Kelso, J. A. Scott

Earlier experimental studies by one of us (Kelso, 1981a, 1984) have shown that abrupt phase transitions occur in human hand movements under the influence of scalar changes in cycling frequency. Beyond a critical frequency the originally prepared out-of-phase, antisymmetric mode is replaced by a symmetrical, in-phase mode involving simultaneous activation of homologous muscle groups. Qualitatively, these phase transitions are analogous to gait shifts in animal locomotion as well as phenomena common to other physical and biological systems in which new "modes" or spatiotemporal patterns arise when the system is parametrically scaled beyond its equilibrium state (Haken, 1983). In this paper a theoretical model, using concepts centPal to the interdisciplinary field of synergetics and nonlinear oscillator theory, is developed, which reproduces (among other features) the dramatic change in coordinative pattern observed between the hands.

Recently some methods have been presented to extract ordinary differential equations (ODE) directly from an experimental time series. Here, we introduce a new method to find an ODE which models both the short time and the long time dynamics. The experimental data are represented in a state space and the corresponding flow vectors are approximated by polynomials of the state vector components. We apply these methods both to simulated data and experimental data from human limb movements, which like many other biological systems can exhibit limit cycle dynamics. In systems with only one oscillator there is excellent agreement between the limit cycling displayed by the experimental system and the reconstructed model, even if the data are very noisy. Furthermore we study systems of
two coupled limit cycle oscillators. There, a reconstruction was only successful for data with a sufficiently long transient trajectory and relatively low noise level.

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.

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced
into studies of learning patterns of inter-personal coordination. Combining mathematical
modeling and experimentation, we investigate how the HDC may be used as a Virtual
Teacher (VT) to help humans co-produce and internalize new inter-personal coordination
pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-
driven avatar, animated by dynamic equations stemming from the well-established
Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate
that the VT is successful in shifting the pattern co-produced by the VT-human system
toward any value (Experiment 1) and that the VT can help humans learn unstable relative
phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from
one partner to the other increases when VT-human coordination loses stability. This suggests
that variable joint performance may actually facilitate interaction, and in the long run
learning. VT appears to be a promising tool for exploring basic learning processes involved
in social interaction, unraveling the dynamics of information flow between interacting partners,
and providing possible rehabilitation opportunities.

Apart from its natural relevance to cognition, music provides a window into the intimate relationships between production,
perception, experience, and emotion. Here, emotional responses and neural activity were observed as they evolved
together with stimulus parameters over several minutes. Participants listened to a skilled music performance that included
the natural fluctuations in timing and sound intensity that musicians use to evoke emotional responses. A mechanical
performance of the same piece served as a control. Before and after fMRI scanning, participants reported real-time
emotional responses on a 2-dimensional rating scale (arousal and valence) as they listened to each performance. During
fMRI scanning, participants listened without reporting emotional responses. Limbic and paralimbic brain areas responded to
the expressive dynamics of human music performance, and both emotion and reward related activations during music
listening were dependent upon musical training. Moreover, dynamic changes in timing predicted ratings of emotional
arousal, as well as real-time changes in neural activity. BOLD signal changes correlated with expressive timing fluctuations in
cortical and subcortical motor areas consistent with pulse perception, and in a network consistent with the human mirror
neuron system. These findings show that expressive music performance evokes emotion and reward related neural
activations, and that music’s affective impact on the brains of listeners is altered by musical training. Our observations are
consistent with the idea that music performance evokes an emotional response through a form of empathy that is based, at
least in part, on the perception of movement and on violations of pulse-based temporal expectancies.