Investigating Robot Influence on Human Behaviour By Leveraging Entrainment Effects
Bibliographic record
Abstract
Humans naturally tend to synchronize their movements with others, a phenomenon known as the entrainment effect, whether voluntarily or involuntarily. This phenomenon extends to interactions between humans and robots, which could have either positive or negative consequences for the human partner. We propose a human-subject study aimed at investigating the use of robots to influence human behaviour through entrainment in diverse Human-Robot Interaction (HRI) scenarios. The current work involves two human-subject experiments investigating the impact of robots on short-term human behaviour, encompassing human-human and human-robot interactions. The goal is to comprehend how variations in robot actions, such as movement frequency during repetitive tasks, influence human perceptions and behaviours in collaborative lab-based settings. Another objective is to investigate the factors that make participants aware of the entrainment effect during HRI. The preliminary results of the HHI experiment provide evidence that individuals tend to synchronize their movements with another person.
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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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".