Advances in Motor Learning & Motor Control

Welcome to Advances in Motor Learning & Motor Control 2020
(Formerly, Translational & Computational Motor Control - TCMC)
Wednesday November 11th, 1:00pm-4:00pm ET & Thursday November 12th, 11:00am-2:30pm ET, 2020.

This symposium provides an annual forum for presenting the best new work in motor control and motor learning, including studies of human motor behavior, imaging, motor neurophysiology, and computational modeling. Note the new stories page we added to help spur some discussion in our community about racial issues. The full meeting program with links to abstracts of the contributed talks is available below. We look forward to seeing you online at the meeting!

Maurice Smith, Adrian Haith, & Alaa Ahmed (co-chairs)


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MLMC 2020 Day 1


Youtube Links: Whole video | Ting | Ranjan | Tsay | Ostry | Heald | Avraham | van Mastrigt

MLMC 2020 Day 2


Youtube Links: Whole video | Finley | Sukumar | Du | Smoulder | Patil | Nettekoven | Sedaghat-Nejad

MLMC 2020 Program:

Day 1: 1pm–4pm Wednesday, November 11, 2020

1:05 PM ET Plenary Speaker: Lena Ting (Emory University and Georgia Tech)- What does a muscle sense? Multiscale interactions governing muscle spindle sensory signals (video)

1:35 PM ET Peer-reviewed talks

Implicit motor adaptation is driven by motor performance prediction error rather than sensory prediction error | (video)
Tanvi Ranjan and Maurice Smith

Distinct processing of sensory-prediction error and target error during implicit motor adaptation | (video)
Jonathan S. Tsay, Adrian M. Haith, Richard B. Ivry and Hyosub E. Kim

2:20 - 2:30 PM ET BREAK

Recognition Memory for Human Motor Learning | (video)
Neeraj Kumar, Floris T. van Vugt and David J. Ostry

Contextual inference underlies the learning of sensorimotor repertoires | (video)
James B. Heald, Máté Lengyel‡ and Daniel M. Wolpert‡
‡ - equal contribution

Explicit and Implicit Processes Exhibit Opposite Effects Upon Relearning a Sensorimotor Perturbation | (video)
Guy Avraham, J. Ryan Morehead, Maya Malaviya, Hyosub E. Kim and Richard B. Ivry

Pitfalls in quantifying exploration in reward-based motor learning | (video)
Nina M. van Mastrigt, Katinka van der Kooij and Jeroen B.J. Smeets

Day 2: 11am–2:30pm ET Thursday, November 12, 2020

11:05 AM ET Plenary Speaker: James Finley (USC) - A Well-balanced Effort: Managing Trade-offs between Effort and Stability during Locomotor Learning and Post-Stroke Gait (video)


11:35 AM ET Peer-reviewed talks

Effort expenditure links control of vigor with decision-making | (video)
Shruthi Sukumar, Reza Shadmehr and Alaa A. Ahmed

When going can be faster than stopping: Rethinking response inhibition as a flexible decision about whether or not to act | (video)
Yue Du, Alexander D. Forrence, Delaney M. Metcalf and Adrian M. Haith

Monkeys exhibit a paradoxical decrement in performance in high-stakes scenarios - they ‘choke under pressure’ | (video)
Adam L. Smoulder†, Nick P. Pavlovsky†, Patrick J. Marino†, Alan D. Degenhart, Nicole T. McClain, Aaron P. Batista* and Steven M. Chase*
†,* - equal contribution

12:40 - 12:50 PM ET BREAK

Human-inspired task-level regulation helps the simplest dynamic walker avoid falls | (video)
Navendu S. Patil, Jonathan B. Dingwell and Joseph P. Cusumano

M1 GABA relates to functional connectivity changes and retention in visuomotor adaptation | (video)
Caroline Nettekoven, Ned Jenkinson and Charlotte Stagg

Population coding of saccadic eye movements by Purkinje cells of the marmoset cerebellum | (video)
Ehsan Sedaghat-Nejad, Paul Hage, Jay Pi and Reza Shadmehr

2:00 PM ET Plenary Speaker: Mark Wagner (Stanford University) - Neocortex-cerebellum dynamics during skill acquisition (video not available)

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Plenary Abstracts

Lena Ting, Emory University and Georgia Tech
What does a muscle sense? Multiscale interactions governing muscle spindle sensory signals
Muscle spindles in vertebrate muscles provide rich sensory information about the body’s mechanical interactions with the environment necessary for neural control of movement. Muscle spindle afferent firing patterns have been well-characterized experimentally, but not fully explained mechanistically. I will present a biophysical model of a muscle spindle that demonstrates how well-known firing characteristics of muscle spindle Ia afferents – including a dependence on prior movement history, and nonlinear scaling with muscle stretch velocity – emerge from first principles of muscle contractile mechanics. The model provides a computational framework that address tension between the common understanding of muscle spindles as providing readouts of muscle kinematics, i.e. length and velocity (primarily obtained in passive muscle stretch conditions) with a variety of evidence from more naturalistic and behavioral conditions that defy this classic description of muscle spindle function. In particular, the role of efferent drive to muscles within the mechanosensory region of the muscle spindle cannot be ignored. Simulations of the mechanical interactions of the muscle spindle with muscle-tendon dynamics reveal the differential and interacting effects of motor commands to the muscle (alpha drive) and muscle spindle (gamma/fusimotor drive) on Ia firing, explaining highly variable and seemingly paradoxical muscle spindle sensory signals during human voluntary force production and active muscle stretch. While in certain conditions, muscle spindle sensory signals may provide a good proxy for muscle length, velocity, force, and/or yank, the common denominator is that muscle spindles reflect the interactions between internally and externally-generated forces on the body and the resulting movement. As such, we propose that muscle spindles are situated to perform physical computations that enable the effects of external forces (ex-afference) to be dissociated from internal forces (re-afference), providing a signal perhaps best described as sensory prediction error. Our multiscale muscle spindle model provides an extendable, multiscale, biophysical framework for understanding and predicting movement-related sensory signals in health and disease.

James Finley (USC)
A Well-balanced Effort: Managing Trade-offs between Effort and Stability during Locomotor Learning and Post-Stroke Gait

Walking is one of the many skills that we learn during development through trial-and-error practice. We eventually gain the ability to not only walk with little effort over flat, unobstructed terrain, but we also learn to adapt our walking pattern to changes in the environment or changes in the body that result from aging or disease. What factors govern the strategies that we choose during these forms of adaptive learning? Likely candidates include a combination of features related to effort, instability, aesthetics, and fear of falling. The relative weighting of these objectives impacts not only how we adapt our walking pattern when features of our environment change, but it also dictates how our preferred movement strategies change when there is damage to the nervous system, as is the case following stroke. Here, I will summarize our recent work to understand the trade-offs between two primary objectives in human walking: effort minimization and minimizing fall risk. Through a combination of empirical studies and biomechanical simulations, I will show that asymmetric walking patterns can, in certain contexts, be considered optimal with respect to effort and balance-related costs for both healthy individuals and people post-stroke. I will conclude by making a case for a more personalized approach to identifying targets for locomotor rehabilitation, one that relies on predictions of optimal movement patterns given the constraints imposed by the neuromuscular system.

Mark Wagner (Stanford University)
Neocortex-cerebellum dynamics during skill acquisition

In striking contrast to the rest of the brain, evolutionary expansion of the cerebellum has kept pace with that of neocortex, and these two structures contain ~99% of all neurons in humans. The entire neocortex and the cerebellum are also reciprocally connected by some of the densest long-range projections in the brain that are universal across mammals. Recently, using simultaneous two photon calcium imaging of neocortical output neurons and downstream cerebellar granule cells, we found that over weeks of learning these structures converge onto common dynamics, with cerebellum integrally recruited into all aspects of premotor neocortical encoding. Our related studies of the climbing fiber system show that learning drives the emergence of sharp olivary synchronization transitions during task execution. In ongoing work, we are integrating simultaneous recordings of these components of the cortico-cerebellar system--cortical Layer 5 and both downstream cerebellar input pathways, granule cells and climbing fibers--to more completely understand the evolution of cortico-cerebellar transmission during novel skill acquisition.

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Abstracts from all previous meetings are available here.

Submissions for future meetings:

There are no submission fees. The acceptance rate for talks has historically been around 30%. The abstract submission deadline for MLMC 2020 was be October 1st. The deadline will change from year to year, but will generally be about 6 weeks before the meeting.

Abstract submissions consist of a 2-page PDF (1 page of text & a 2nd page primarily of figures and their captions). The main text should be ≥ 11pt with a line spacing of ≥ 1, and figure captions should be ≥ 9pt. Successful abstracts from last year are linked at the bottom of this page, and for prior years, you can find them here. See the bottom of this page for the link to submit an abstract. 

Submissions will be competitively peer reviewed by our program committee of over 40 leading experts in motor control and motor learning, and reviewer comments will be provided. The top submissions will be accepted for 22-minute oral presentations (6 minutes of which is reserved for questions). If you are a faculty member willing to review about 5 abstracts and would like to join the program committee please send a message to tcmc.conference@gmail.com.




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