Role specialization enables superior task performance by human dyads than individuals

It is generally accepted that collaboration yields better performance than when the same task is performed by individuals operating alone. Is collaboration always superior to individual performances, or do its benefits depend on whether collaborating individuals have the same or different roles? Here, participants performed tasks to transport a virtual beam while keeping it horizontal. Twin robotic manipulanda were coupled to the simulated beam with either symmetric or asymmetric dynamics. In paired modes, two participants grasped the robot handles, whereas in solo modes, individual participants grasped both handles with their two hands. Computational simulations revealed that this task could be successfully performed regardless of whether roles were the same or different for each beam dynamics. However, pairs performed better than individuals after two participants spontaneously assumed different roles, co-adapting by learning in asymmetric dynamics, with one participant leading the movement and exerting more force than the other. In contrast, individuals performed better than pairs if pairs did not assume different roles. Our findings suggest that better performance by pairs than individuals is not a general phenomenon, but that collaboration only yields better performance if role specialization spontaneously emerges in such interactions.

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Publication Details

Journal
The International Journal of Robotics Research
Published
2025-08-25
DOI
https://doi.org/10.1177/02783649251363274
Primary Topic
Action Observation and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

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article

Role specialization enables superior task performance by human dyads than individuals

Yuzuru Doibata, Marco Santello, Jun Morimoto, Asuka Takai et al.
The International Journal of Robotics Research
Action Observation and Synchronization
article

Role specialization enables superior task performance by human dyads than individuals

Yuzuru Doibata, Marco Santello, Jun Morimoto, Asuka Takai, Mitsuo Kawato, Keivan Mojtahedi, Giuseppe Lisi, Toshiki Tsuchiya, Qiushi Fu, Toshinori Yoshioka
article en

Abstract

It is generally accepted that collaboration yields better performance than when the same task is performed by individuals operating alone. Is collaboration always superior to individual performances, or do its benefits depend on whether collaborating individuals have the same or different roles? Here, participants performed tasks to transport a virtual beam while keeping it horizontal. Twin robotic manipulanda were coupled to the simulated beam with either symmetric or asymmetric dynamics. In paired modes, two participants grasped the robot handles, whereas in solo modes, individual participants grasped both handles with their two hands. Computational simulations revealed that this task could be successfully performed regardless of whether roles were the same or different for each beam dynamics. However, pairs performed better than individuals after two participants spontaneously assumed different roles, co-adapting by learning in asymmetric dynamics, with one participant leading the movement and exerting more force than the other. In contrast, individuals performed better than pairs if pairs did not assume different roles. Our findings suggest that better performance by pairs than individuals is not a general phenomenon, but that collaboration only yields better performance if role specialization spontaneously emerges in such interactions.

The International Journal of Robotics ResearchVol. 45(3)
University of Central Florida (US), Kyoto University (JP), X-Fab (Germany) (DE), Kyoto College of Graduate Studies for Informatics (JP), Advanced Telecommunications Research Institute International (JP), Arizona State University (US)
Japan Agency for Medical Research and Development, Kayamori Foundation of Informational Science, New Energy and Industrial Technology Development Organization
Openalex Percentile: Top 74%
Action Observation and Synchronization
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