Journal Club Meetings
Alon Fishbach, Stephane A. Roy Christina Bastianen, Lee E. Miller James C. Houk “Deciding when and how to correct a movement: discrete sub movements as a decision making process” Exp Brain Res (2007)
They studied the asymmetric multi-peaked velocity profiles observed from the one-dimensional step-tracking task on a screen by supination/pronation of the forearm made by trained monkeys. The profile was decomposed into discrete sub-movements by “soft symmetry method” namely the Primitive movements, Overlapping sub movements and Delayed sub movements. From different analyses of the velocity profiles it is evident that the corrective sub movements were initiated when the probability distribution of the predicted end-point from the continuously accumulated information is statistically different from the target’s location. They also showed that the latency in onset of the following sub movements are proportional to the amplitude of the previous primitive and the ‘extent to go’ of the reach. Thus, these evidences claim that there is not only a continuous feedforward controller but it triggers a discrete execution when an error threshold is met. (N.V) #velocityprofile #discretecontroller
I. S. Howard, D. W. Franklin and D. M. Wolpert "Gone in 0.6 Seconds: The Encoding of Motor Memories Depends on Recent Sensorimotor States"
Subjects can learn interfering force fields only if dynamic (active or passive) cues are available prior to the movement. The cue should be present in a time window of 600ms prior to the movement. This means that subjects are able to learn a force field if there is a special movement associated with that force field. This paper is evidence for the fact that dynamic motor learning is not a map through current states to motor output but recent history of the previous states have fundamental role in learning. (by Y.Z.) #interference #forcefieldadaptation #motormemory
Mattar, Andrew AG, and David J. Ostry. "Generalization of dynamics learning across changes in movement amplitude."
The authors showed that in dynamics learning, decreasing the amplitude of a test reach relative to training allows generalization, while increasing the amplitude does not. The maximum threshold at which generalization is possible corresponds to the moment at which velocity increases beyond that experienced in test trials. Accordingly, generalization is based on experienced dynamics, rather than the brain learning exactly how a force field works and building a model. This corroborates the findings of the Mattar & Ostry 2007 paper. Since the experience gained by reaching for one target applies minimally to reaching for another target 90° away, it makes sense that there is little to no generalization. Thus, this paper builds the theory that generalization is based on past experience (like a lookup table), rather than constructing a model. Additionally, interleaving test targets with training targets artificially impacts generalization by making subjects no longer naïve, and should be avoided. (by T.A.)
Mattar, Andrew AG, and David J. Ostry. "Modifiability of generalization in dynamics learning."
Generalization of dynamics learning in hand movement is highly localized to the very nearest movements and does not benefit from an extensive distribution of movement experience throughout the working space, however, it depends on interpolating to the local instances. Therefore, there is just a narrow dynamics learning generalization.The results of this study suggest that the process behind dynamics learning relies on performing an interpolation rather than building a model. (by A.R)
Todorov, Emanuel. "Direct cortical control of muscle activation in voluntary arm movements: a model"
The author displayed that a model of motor cortex control can qualitatively represent many recent neural data findings and reaching movement behaviors. If this model is true, the cortex could be a low-level controller directly determining the muscle activations needed to make a movement. This could be a novel representation of the general interaction between motor cortex activity and the kinematics of the hand, which coexists with the findings of Georgopoulos and other studies. (by S.P.)
Georgopoulos et al. "Neuronal population coding of movement direction"
The experiment studied the motor cortex activity of Rhesus monkeys, using microelectrode penetrations, when they performed reaching movements in 3D space. The movement direction of the monkey’s arm was correlated with the rate of firing of the neurons and their vectorial sum known as the population vector. As this population vector predicted accurately the movement vector, it can be understood that the motor cortex activity represents the kinematics of the arm’s reaching movement. Thus, the research gave more insights on how the motor cortex represents a step, of the entire control process, before the brain commands the muscles to perform the movement. (by N.V)
Max Berniker and Konrad Kording "Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects "
This study presents a new model that demonstrates properties of motor adaptation such as short-term/long-term savings, inference and spontaneous rebound. They suggest that the CNS both estimates the sources and the relevance of disturbances. This model could be an answer to the deficiencies of the linear model. (by Y.Z.)
Max Berniker and Konrad Kording "Estimating the sources of motor errors for adaptation and generalization"
The authors showed that a model that estimates the source of motor errors (either world or body) can explain adaptaion and generalization in reaching movement experiments. Instead of explaining adapataion and generalization in terms of intrinsic or extrinsic coordinate frames, they provide evidence for an internal probabilistic model that updates limb and world paramaters. The CNS may be constatnly updating these parameters to understand adapt to disturbances and generalize to other workspaces or limbs. (by S.P.)
Wang, Jinsung and Robert L. Sainburg "Interlimb Transfer of Novel Inertial Dynamics Is Asymmetrical "
The experiment studies two groups of dominantly right handed people, making reaching movements to intrinsic targets with an external mass attached to their forearm. They show that the adaptation with the dominant arm allowed for more accurate movement in their non-dominant arm than the group which had their adaptations in the non-dominant arm and tried moving with their dominant hand. This bolsters the case for the non-dominant arm controller, having better access to the memory resources where the movement information obtained during dominant arm adaptation is stored. Adaptations to visuomotor and dynamic transformations having different generalizations also point out that these adaptations may involve distinct neural processes. (by N.V.)
Emilio Bizzi et al. "Posture Control and Trajectory Formation During Arm Movement"
During single joint, long reaching movements of the forearm for both intact and deafferented monkeys, the acceleration of the limb does not rapidly approach a constant value, but gradually approaches a constant value well after (~400 msec) the beginning of EMG activity. In addition, intact and deafferented monkeys both returned to an intermediate position between the start and end points of the reach, after being pushed towards or moved to the final position. This suggests that the CNS has programmed a slow shift in equillibrium point along the trajectory. This is evidence against the "final position control" hypothesis and in favor of a "series of equillibrium points" throughout a movement.
Paul Dizio and James R. Lackner "Motor adaptation to Coriolis Force Perturbations of Reaching Movements: Endpoint but not Trajectory Transfers to the Nonexposed Arm"
Making reaches in a null field, after adapting to a coriolis force field with the right hand, subjects make straight but deviated reaches with their left hand. Afterwards, when they make a reach with their right hand, they make curved reaches but with large endpoint errors. This means that endpoint control and path control are independent from each other. These results are in contrast with the equilibrium theory (by Y.Z.)
James R. Lackner and Paul Dizio "Rapid Adaptation to Coriolis Force Perturbations of Arm Trajectory"
The author demonstrated that people can learn to adapt to relatively new types of forces, without any aid from visual or haptic feedback. They observed the reaching movements of subjects in a rotating room, under the influence of Coriolis force. Experiments were devised to have both touch and no touch movements showing different end point and trajectory errors, suggesting that the brain has more than one controller for reaching movements (by N.V.)
Ali Farshchian, et al. "Sensory Agreement Guides Kinetic Energy Optimimzation of Arm Movements during Object Manipulation"
People who saw and felt a double pendulum made curved reaches, while those who saw a cursor or felt a point mass made straight reaches. This means that subjects choose the efficient path when the sensing modalities are in agreement but, go straight otherwise. This shows that visual feedback is a critical piece of information for the motor system and the standard visual feedback provides strong bias towards straight movements (by Y.Z.). #forcefieldadaptation #visualfeedback #efficient
Franklin, David W., et al. "Visual feedback is not necessary for the learning of novel dynamics."
The authors showed that subjects can learn to make accurate and straight reaching movements in alternative fields with and without vision. For point to point reaching movements, subjects will learn to make straight reaches; however, this may be conditioned upon to fact that a subject can use visual feedback to return the hand to the starting position. If true, then proprioception, not vision, is the primary feedback used for learning.
Max Berniker, et al. "Simplified and Effective Motor Control Based on Muscle Synergies to Exploit Musculoskeletal Dynamics"
The authors developed a low dimensional control model, based on muscle synergies and compared its performance to other conventional control models. A low dimensional controller can be successful in simplifying control while maintaining accuracy, it is a behavior specific controller that does not generalize well to all behaviors. This would mean that the CNS uses task relevant low-dimensional controller and muscle synergies to simplify high dimensional control problems of movement and gives an explanation for perhaps why synergies would exist.
Valero-Cuevas et al. "Challenges and New Approaches to Proving the Existence of Muscle Synergies of Neural Origin"
They demonstrated feedback and feed forward related muscle synergies using cadaveric human hand and computational model of the human leg, simulating low dimensional muscle activations (EMG responses) creating the appearance of muscle synergies even in the absence of a specific neural controller. Hence this supports the claim that the low dimensionality of the muscle activation and constraints are not only of neural origin but also a combinational effect of bio-mechanical constraints. (by N.V.)
Mathew C. Tresch, et al. "The Construction of Movement by the Spinal Cord"
By looking at a number of motor behaviors and muscle EMG records they show that the motor behavior can be explained by a low dimensional method. This means that the muscle synergy method can explain motor behavior in vertebrate spinal cord. Their results could explain the simplification method that the spinal cord uses to control movements. (by Y.Z.)
Mathew C. Tresch, et al. "The Case For and Against Muscle Synergies"
The author presented evidence for and against the muscle synergy hypothesis. Although, many studies have provided evidence that the CNS can create movements with a low dimension of synergies, these experiments only analyze a limited range of movements or behaviors. A study that encompasses all possible movements of an organism and still manages to find a low dimension of muscle synergies would provide very strong evidence for this hypothesis. If the muscle synergy hypothesis were true, then it would show that the brain actively reduces the degrees of freedom to reduce computational load during movement. (by S.P.)
Robert L Sinburg et al. “Control of limb dynamics in Normal Subjects and Patients without Proprioception”
The experiments studied both normal subjects and patients with large-fiber sensory neuropathy (lack the sense of proprioception) moving their arms with slicing motions in different directions. It was observed that the patients had greater directional errors and conspicuous drifts at the end of movements. The authors infer that this is mainly due to the failure to compensate for their inertial variation along the motion of their arms and poor inter-joint coordination as result of impaired feedback. (by N.V.)
Gordon, James, et al. "Impairments of Reaching Movements in Patients Without Proprioception. I. Spatial Errors"
The authors studied multi-directional reaching movements between healthy subjects and patients lacking proprioception, by analyzing the latter's failure to reach targets. They provided evidence that directional dependent errors by patients are caused by a failure to account for the biomechanics of the limb, however the evidence was not enough to rule out other factors such as noisy signals or the lack of mechanical ability to know when they have stopped. A thorough understanding of why patients fail to make simple reaching movements could help provide better rehabilitation for patients and help us understand which contributions to movement are feedforward control vs. feedback control. (by S.P.)
P. Ramkumar et al., "Chunking as the result of an efficiency computation trade-off"
Sequence reaching in monkeys is studied. The authors show that the optimal motor control problem can be reframed as a series of chunks and local optimization within those chunks. This could be a possible strategy for the motor system in learning reaching movements. (by Y.Z.)
M. Conditt, F. Gandolfo & F. Mussa-Ivaldi, "The motor system does not learn the dynamics of the arm by rote memorization"
The authors provide evidence that after adapting to a force field making point-to-point reaches, circle movements in the null and force field are similar to those when circle movements are practiced directly. Thus the motor system does not merely memorize how to make movements, but rather learns general information about them. In turn this is strong evidence for an internal model that infers the dynamics of our reaches. (by M.B.)
L. N. Gonzalez Castro, M. A. Smith "The Binding of Learning to Action in Motor Adaptation"
They are interested in adaptation rates and the way they are improved. They study Plan Reference Learning versus Motion Reference Learning model and compare these two theories with actual data. They found that the experimental results are closer to MRL. Their results show the importance of feedback errors on the model formed in the CNS. Which indicates the crucial effect of feedback during adaptation (by Y.Z.)
T. Flash, "The Control of Hand Equilibrium Trajectories in Multi-Joint Arm Movements"
Human arm stiffness and equilibrium-point trajectory during multi-joint movement paper showed that stiffness of the limb is smaller than previously presented and that it changes dramatically throughout the movement. It is important to know because we can better understand the internal model of the brain based on presented data (by U.S.)
R. C. Miall, P. N. Haggard, "The curvature of human arm movements in the absence of visual experience"
They found that blind subjects make straighter reaches than the ones wearing blind folds. However, their perception of curvature is not statistically distinguishable. So this paper again (like the paper by Sergio) shows that vision is not a sufficient explanation for making straight reaches. (by Y.Z.)
M. Kawato, "Feedback-Error-Learning Neural Network for Supervised Motor Learning"
The author presented a new learning rule. This might be helpful when trying to understand unsupervised learning i.e. when we don't know what the desired output should be. (by V.S.)
K. Kording, D. Wolpert, "Bayesian integration in sensorimotor learning"
When making simple decisions subjects make correct use of both the likelihood and the prior distribution of events, consistent with Bayesian integration. It could be the case the the brain makes use of similar Bayesian computations in many other processes. (by M.B.)
M.Berniker, M. Voss, K. Kording, "Learning Priors for Bayesian Computations in the Nervous System"
The paper shows how Bayesian statistics can explain human behavior by combining prior and likelihood. Is it important in optimizing rational decision making and minimizing uncertainties. (by U.S.)
Lauren E. Sergio, Stephan H. Scott, "Hand and Joint Paths During Reaching Movements With and Without Vision"
The aim of this paper is to see the effect of vision on limb trajectories.
The results show that subjects that were congenitally blind make straighter movements compared to blindfolded subjects.
They make a conclusion that there is a powerful tendency of making straight hand trajectories, even when no vision is involved. They also mention that visual feedback itself is not sufficient to explain why subjects tend to generate straight hand paths during movements. (by Y.Z.)
Shadmehr & Mussa-Ivaldi, "Adaptive Representation of Dynamics during Learning of a Motor Task"
The authors addressed whether we adapt to change in dynamics, the presence of a kinematic plan, generalizing of the internal model, what the coordinates of the internal model are, and whether they could simulate the results of the experiment. They found that we do adapt, there exists a kinematic plan, the internal model is intrinsic and can be generalized over different workspaces. Also, the results could be simulated. The authors studied the aftereffects, tested how you adapt, and adaptation in different workspaces. They also showed evidence for an intrinsic coordinate based system. (by A.C.)
Maurice A. Smith et. al., "Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning"
In this study it is shown that a two state, gain-independent, multi-rate model is a better representation for motor adaptation simulations. The spontaneous rebound in channel trials could be explained by this model.
This paper is the first study on spontaneous rebound in motor adaptation. Their model shows that in motor learning many time scales are involved. (by Y.Z.)
Fritzie Arce et. al., "Differences in Context and Feedback Result in Different Trajectories and Adaptation Strategies in Reaching"
In this paper the authors found that prior history of learning influences the control policy during adaptation and continuous visual feedback is needed to make straight reaches in a force field.
Their experiment is important to know that under some certain conditions subjects do make curved trajectories in planer reaching movements. (by Y.Z.)
John W. Krakauer, Maria-Felice Ghilardi and Claude Ghez, "Independent learning of internal models for kinetic and dynamic control of reaching"
The main finding that was supported by the results of the experiments is that learning of another model with conflicting sensorimotor mappings interferes with the memory of previously learned models. This concept improves current computational methods as it suggests including both kinematic and dynamic sources of error, instead of combining both performance errors. (by M.D.)
Sarah E. Criscimagna-Hemminger, Opher Donchin, Micheal S. Gazzaniga and Reza Shadmehr, "Learned Dynamics of Reaching Movements Generalize From Dominant to Nondominant Arm"
The purpose of the paper is to investigate inter-limb generalization. The goal is to discover the type of coordinate this generalization is based on and the neural basis of it. The result shows that generalization occurred only from the dominant to the non-dominant hand and it is based on an extrinsic coordinate. Moreover, inter-hemisphere communication does not depend on the corpus callosum. The result is useful to understand the ability of brain in executing what it has learned. (by Y.Z.)