Virtual Partner Interaction (VPI): exploring novel behaviors via coordination dynamics

Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI -- Virtual Partner Interaction -- a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual par...

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Bibliographic Details
Published in:PloS one 2009-06, Vol.4 (6), p.e5749-e5749
Main Authors: Kelso, J A Scott, de Guzman, Gonzalo C, Reveley, Colin, Tognoli, Emmanuelle
Format: Article
Language:English
Subjects:
Adolescent
Adult
Algorithms
Behavior
Brain
Computational Biology/Computational Neuroscience
Computer Science/Systems and Control Theory
Computer Simulation
Digitization
Female
Humans
Interpersonal Relations
Laboratories
Male
Mathematical models
Models, Neurological
Movement
Nervous system
Neural networks
Neural Networks (Computer)
Neurons - metabolism
Neuroscience/Cognitive Neuroscience
Neuroscience/Psychology
Neuroscience/Theoretical Neuroscience
Neurosciences
Software
User-Computer Interface
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Summary:Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI -- Virtual Partner Interaction -- a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0005749