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Human social motor solutions for human-machine interaction in dynamical task contexts
Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia.
Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA.ORCID iD: 0000-0003-2254-1396
Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia.
Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA.
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2019 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 116, no 4, p. 1437-1446Article in journal (Refereed) Published
Abstract [en]

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 human–machine 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. Human–human and human–artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human–human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a “Turing-like” 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.

Place, publisher, year, edition, pages
2019. Vol. 116, no 4, p. 1437-1446
Keywords [en]
Human-machine interaction, Multiagent coordination, Shepherding, Task-dynamic modeling, dynamical motor primitives
National Category
Human Computer Interaction Robotics
Identifiers
URN: urn:nbn:se:his:diva-17625DOI: 10.1073/pnas.1813164116ISI: 000456336100052Scopus ID: 2-s2.0-85060308746OAI: oai:DiVA.org:his-17625DiVA, id: diva2:1348003
Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-09-09Bibliographically approved

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Lamb, Maurice

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