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Did You Notice? Unveiling Robot-Induced Synchronization in Human-Robot Interaction

2025· article· W4415524171 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsSynchronization (alternating current)AnimacyRobotNoticePerceptionPhenomenonHuman–robot interactionPoint (geometry)

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.281
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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