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Sporns, Olaf
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Digital Document
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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.
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.
Member of
Model
Digital Document
Description
Sensory responses of the brain are known to be highly variable, but the origin and functional relevance of this variability
have long remained enigmatic. Using the variable foreperiod of a visual discrimination task to assess variability in the
primate cerebral cortex, we report that visual evoked response variability is not only tied to variability in ongoing cortical
activity, but also predicts mean response time. We used cortical local field potentials, simultaneously recorded from
widespread cortical areas, to gauge both ongoing and visually evoked activity. Trial-to-trial variability of sensory evoked
responses was strongly modulated by foreperiod duration and correlated both with the cortical variability before stimulus
onset as well as with response times. In a separate set of experiments we probed the relation between small saccadic eye
movements, foreperiod duration and manual response times. The rate of eye movements was modulated by foreperiod
duration and eye position variability was positively correlated with response times. Our results indicate that when the time
of a sensory stimulus is predictable, reduction in cortical variability before the stimulus can improve normal behavioral
function that depends on the stimulus.
have long remained enigmatic. Using the variable foreperiod of a visual discrimination task to assess variability in the
primate cerebral cortex, we report that visual evoked response variability is not only tied to variability in ongoing cortical
activity, but also predicts mean response time. We used cortical local field potentials, simultaneously recorded from
widespread cortical areas, to gauge both ongoing and visually evoked activity. Trial-to-trial variability of sensory evoked
responses was strongly modulated by foreperiod duration and correlated both with the cortical variability before stimulus
onset as well as with response times. In a separate set of experiments we probed the relation between small saccadic eye
movements, foreperiod duration and manual response times. The rate of eye movements was modulated by foreperiod
duration and eye position variability was positively correlated with response times. Our results indicate that when the time
of a sensory stimulus is predictable, reduction in cortical variability before the stimulus can improve normal behavioral
function that depends on the stimulus.
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