Impact of Multi-Robot Presence and Anthropomorphism on Human Cognition and Emotion
Bibliographic record
Abstract
Exploring how robots impact human cognition and emotions has become increasingly important as robots gradually become ubiquitous in our lives. In this study, we investigate the impact of robotic presence on human cognition and emotion by examining various robot parameters such as anthropomorphism, number of robots, and multi-robot motion patterns. 16 participants completed two cognitive tasks in the presence of anthropomorphic and non-anthropomorphic robots, alone, and with a human nearby. The non-anthropomorphic robot conditions were further varied in the number of robots and their motion patterns. We find that increasing the number of non-anthropomorphic robots generally leads to slower performance, but coordinated patterned motions can lower the completion time compared to random movements. An anthropomorphic robot induces an increased level of feelings of being judged compared to a non-anthropomorphic robot. These findings provide preliminary insights into how designers or users can purposefully integrate robots into our environment by understanding the effects of anthropomorphism, number of robots, and multi-robot motion patterns on human cognition and emotion.
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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".