Did You Notice? Unveiling Robot-Induced Synchronization in Human-Robot Interaction
Bibliographic record
Abstract
Decades of studies in psychology and neuroscience establish that humans naturally synchronize their movements with others. This phenomenon is not limited to Human-Human Interaction (HHI) but also observed in Human-Robot Interaction (HRI), where individuals align their motions to the rhythm of a robot without conscious effort. While such influences are often subtle, characterizing the dynamics of robot-to-human synchronization is critical for designing effective collaborative systems. This study explores whether humans synchronize differently to the movements of robots compared to other humans, and at what point individuals become conscious of the changes in the robot's movements. Our results reveal an asymmetry in synchronization between HHI and HRI. We find that participants are more likely to notice changes in a robot's speed, particularly when the robot speeds up or slows down by over 20% compared to the base speed. Participants' perception of the robot's animacy is also influenced by these speed changes. Building on our findings, we provide insights into how roboticists can design robot behaviours to minimize unwanted influence and respect human autonomy.
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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 | 0.001 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".