Biomedical subjects

Ferdinando A Mussa-Ivaldi

Publications and source records attributed to Ferdinando A Mussa-Ivaldi.

7 recordsLinked to original sources

PubMed2003

Generalization of object manipulation skills learned without limb motion.

Recent work suggests that human subjects may learn mappings between object motion and exerted torque during manipulation of freely pivoting or unstable objects. In the present work, we studied an object manipulation task involving no arm movement to determine how subjects internally represent the force-motion relationship of an object during a skilled manipulation task. Human subjects learned to balance a simulated inverted pendulum. The simulation was controlled by pressing on a fixed force sensor, and applied forces resulted in motion of the simulated pendulum on a computer screen according to its equation of motion. Each subject initially learned the task in one arm posture and was tested 1 d later in a new arm posture. In one test condition, the effects of arm torque were matched to the original task, and in the other test condition, the simulation was unchanged. The pattern of skill transfer to different arm postures suggested that subjects had learned joint torque responses rather than a general model of the object interface forces. A second experiment showed that the advantage of training with matched arm torques was object specific, because torque-matched training on a tracking task involving similar forces was not a substitute for training in the balancing task.

Adult↗

PubMed2003

Evidence for a specific internal representation of motion-force relationships during object manipulation.

Human subjects learned a tracking task which required them to point at a moving target with the free end of an inverted pendulum object. In order to determine how subjects represented this object internally, we studied learning interference between variants of this task in which the pendulum object had either stable or unstable dynamics. Using a novel method, agreement between possible internal representations of the two tasks was estimated by analysis of the motion-to-torque relationships experienced by each subject as they manipulated each object. It was possible to predict retention of the primary task on day 2 from our measure of agreement between primary and interfering tasks on day 1. This result suggests that the subjects learned the correct torque patterns to use to produce specific desired patterns of motion as they learned the balancing task. Surprisingly, the analyses indicate that retention was not impaired when similar motions of the two objects required retrieval of incompatible torque responses, but retention was impaired when similar patterns of motion in the two tasks required similar patterns of applied torque. These findings can be accounted for by a simple model of how multiple similar torque responses are selected and retrieved from memory when responses are freely chosen.

Adult↗

PubMed2003

Sequence, time, or state representation: how does the motor control system adapt to variable environments?

Does the observation of well-timed movements imply the existence of some internal representation of time, such as a hypothetical neural clock? Here we report the results of experiments designed to investigate whether subjects form a correct adaptive representation of mechanical environments that change in a very predictable manner. In these experiments, subjects were asked to execute arm movements over a two-dimensional workspace while experiencing time-dependent disturbing forces. We provide a formal definition for time representation and conclude that our subjects didn't use time representation for motor adaptation under the tested conditions. Subjects performed arm-reaching movements in the following experiments: (1) six experiments in a sinusoidal time-varying force field; (2) six experiments in a simple sequence of alternating viscous force fields, in which the number of targets allowed for the approximation of the force by a complex state-dependent force field; and (3) six experiments in the same simple sequence of alternating viscous force fields, in which no state-dependent force field approximation was possible. We found that the subjects did not adapt to the time-varying force field and were unable to form an adequate representation of the simple sequence of force fields. In the latter case, whenever possible, they adapted to a single state-dependent field that produced forces similar to the two alternating fields. This state-dependent field produced the same forces as the applied sequence of fields only over the trajectories that subjects executed during the training phase. However, the state-dependent field was inadequate to produce the correct forces generated by the field sequence over a new set of trajectories.These results are not consistent with the hypothesis that subjects would develop a correct representation of time-dependent forces, at least under the tested circumstances. We speculate that the system responsible for adaptation of movements to external forces may be unable to employ temporal representation. While it is possible that such a representation may emerge in a more prolonged and/or intense training, our findings indicate a preference by the adaptive system to generalize based on representing dependence of external forces upon state rather than upon time.

Adaptation, Physiological↗

PubMed2003

Brain-machine interfaces: computational demands and clinical needs meet basic neuroscience.

As long as 150 years ago, when Fritz and Hitzig demonstrated the electrical excitability of the motor cortex, scientists and fiction writers were considering the possibility of interfacing a machine with the human brain. Modern attempts have been driven by concrete technological and clinical goals. The most advanced of these has brought the perception of sound to thousands of deaf individuals by means of electrodes implanted in the cochlea. Similar attempts are underway to provide images to the visual cortex and to allow the brains of paralyzed patients to re-establish control of the external environment via recording electrodes. This review focuses on two challenges: (1) establishing a 'closed loop' between sensory input and motor output and (2) controlling neural plasticity to achieve the desired behavior of the brain-machine system. Meeting these challenges is the key to extending the impact of the brain-machine interface.

Animals↗

PubMed2003

Dynamical dimension of a hybrid neurorobotic system.

The goal of this work is to understand how neural tissue can be programed to execute predetermined functions. We developed a research tool that includes the brainstem of a lamprey and a two-wheeled robot interconnected in a closed loop. We report here the development of a framework for studying the dynamics of the neural tissue based on the interaction of this tissue with the robot.

Animals↗

PubMed2002

Linear combinations of nonlinear models for predicting human-machine interface forces.

This study presents a computational framework that capitalizes on known human neuromechanical characteristics during limb movements in order to predict human-machine interactions. A parallel-distributed approach, the mixture of nonlinear models, fits the relationship between the measured kinematics and kinetics at the handle of a robot. Each element of the mixture represented the arm and its controller as a feedforward nonlinear model of inverse dynamics plus a linear approximation of musculotendonous impedance. We evaluated this approach with data from experiments where subjects held the handle of a planar manipulandum robot and attempted to make point-to-point reaching movements. We compared the performance to the more conventional approach of a constrained, nonlinear optimization of the parameters. The mixture of nonlinear models accounted for 79 +/- 11% (mean +/- SD) of the variance in measured force, and force errors were 0.73 +/- 0.20% of the maximum exerted force. Solutions were acquired in half the time with a significantly better fit. However, both approaches suffered equally from the simplifying assumptions, namely that the human neuromechanical system consisted of a feedforward controller coupled with linear impedances and a moving state equilibrium. Hence, predictability was best limited to the first half of the movement. The mixture of nonlinear models may be useful in human-machine tasks such as in telerobotics, fly-by-wire vehicles, robotic training, and rehabilitation.

Artificial Intelligence↗

PubMed2002

Manipulating objects with internal degrees of freedom: evidence for model-based control.

There is substantial evidence that humans possess an accurate and adaptable internal model of the dynamics of their arm that is utilized by the nervous system for controlling arm movements. However, it is not known if such model-based strategies are used for controlling dynamical systems outside the body. The need to predict events in the external world is not restricted to the execution of reaching movements or to the handling of rigid tools. Model-based control may also be critical for performing functional tasks with non-rigid objects such as stabilizing a cup of coffee. The present study investigated the strategies used by humans to control simple mass-spring objects. Subjects made straight line reaching movements to a target while interacting with a robotic manipulandum that simulated the dynamics of a one-dimensional mass on a spring. After learning, neither hand nor object kinematics returned to those of free reaching, suggesting that this task was not learned as a perturbation of free reaching. Although there are control strategies (such as slowing the movement of the hand) that would require little or no knowledge of object dynamics, subjects did not adopt these strategies. Instead, they tailored their motor commands to the particular object being manipulated. When object parameters were unexpectedly altered in a way that required no changes in kinematics to successfully complete the task, subjects nonetheless exhibited substantial kinematic deviations. These deviations were consistent with those predicted by a model of the arm-plus-object system driven by a low-impedance controller that incorporated an explicit inverse model of arm-plus-object dynamics. The observed behavior could not be reproduced by a controller that relied on modulating hand impedance alone with no inverse model. These results were therefore consistent with the hypothesis that subjects learn to control the kinematics of manipulated objects by forming an internal model that specified the forces to be exerted by the hand on the object to induce the desired motion of that object.

Adaptation, Physiological↗