Stabilization Strategies for Unstable Dynamics

Background: When humans are faced with an unstable task, two different stabilization mechanisms are possible: a highstiffness strategy, based on the inherent elastic properties of muscles/tools/manipulated objects, or a low-stiffness strategy, based on an explicit positional feedback mechanism. Specific constraints related to the dynamics of the task and/or the neuromuscular system often force people to adopt one of these two strategies. Methodology/Findings: This experiment was designed such that subjects could achieve stability using either strategy, with a marked difference in terms of effort and control requirements between the two strategies. The task was to balance a virtual mass in an unstable environment via two elastic linkages that connected the mass to each hand. The dynamics of the mass under the influence of the unstable force field and the forces applied through the linkages were simulated using a bimanual, planar robot. The two linkages were non-linear, with a stiffness that increased with the amount of stretch. The mass could be stabilized by stretching the linkages to achieve a stiffness that was greater than the instability coefficient of the unstable field (high-stiffness), or by balancing the mass with sequences of small force impulses (low-stiffness). The results showed that 62 % of the subjects quickly adopted the high-stiffness strategy, with stiffness ellipses that were aligned along the direction of instability. The remaining subjects applied the low-stiffness strategy, with no clear preference for the orientation of the stiffness ellipse

Motor Learning and Motor Control Mechanisms in an Haptic Dyad

The word \u201cdyad\u201d defines the interaction between two human or cybernetic organisms. During such interaction, there is an organized flow of information between the two elements of the dyad, in a fully bidirectional manner. With this mutual knowledge they are able to understand the actual state of the dyad as well as the previous states and, in some cases, to predict a response for possible scenarios. In the studies presented in this thesis we aim to understand the kind of information exchanged during dyadic interaction and the way this information is communicated from one individual to another not only in a purely dyadic context but also in a more general social sense, namely dissemination of knowledge via physical and non-physical interpersonal interactions. More specifically, the focus of the experimental activities will be on motor learning and motor control mechanisms, in the general context of embodied motor cognition. Solving a task promotes the creation of an internal representation of the dynamical characteristics of the working environment. An understanding of the environmental characteristics allows the subjects to become proficient in such task. We also intended to evaluate the application of such a model when it is created and applied under different conditions and using different body parts. For example, we investigated how human subjects can generalize the acquired model of a certain task, carried out by means of the wrist, in the sense of mapping the skill from the distal degrees of freedom of the wrist to the proximal degrees of freedom of the arm (elbow & shoulder), under the same dynamical conditions. In the same line of reasoning, namely that individuals solving a certain task need to develop an internal model of the environment, we investigated in which manner different skill levels of the two partners of a dyad interfere with the overall learning/training process. It is known indeed that internal models are essential for allowing dyadic member to apply different motor control strategies for completing the task. Previous studies have shown that the internal model created in a solo performance can be shared and exploited in a dyadic collaboration to solve the same task. In our study we went a step forward by demonstrating that learning an unstable task in a dyad propitiates the creation of a shared internal model of the task, which includes the representation of the mutual forces applied by the partners. Thus when the partners in the dyad have different knowledge levels of the task, the representation created by the less proficient partner can be mistaken since it may include the proficient partner as part of the dynamical conditions of the task instead of as the assistance helping him to complete the experiments. For this reason we implemented a dyadic learning protocol that allows the na\uefve subject to explore and create an accurate internal model, while exploiting, at the same time, the advantage of working with an skilled partner

Sensorimotor learning under switching dynamics

Humans have a remarkable capacity to learn new motor behaviours without forgetting old ones. This capacity relies on the ability to acquire and express multiple motor memories without interference. Here we combine behavioural experiments and computational modelling to investigate how the sensorimotor system uses contextual information to create, update and recall motor memories. We first examine the role of muscle co-contraction in the learning of novel movement dynamics. We show that muscle co-contraction, as measured by surface electromyography, accelerates motor learning. We then explore the role of control points on objects in the formation of motor memories during object manipulation. We show that opposing dynamic perturbations, which interfere when controlling a single location on an object, can be learned when each is associated with a separate control point. To account for these results, we develop a parametric switching state-space model, in which the association between cues (control points) and contexts (dynamics) is learned from experience rather than fixed. We then extend this model to a Bayesian nonparametric switching state-space model, in which the number of contexts and cues are learned online rather than specified in advance. This model can instantiate new memories when novel perturbations are experienced and exhibits spontaneous recovery of a memory that has been ostensibly overwritten. To test the model, we perform an experiment in which we briefly present a previously experienced perturbation after behaviour has returned to baseline. As predicted, we observe a qualitatively distinct and more pronounced form of recovery, which we refer to as evoked recovery. Finally, we investigate Bayesian context estimation using single-trial learning. We show that people are able to learn novel associations between cues and contexts and that they use both contextual cues and state feedback to infer the current context and partition learning between memories. Taken together, these findings further the understanding of the behaviour and computational principles of sensorimotor learning under switching dynamics.Engineering and Physical Sciences Research Counci

Principles of sensorimotor learning.

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved

Investigating sensory-motor interactions to shape rehabilitation

Over the last decades, robotic devices for neurorehabilitation have been developed with the aim of providing better and faster improvement of motor performance. These devices are being used to help patients repeat movements and (re)learn different dynamic tasks. Over the years, these devices have become bigger and more complex, so as to provide the end user with a more realistic and sophisticated stimuli while still allowing the experimenter to have control over the interaction forces that can potentially shape the motor behaviour. However, experimental results have shown no clear advantage of these complex devices over simpler versions. In this context, this thesis investigates sensory-motor processes of human interaction, which can help us understand the main issues for rehabilitation devices and how to overcome the limitations of simple devices to train particular motor behaviours. Conventional neurorehabilitation of motor function relies on haptic interaction between the patient and physiotherapist. However, how humans deal with human-human interactions is largely unknown, and has been little studied. In this regard, experiments of the first section of the thesis investigate the mechanisms of interaction during human-human collaborative tasks. It goes from identifying the different strategies that dyads can take to proposing methods to measure and understand redundancy and synchrony in haptic interactions. It also shows that one can shape the interaction between partners by modifying only the visual information provided to each agent. Learning a novel skill requires integration of different sensory modalities, in particular vision and proprioception. Hence, one can expect that learning will depend on the mechanical characteristics of the device. For instance, a device with limited degrees of freedom will reduce the amount of information about the environment, modify the dynamics of the task and prevent certain error-based corrections. To investigate this, the second section of the thesis examines whether the lack of proprioceptive feedback that is created due to mechanical constraints or haptic guidance can be substituted with visual information. Psychophysical experiments with healthy subjects and some preliminary experiments with stroke patients presented in this thesis support the idea that by incorporating task-relevant visual feedback into simple devices, one could deliver effective neurorehabilitation protocols. The contributions of the thesis are not limited to the role of visual feedback to shape motor behaviour, but also advance our understanding on the mechanisms of learning and human-human interaction

Temporal structure in haptic signaling under a cooperative task

Haptic communication between humans plays an important role in society. Although this form of communication is ubiquitous at all levels of society and of human development, little is known about how synchronized coordination of motion between two persons leads to higher-order cognitive functions used in communication. In this study, we developed a novel experimental paradigm of a coin-collecting task in which participants used their hands to control a rod to jointly collect the coins on the screen. We characterized the haptic interactions between paired participants while they were taking part in a cooperative task. The individual participants first completed this task on their own and then with a randomly assigned partner for the cooperative task. Single participant experiments were used as a baseline to compare results of the paired participants. Forces applied to the rod were translated to four possible haptic states which encode the combination of the haptic interactions. As a next step, pairs of consecutive haptic states were then combined into 16 possible haptic signals which were classified in terms of their temporal patterns using a Tsallis q-exponential function. For paired participants, 80% of the haptic signals could be fit by the Tsallis q-exponential. On the other hand, only 30% of the signals found in the single-participant trials could be fit by the Tsallis q-exponential. This shows a clear difference in the temporal structures of haptic signals when participants are interacting with each other and when they are not. We also found 94 a large difference in the number of haptic signals used by paired participants and singles. Single participants only used 1/4 of the possible haptic signals. Paired participants, on the other hand, used more than half of the possible signals. These results suggest that temporal structures present in haptic communication could be linked to the emergence of language at an evolutionary level

The role of reinforcement and contextual cues in the acquisition and expression of motor memories

Each day we interact with a wide variety objects, from using our computer, to driving a car or preparing a cup of coffee. We can complete the actions necessary to use these objects with little to no error or relearning from the last time we completed the same task. As the phrase ‘just like riding a bicycle’ implies, once learned, certain actions can always be called upon, regardless of how infrequently we execute them. In the following chapters, we focus on the initial learning of goal-directed motor commands, investigating how people first learn to interact with a novel object and what memory they retain from this interaction. First, we investigated what cues serve as indicators that a particular set of motor commands should be retained for future recall. Our results suggested that decreases in reinforcement signaled participants of a change in the dynamics of the tool, allowing participants to separate and retain multiple motor memories for use of the same tool. From these experiments, we determined that reinforcement of actions also served as a critical cue to recall upon these motor memories. When reinforcement of the current motor commands was withheld, participants switched their actions and recalled previous motor training. We next investigated if the retention of a particular set of motor commands was specific to the tool on which these commands were learned. Here, we found that when participants encountered a new tool similar to the one they had used for training, they relied upon their memory of the trained tool, and generalized some of their previous learning. We then asked if the contents of motor memory were stable or if modifications occurred with continued training. The results of these experiments suggested that with sufficient time away from practice, motor memories become more efficient and we begin to minimize energetic inefficiencies in our movements. Finally, we revisited the idea of reinforcement and action selection. We found that patients diagnosed with Parkinson’s Disease (PD), were less sensitive to a lack of reinforcement, which in turn lead to less motor exploration in these patients as compared to healthy controls. Thesis advisor: Dr. Reza Shadmehr ([email protected]) Thesis committee: Drs Reza Shadmehr, Amy Bastian and Jeffrey Ellenboge

Assessing Performance, Role Sharing, and Control Mechanisms in Human-Human Physical Interaction for Object Manipulation

abstract: Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

WALKING FOR OBJECT TRANSPORT: AN EXAMINATION OF THE COORDINATIVE ADAPTATIONS TO LOCOMOTOR, PERCEPTUAL, AND MANUAL TASK CONSTRAINTS

The goal of this dissertation was to understand how the intrinsic dynamics of gait adapt to support the performance of an ecologically relevant object transport task. A common object transport task is walking with a cup of water. Because the water can move relatively independent of the cup, the cup and water system is classified as a complex object. To model this task participants carried a cup with a wooden lid placed on top. On the lid there was a circular region with the same circumference as the cup and a ball. The object of the task was to keep the ball inside the circular region. We explored two questions: 1) how do the intrinsic coordinative gait dynamics adapt to support object transport during walking? And 2) how do individuals adapt to manually control a complex object when asked to concurrently attend to visual information? To address question 1, participants walked on a treadmill at six speeds (0.4 - 1.4 m/s) and performed three conditions: normal walking, walking with a cup only (Cup), and walking with the cup and ball (Cup-Ball). When performing the Cup-Ball condition, as gait speed increased, pelvis-thorax coordination was more in-phase compared to the normal walking and the Cup conditions. Arm-leg coordination was affected by the performance of the Cup-Ball condition. On the constrained side arm-leg coordination was 2:1 while a 1:1 relationship was maintained on the unconstrained side. A correlation between the amplitude of the unconstrained arm and manual task performance revealed a significant negative correlation as gait speed increased, indicating that individuals who reduced their arm swing performed better. To address question 2, participants walked on a treadmill at three gait speeds under four task conditions: normal walking, walking with the cup and ball system (Cup-Ball), walking while identifying visual stimuli (Visual), and a combined condition where participants walked with the cup and ball system while identifying visual stimuli (Cup-Ball-Vis). The addition of the visual task in study 2 resulted in the head orientation to be more extended relative to the trunk with a larger range of motion compared to the manual task only condition; participants optimized on the visual task at the expense of manual task performance. In both manual task conditions pelvis-thorax coordination was more in-phase as gait speed increased and more variable compared to the walking only condition. The latter result demonstrates the functionality of increased coordination variability during object transport tasks. The amplitude of the unconstrained arm decreased as the system became more constrained (i.e., going from walking only to Cup-Ball to Cup-Ball-Vis tasks). Although the arm amplitude decreased, the unconstrained arm maintained a 1:1 arm-leg coordination while the constrained arm was in a 2:1 relationship for both manual task conditions. This result demonstrates that the unconstrained arm continues to move to counteract angular momentum imparted by the legs while the arm carrying the object is coupled to the step frequency, counteracting disturbances imposed by heel contacts. The overall results from both studies demonstrate that the body’s natural walking dynamics adapt to support manual task performance. The segments not directly involved in the task continue to interact to maintain intrinsic gait dynamics. This dissertation makes significant contributions to the literature by demonstrating: 1) asymmetries in arm-leg coordination are exploited by the body to maintain manual task performance and intrinsic gait dynamics; 2) amplitude of the freely swinging arm is an important factor in task performance during object transport; and 3) increased variability at the level of the pelvis-thorax interaction plays a functional role in maintaining both manual and visual task performance. The significance of the findings here is that they demonstrate how task constraints alter intrinsic coordination dynamics during walking in order to support performance while at the same time maintaining gait stabilit

Motor learning during reaching movements: model acquisition and recalibration

This thesis marks a departure from the traditional task-based distinction between sensorimotor adaptation and skill learning by focusing on the mechanisms that underlie adaptation and skill learning. I argue that adaptation is a recalibration of an existing control policy, whereas skill learning is the acquisition and subsequent automatization of a new control policy. A behavioral criterion to distinguish the two mechanisms is offered. The first empirical chapter contrasts learning in visuomotor rotations of 40° with learning left-right reversals during reaching movements. During left-right reversals, speed-accuracy trade-offs increased and offline gains emerged, whereas during visual rotations, speed-accuracy trade-offs remained constant and instead of offline gains, there was offline forgetting. I argue that these dissociations reflect differences in the underlying learning mechanisms: acquisition and recalibration. The second empirical chapter tests whether the dissociation based on time-accuracy trade-offs reveals a general property of recalibration or whether instead the interpretation is limited to the specific contrast between left-right reversals and visuomotor rotations. When the size of the prediction error– the difference between intended and perceived movement – was gradually increased participants switched from recalibration to control policy acquisition. This switching point can be derived by considering the role of internal models in recalibration: If the internal model that learns from errors and the environment are too dissimilar – e.g. in left-right reversal and large rotations– recalibration would cause the system to learn from errors in the wrong way, such that prediction errors would increase further. To address this problem the final empirical chapter explores if the way the system learns from errors can be reversed. In conclusion, the results provide behavioral criteria to differentiate between adaptation and skill learning. By exploring the boundaries of recalibration this thesis contributes to a more principled understanding of the mechanisms involved in adaptation and skill learning