Research in the Action Lab is dedicated to the experimental and theoretical study of human motor control and learning. Our experiments collect kinematic and kinetic data, complemented by electromyographic and brain imaging data. Physical models of the task provide understanding of the task solutions as basis for comparison with human data.

Six major lines of research investigate the acquisition, control, and retention of motor skills.

  • Variability and Stability in Skill Acquisition: We developed a new approach that quantifies acquisition and performance in motor skills by decomposing performance variability into three components: Tolerance, Noise, Covariation. These concepts serve to design novel interventions to improve and accelerate skill acquisition.
  • From Action to Interaction: Complex Object Manipulation: This research examines human interaction with complex objects, in particular complex objects with internal dynamics. Using the exemplary task of carrying a cup of coffee we examine how interaction ensure predictability of the object dynamics.
  • Dynamic Primitives: We test the hypothesis that complex human actions are generated by employing dynamic primitives: submovements, oscillations and impedance. These basic units are combined to control complex interactive behavior.
  • Learning and Retention of Asymmetric Bimanual Actions: This line of work examines self-guided learning over extensive practice period until individualistic movement patterns are stabilized. We further use retention tests after a long interval to establish behavioral correlates of neuroplasticity.
  • Brain Measurements and Stimulation: We use Electroencephalography (EEG), Transcranial Magnetic Stimulation (TMS), and Transcranial Direct Current Stimulation (tDCS) to obtain data for the three research questions above.

Variability and Stability in Skill Acquisition

In the inquiry of acquisition and control of motor skills the concepts of stability and variability have played a central role, albeit with many different definitions. 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 exploration. 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, Noise, and Covariation (TNC-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. Experiments examine how acquisition of the skill is characterized by an increasing reliance on dynamical stability. In conjunction, performance variability is analyzed using the TNC-method to examine how different components contribute to this change in stability.


Selected Publications:

  1. Hasson, C.J., Zhang, Z., Abe, M.O., & Sternad, D. (2016). Neuromotor noise is malleable by amplification of perceived error. PLoS Computational Biology.
  2. Huber, M.E., Kuznetsov, N., Sternad, D. (revised). Reducing neuromotor noise in long-term motor skill learning. Journal of Neurophysiology.
  3. Huber, M.E. & Sternad, D. (2015). Implicit guidance to stable performance in a rhythmic perceptual-motor skill. Experimental Brain Research, 233, 6, 1783-99. DOI 10.1007/s00221-015-4251-7.
  4. Sternad, D., Huber, M.E., & Kuznetsov, N. (2014). Acquisition of novel and complex motor skills: Stable solutions where intrinsic noise matters less. Advances in Experimental Medicine and Biology, 826, 101-24. doi: 10.1007/978-1-4939-1338-1_8.
  5. Abe, M.O., & Sternad, D. (2013). Directionality in distribution and temporal structure of variability in skill acquisition. Frontiers in Human Neuroscience, 7:225.
  6. Sternad, D., Abe, M.O., Hu, X., & Muller, H. (2011). Neuromotor noise, sensitivity to error and signal-dependent noise in trial-to-trial learning. PLoS Computational Biology, 7, 9, e1002159.

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From Action to Interaction: Complex Object Manipulation

The manipulation of complex objects, or tool use, is ubiquitous in everyday life and has given humans their evolutionary advantage. Such object interactions become particularly intriguing when the objects themselves have internal degrees of freedom and add complex dynamics to the interaction. For example, when bringing a cup of coffee to one’s mouth, the coffee is only indirectly controlled via moving its container. The dynamics of the sloshing coffee creates complex interaction forces that the person has to take into account during control. To gain insight into the control mechanisms underlying this remarkable skill, this research examines the strategies that humans choose when manipulating an object with complex internal dynamics, mimicking a cup of coffee. Using the cart-and-pendulum model implemented in a virtual environment as a cup containing a rolling ball, our research studies the strategies that people choose when manipulating this object via a robotic manipulandum. One study examined point-to-point movements, quantifying how the safety margin changes with practice as a function of time constraints. The same task sensitively shows how older people have larger safety margins, i.e. a higher risk of spilling. When subjects move the “cup of coffee” in continuous rhythmic fashion, the complex nonlinear dynamics comes to the fore. Analysis of forces show that subjects seek strategies that make the interaction object more predictable, avoiding chaos. Another study examined how subjects maneuvered the ‘‘cup of coffee’’ in the face of perturbations. Experimental results showed that subjects exploited stability to overcome the perturbations: subjects moved through contraction regions to attenuate perturbations and make the dynamics more predictable.

Selected Publications:

  1. Bazzi, S., Ebert, J., Hogan, N., & Sternad, D. (2018). Stability and predictability in human control of complex objects. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(10), 103103.
  2. Nasseroleslami, B., Hasson, C.J., & Sternad, D. (2014). Rhythmic manipulation of objects with complex dynamics: Predictability over chaos. PLoS Computational Biology.
  3. Hasson, CJ & Sternad, D (2014). Safety margins in older adults increase with improved control of a dynamic object. Frontiers in Aging Neuroscience, 6: 158, doi: 10.3389/fnagi.2014.00158
  4. Hasson, C.J., Shen, T., & Sternad, D. (2012). Energy margins in dynamic object manipulation. Journal of Neurophysiology, 108, 5, 1349-65.

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Dynamic Primitives

Daily activities consist of a coordinated sequence or combination of rhythmic and discrete movements. 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 in motor control is whether such complex actions are controlled by simpler units that can be regarded as control primitives. Due to fundamental features of the neuromuscular system, most notably its slow response, we argue control may be in terms of parameterized primitives, which may simplify learning, performance, and retention of complex skills. Specifically, the hypothesis is that submovements, oscillations, and impedance primitives that are coupled to generate complex movements, the latter necessary for interaction with objects and the environment.

To test this hypothesis, we examine single-joint and multi-joint movements in unconstrained and interactive tasks. In the experiments participants perform rhythmic movements paced by a metronome interspersed with discrete changes in their trajectory, or in transition from slow to fast movements to identify constraints of these primitives and how they may be coupled.

Selected Publications:

  1. Sternad, D., Marino, H., Duarte, M., Dipietro, L., Charles, S., & Hogan, N. (2013). Transitions between discrete and rhythmic primitives in a unimanual task. 2. Frontiers in Computational Neuroscience, 7:90.
  2. Hogan, N. & Sternad, D. (2013). Dynamic primitives in the control of locomotion. Frontiers in Computational Neuroscience, 7:71.
  3. Hogan, N., & Sternad, D. (2012). Dynamic primitives of motor behavior. Biological Cybernetics, 106 (11-12), 727-739.
  4. Sternad, D. & Dean, W.J. (2003). Rhythmic and discrete elements in multi-joint coordination. Brain Research, 989, 152-171.

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Learning and Retention of Asymmetric Bimanual Skills

Despite anecdotal reports that humans retain acquired motor skills for many years, if not a lifetime, long-term memory of motor skills has received little attention. Several recent neuroimaging and electrophysiological studies on animals and humans revealed details of neuroplasticity underlying motor skill learning and motor memory. Advances notwithstanding, characterization of changes in observable behavior has been limited to short-term retention and to relatively gross measures of task achievement. To complement the understanding of practice-induced neural changes, our longitudinal studies present fine-grained kinematic characterization over extensive practice, followed by retention tests after 3-6 months and after 8 years! The experiments involve asymmetric bimanual tasks, performed either in continuous rhythmic fashion or as combination of rhythmic and discrete elements. Results suggest that motor memory may comprise not only higher-level task achievement, but also individual kinematic signatures.

Selected Publications:

  1. Park, S-W, Dijkstra, TMH, & Sternad, D (2013). Learning to never forget: Time scales and specificity of long-term memory of a motor skill. Frontiers in Computational Neuroscience, 7:111.
  2. Park, S-W. & Sternad, D. (2015). Robust retention of individual sensorimotor skill after self-guided practice. Journal of Neurophysiology, 113, 7, 2635-45.

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Brain Measurements and Stimulation

Complementing our behavioral measurements and modeling, we recently started to use Electroencephalography (EEG), Transcranial Magnetic Stimulation (TMS), and Transcranial Direct Current Stimulation (tDCS). With single-pulse TMS we examine the question how discrete and rhythmic movements have differential involvement of cortical regions. EEG and tDCS is used to obtain additional measurements to better characterize the learning process, both in bimanual skill acquisition and in the throwing task skittles.

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