in the Action Lab
The Action Lab is a research facility
dedicated to the experimental study of human motor control. More
specifically, we study human action and perception focusing on physical and mechanical aspects of both
the performer and the task.
Five major lines of our research
investigate the generation of perceptually controlled behavior in
and rhythmic elements in single- and multi-joint movements. The
hypothesis is that unconstrained multi-joint movements can be
understood in terms of two fundamental units of action: discrete
movements and rhythmic movements.
||Dual-task locomotor taining in older individuals and stroke survivors
into dynamic stability. The complex task of bouncing a ball is studied
in a task-based approach, where dynamical stability provides the
framework for defining successful performance.
role of resonance properties of the limb in rhythmic movements. We
show that resonance properties are essential in understanding rhythmic
variability and tracking performance.
and stability in skill acquisition. We developed a new approach to
quantify performance and change in redundant tasks. The method
decomposes variability into three components that quantify different
aspects and stages of skill improvement.
This research is supported by supported by the National Science Foundation grant
BCS-0904464, DMS-0928587, Deutsche Forschungsgesellschaft DFG-MU 1374/3-1, National Institutes of Health R01HD045639.
and Rhythmic Elements in Single and Multi-Joint Movements
Daily activities consist of a coordinated sequence or combination of cyclic and translatory
elements. Examples range from rhythmic locomotion when it is combined with stepping over obstacles, to rhythmic
finger actions in piano playing while translating the hand over the keyboard. A longstanding question is
motor control is whether such complex actions can be decomposed into simpler units that can be regarded
as primitives. The hypothesis of this line of research is that multijoint coordination can be understood
as consisting of discrete and rhythmic primitives that are coupled to produce complex movements. To test
this hypothesis, we examine single-joint and multi-joint tasks consisting of rhythmic and discrete task
elements. We measure kinematic trajectories and muscular activity measured by EMG. In one
experiment participants perform oscillatory movements in the horizontal plane paced by a metronome interspersed
with discrete changes in their trajectory. We identified constraints in how these two units can be
coupled together. Analyses showed that these constraints arise at the neuro-muscular level, such that
EMG bursts of the discrete and rhythmic movement have a tendency to synchronize.
Sternad, D., de Rugy, A., Pataky, T.,
& Dean, W.J. (2002). Interactions between rhythmic and discrete elements
over a wide range of movement periods. Experimental Brain Research, 147,
De Rugy, A., & Sternad, D. (2003).
Interaction between discrete and rhythmic movements: reaction time and phase
of discrete movement initiation against oscillatory movements. Brain
Research, 994, 160-174.
Sternad, D. & Dean, W.J. (2003).
Rhythmic and discrete elements in multi-joint coordination. Brain Research,
Wei, K., Wertman, G., & Sternad, D. (2003). Interactions
between rhythmic and discrete components in a bimanual task. Motor Control,
7, 2, 134-155.
of Resonance Properties of the Limb in Rhythmic Movements
is an essential component in the control of all human movements.
Behavioral studies of rhythmic timing have a long tradition where
rhythmic finger tapping has been the most common paradigm. In order to
investigate the influence of inertial properties of the moving limb,
particularly its resonance frequency, we have used wrist-pendular
movements where the pendular manipulanda easily allow modification of
the resonance frequency. In a variety of experimental manipulations we
showed that the resonance frequency systematically determines the
subjectively preferred frequency. In movements paced at different
frequencies we observed an increase in the variability of timing
proportional to the discrepancy between the pacing and the preferred
frequency. Additionally, in longer trials we observed a systematic
drift towards the resonance frequency. These results were captured in a
coupled oscillator model consisting of internal and mechanical
oscillators. In a recent experiment, we investigated the robustness of
the preferred frequency by submitting rhythmic movements to external
force fields. The presence of external torques did not affect the
preferred frequency following the removal of the force field. These
results give evidence that the neuromuscular frequency is a relatively
H., Russell, D.M., & Sternad, D. (2003). Task-effector asymmetries in a
rhythmic continuation task. Journal of Experimental Psychology: Human
Perception and Performance, 29, 3, 616-630.
Russell, D.M., &
Sternad, D. (2001). Sinusoidal visuomotor tracking: Intermittent
servo-control or coupled oscillations? Journal of Motor Behavior, 33, 4,
Russell, D., de Rugy, A., &
Sternad (submitted). The role of resonance frequency in rhythmic visuo-motor control.
Experimental Brain Research.
Yu, H., Kalveram, K-T., &
Sternad, D. (in preparation). Robustness of neuromechanical resonance
frequency under force field perturbations.
into Dynamical Stability - Bouncing a Ball
bouncing a ball is a perceptual-motor task that poses all the
challenges present in the control of movements. How do we control arm
and racket movements to hit a ball at the appropriate position to
achieve a given target height? This study presents a task-based
approach in understanding the principles in movement control. Starting
with a kinematic model of the ball-racket system, we derived
predictions for a dynamically stable control of the ball. This strategy
is computationally efficient as small errors do not require explicit
corrections. A series of experiments evaluated whether human actors
exploit and optimize dynamical stability and what perceptual support is
necessary for stable behavior. In a series of experiments we showed
that participants performed the task with ball-racket contacts that
were consistent with model predictions: Humans indeed tuned their
performance to exploit dynamical stability. This line of research was
extended by developing a virtual set-up in which subjects manipulate a
racket, but the ball only exists in the virtual environment. In one
experiment we applied large perturbations to study how actors regain
stability. Results revealed that an adjustment of the racket period
ensured that the impacts occurred at a phase associated with dynamical
stability. These findings were simulated in a model consisting of a
neural oscillator that drives a mechanical actuator (forearm holding
the racket) to bounce the ball
Dijkstra, T.M.H., Katsumata,
H., de Rugy, A., & Sternad, D. (2004). The dialogue between data and
model: Passive stability and relaxation behavior in a ball bouncing task.
Journal of Nonlinear Science.11, 3, 319- 345.
Rugy, A., Wei, K., Muller, H., & Sternad, D. (2003). Actively
tracking 'passive' stability. Brain Research, 982, 1, 64-78.
Sternad, D., Duarte, M.,
Katsumata, H., & Schaal, S. (2001). Bouncing a ball: Tuning into dynamic
stability. Journal of Experimental Psychology: Human Perception and
Performance, 27, 5, 1163-1184.
Sternad, D., Duarte, M., Katsumata, H., & Schaal, S. (2000). Dynamics of a
bouncing ball in human performance. Physical Review E, 63, 011902-1 -011902-8.
and Stability in Skill Acquisition
In the inquiry of acquisition and control of skills the concepts of stability and variability have
played a central role, albeit with many different definitions and levels of rigor. Most commonly,
improvement of performance is associated with a decrease in variability of some task parameters. This
reduced variability, in turn, has been interpreted as an increase in stability. This simple inverse
relationship obscures that empirical variability can be indicative of many different facets, ranging
from the obvious "lack of control", seen as errors in target-oriented tasks, to more beneficial aspects,
such as compensatory variation between parameters, and adaptation to new tasks. Dynamical stability is
also a formally rigorous concept that can be quantified independently from measured variability. This
research examines skill acquisition in two selected tasks to differentiate our understanding of
variability and stability in human performance. In skittles, a target-oriented throwing action
predominantly under feedforward control, we develop a method to decompose variability into three
independent components: tolerance, covariation, and noise (TCN-decomposition), each capturing a
different contribution to successful performance. Experiments test how different components of
variability contribute in different stages of learning, and how stochastic noise can be a means to find
successful solutions. The second task is the continuous perceptually-guided skill of rhythmically
bouncing a ball as described above. Experiments examine how acquisition of the skill is characterized by
an increasing reliance on dynamical stability. In conjunction, performance variability is analyzed using
the TCN-method to examine how different components contribute to this change in stability.
& Sternad, D. (2004). Decomposition of variability in the execution of
goal-oriented tasks - Three components of skill improvement. Journal
of Experimental Psychology: Human Perception and Performance,
Muller, H. &
Sternad, D. (2003). A randomization method for the calculation of
covariation in multiple nonlinear relations: Illustrated at the example of
goal-directed movements. Biological Cybernetics, 89, 22-33.