participants to rate both robots' likability individually. Our results show that the participants liked a robot with a positively attributed form of humor significantly more than its gloating robotic interaction partner. However, likability ratings showed a trend to approach each other when either robot laughed or when both robots laughed together. Both, the higher likability ratings for a robot showing positively attributed humor and the decreasing difference in likability ratings when both robots laugh together, provide proof of the positive effect of humor. While participants' age did not affect likability ratings, there was a significant interaction effect between participants' gender and robot type. Female participants rated the iCat more likable, while male participants liked NAO better. In addition, more neurotic people liked the self-ironic robot more when no robot laughed and more open people like the robot showing Schadenfreude more when both robots laughed.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".