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Human social motor ...
Human social motor solutions for human-machine interaction in dynamical task contexts
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- Nalepka, Patrick (författare)
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia
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- Lamb, Maurice (författare)
- Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA
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- Kallen, Rachel W. (författare)
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia
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- Shockley, Kevin (författare)
- Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA
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- Chemero, Anthony (författare)
- Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA
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- Saltzman, Elliot (författare)
- Department of Physical Therapy & Athletic Training, Sargent College of Health & Rehabilitation Sciences, Boston University, Boston, MA 02215, USA / Haskins Laboratories, New Haven, CT 06511, USA
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- Richardson, Michael J. (författare)
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia
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(creator_code:org_t)
- 2019-01-07
- 2019
- Engelska.
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 116:4, s. 1437-1446
- Relaterad länk:
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https://www.pnas.org...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of humanâmachine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Humanâhuman and humanâartificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of humanâhuman dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a âTuring-likeâ methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Människa-datorinteraktion (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Human Computer Interaction (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)
Nyckelord
- Human-machine interaction
- Multiagent coordination
- Shepherding
- Task-dynamic modeling
- dynamical motor primitives
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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