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  • 1.
    Billing, Erik
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Lamb, Maurice
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Högberg, Dan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Digital Human Modelling in Action2019In: Proceedings of the 15th SweCog Conference / [ed] Linus Holm, Erik Billing, Skövde: University of Skövde , 2019, p. 25-28Conference paper (Refereed)
  • 2.
    Grover, Francis
    et al.
    Center for Cognition, Action, and Perception, Department of Psychology, University of Cincinnati, USA.
    Lamb, Maurice
    Center for Cognition, Action, and Perception, Department of Psychology, University of Cincinnati, USA.
    Bonnette, Scott
    Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
    Silva, Paula L.
    Center for Cognition, Action, and Perception, Department of Psychology, University of Cincinnati, USA.
    Lorenz, Tamara
    Center for Cognition, Action, and Perception, Department of Psychology, University of Cincinnati, USA / Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH, USA / Department of Electrical Engineering and Computer Science, University of Cincinnati, OH, USA.
    Riley, Michael A.
    Center for Cognition, Action, and Perception, Department of Psychology, University of Cincinnati, USA.
    Intermittent coupling between grip force and load force during oscillations of a hand-held object2018In: Experimental Brain Research, ISSN 0014-4819, E-ISSN 1432-1106, Vol. 236, no 10, p. 2531-2544Article in journal (Refereed)
    Abstract [en]

    Tightly coordinated grip force adaptations in response to changing load forces have been reported as continuous, stable, and proportional to the load force changes. Considering the existence of inherent sensorimotor feedback delays, current accounts of grip force–load force coupling invoke explicit predictive mechanisms in the form of internal models for feedforward control to account for anticipatory grip force modulations. However, recent findings suggest that the stability and regularity of grip force–load force coupling is less persistent than previously thought. Thus, the objective of the current study was to comprehensively quantify the time-varying characteristics of grip force–load force coupling. Investigations into the coupling’s dynamics during continuous 30 s bouts of load force oscillation revealed intermittent phases of coordination, as well as phases that varied in stability, rather than a persistent and continuously stable pattern of coordination. These findings have important implications for accounts of grip force–load force coupling and of anticipation in motor control, more broadly.

  • 3.
    Lamb, Maurice J.
    et al.
    Department of Philosophy, University of Cincinnati, United States.
    Chemero, Anthony P.
    Department of Philosophy and Psychology, University of Cincinnati, United States.
    Interaction-dominant dynamics and extended embodiment2013In: Constructivist Foundations, ISSN 1782-348X, E-ISSN 1782-348X, Vol. 9, no 1, p. 88-89Article in journal (Refereed)
  • 4.
    Lamb, Maurice
    et al.
    Center for Cognition, Action and Perception, University of Cincinnati, Cincinnati, OH, United States.
    Kallen, Rachel W.
    Center for Cognition, Action and Perception, University of Cincinnati, Cincinnati, OH, United States.
    Harrison, Steven J.
    Department of Kinesiology, University of Connecticut, Connecticut, CT, United States.
    Di Bernardo, Mario
    Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy / Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.
    Minai, Ali A.
    Department of Electrical Engineering and Computing Science, University of Cincinnati, Cincinnati, OH, United States.
    Richardson, Michael J.
    Center for Cognition, Action and Perception, University of Cincinnati, Cincinnati, OH, United States.
    To Pass or Not to Pass: Modeling the Movement and Affordance Dynamics of a Pick and Place Task2017In: Frontiers in Psychology, ISSN 1664-1078, E-ISSN 1664-1078, Vol. 8, article id 1061Article in journal (Refereed)
    Abstract [en]

    Humans commonly engage in tasks that require or are made more efficient by coordinating with other humans. In this paper we introduce a task dynamics approach for modeling multi-agent interaction and decision making in a pick and place task where an agent must move an object from one location to another and decide whether to act alone or with a partner. Our aims were to identify and model (1) the affordance related dynamics that define an actor’s choice to move an object alone or to pass it to their co-actor and (2) the trajectory dynamics of an actor’s hand movements when moving to grasp, relocate, or pass the object. Using a virtual reality pick and place task, we demonstrate that both the decision to pass or not pass an object and the movement trajectories of the participants can be characterized in terms of behavioral dynamics model. Simulations suggest that the proposed behavioral dynamics model exhibits features observed in human participants including hysteresis in decision making, non-straight trajectories, and non-constant velocity profiles. The proposed model highlights how the same low-dimensional behavioral dynamics can operate to constrain multiple (and often nested) levels of human activity and suggests that knowledge of what, when, where and how to move or act during pick and place behavior may be defined by these low dimensional task dynamics and, thus, can emerge spontaneously and in real-time with little a priori planning.

  • 5.
    Lamb, Maurice
    et al.
    Department of Psychology, Cincinnati, OH, USA.
    Lorenz, Tamara
    Department of Psychology, Cincinnati, OH, USA / Department of Electrical Engineering and Computer Science, Rhodes Hall Cincinnati, OH, USA / Department of Materials and Mechanical Engineering, Cincinnati, Ohio, USA.
    Harrison, Stephen
    Department of Kinesiology, University of Connecticut, CT, USA.
    Kallen, Rachel
    Department of Psychology, Cincinnati, OH, USA.
    Minai, Ali
    Department of Electrical Engineering and Computer Science, Rhodes Hall Cincinnati, OH, USA.
    Richardson, Michael
    Department of Psychology, Cincinnati, OH, USA.
    Behavioral Dynamics and Action Selection in a Joint Action Pick-and-Place Task2017In: Proceedings of the 39th Annual Meeting of the Cognitive Science Society (CogSci), Cognitive Science Society , 2017, p. 2506-2511Conference paper (Refereed)
    Abstract [en]

    Many common tasks require or are made more efficient by coordinating with others. In this paper we investigate the coordination dynamics of a joint action pick-and-place task in order to identify the behavioral dynamics that underlie the emergence of human coordination. More precisely, we introduce a task dynamics approach for modeling multi-agent interaction in a continuous pick-and-place task where two agents must decide to work together or alone to move an object from one location to another. Our aims in the current paper are to identify and model (1) the relevant affordance dynamics that underlie the selection of the different action modes required by the task and (2) the trajectory dynamics of each actor’s hand movements when moving to grasp, relocate, or pass the object. We demonstrate that the emergence of successful coordination can be characterized in terms of behavioral dynamics models which may have applications for artificial agent design.

  • 6.
    Lamb, Maurice
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Center for Cognition, Action and Perception, Department of Psychology, University of Cincinnati, USA.
    Nalepka, Patrick
    Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
    Kallen, Rachel W.
    Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
    Lorenz, Tamara
    Center for Cognition, Action and Perception, Department of Psychology, University of Cincinnati, USA / Department of Electrical Engineering and Computer Science, University of Cincinnati, USA / Department of Mechanical and Materials Engineering, University of Cincinnati, USA.
    Harrison, Steven J.
    Department of Kinesiology, University of Connecticut, USA.
    Minai, Ali A.
    Department of Electrical Engineering and Computer Science, University of Cincinnati, USA.
    Richardson, Michael J.
    Department of Psychology, Center for Elite Performance, Expertise and Training, Macquarie University, Australia.
    A Hierarchical Behavioral Dynamic Approach for Naturally Adaptive Human-Agent Pick-and-Place Interactions2019In: Complexity, ISSN 1076-2787, E-ISSN 1099-0526, article id 5964632Article in journal (Refereed)
    Abstract [en]

    Interactive or collaborative pick-and-place tasks occur during all kinds of daily activities, for example, when two or more individuals pass plates, glasses, and utensils back and forth between each other when setting a dinner table or loading a dishwasher together. In the near future, participation in these collaborative pick-and-place tasks could also include robotic assistants. However, for human-machine and human-robot interactions, interactive pick-and-place tasks present a unique set of challenges. A key challenge is that high-level task-representational algorithms and preplanned action or motor programs quickly become intractable, even for simple interaction scenarios. Here we address this challenge by introducing a bioinspired behavioral dynamic model of free-flowing cooperative pick-and-place behaviors based on low-dimensional dynamical movement primitives and nonlinear action selection functions. Further, we demonstrate that this model can be successfully implemented as an artificial agent control architecture to produce effective and robust human-like behavior during human-agent interactions. Participants were unable to explicitly detect whether they were working with an artificial (model controlled) agent or another human-coactor, further illustrating the potential effectiveness of the proposed modeling approach for developing systems of robust real/embodied human-robot interaction more generally.

  • 7.
    Nalepka, Patrick
    et al.
    Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia.
    Lamb, Maurice
    Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA.
    Kallen, Rachel W.
    Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia.
    Shockley, Kevin
    Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA.
    Chemero, Anthony
    Center for Cognition, Action & Perception, Department of Psychology, University of Cincinnati, OH 45220, USA.
    Saltzman, Elliot
    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.
    Richardson, Michael J.
    Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, NSW 2109, Australia / Department of Psychology, Macquarie University, Sydney, NSW 2109, Australia.
    Human social motor solutions for human-machine interaction in dynamical task contexts2019In: 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)
    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.

  • 8.
    Richardson, Michael J.
    et al.
    Center for Cognition, Action and Perception, University of Cincinnati, OH, USA.
    Kallen, Rachel W.
    Center for Cognition, Action and Perception, University of Cincinnati, OH, USA.
    Nalepka, Patrick
    Center for Cognition, Action and Perception, University of Cincinnati, OH, USA.
    Harrison, Steven J.
    School of Health, Physical Education and Recreation, University of Nebraska Omaha, NE, USA.
    Lamb, Maurice
    Center for Cognition, Action and Perception, University of Cincinnati, OH, USA.
    Chemero, Anthony
    Center for Cognition, Action and Perception, University of Cincinnati, OH, USA.
    Saltzman, Elliot
    Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, MA, USA.
    Schmidt, Richard C.
    Department of Psychology, College of the Holy Cross, MA, USA.
    Modeling embedded interpersonal and multiagent coordination2016In: COMPLEXIS 2016 - Proceedings of the 1st International Conference on Complex Information Systems / [ed] Víctor Méndez Muñoz, Oleg Gusikhin, Victor Chang, Setubal: SciTePress, 2016, p. 155-164Conference paper (Refereed)
    Abstract [en]

    Interpersonal or multiagent coordination is a common part of everyday human activity. Identifying the dynamic processes that shape and constrain the complex, time-evolving patterns of multiagent behavioral coordination often requires the development of dynamical models to test hypotheses and motivate future research questions. Here we review a task dynamic framework for modeling multiagent behavior and illustrate the application of this framework using two examples. With an emphasis on synergistic self-organization, we demonstrate how the behavioral coordination that characterizes many social activities emerges naturally from the physical, informational, and biomechanical constraints and couplings that exist between two or more environmentally embedded and mutually responsive individuals.

1 - 8 of 8
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