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In the Turing test a computer model is deemed to "think intelligently" if it can generate answers that are indistinguishable from those of a human. We developed an analogous Turing-like handshake test to determine if a machine can produce similarly indistinguishable movements. The test is administered through a telerobotic system in which an interrogator holds a robotic stylus and interacts with another party - artificial or human with varying levels of noise. The interrogator is asked which party seems to be more human. Here, we compare the human-likeness levels of three different models for handshake: (1) Tit-for-Tat model, (2) λ model, and (3) Machine Learning model. The Tit-for-Tat and the Machine Learning models generated handshakes that were perceived as the most human-like among the three models that were tested. Combining the best aspects of each of the three models into a single robotic handshake algorithm might allow us to advance our understanding of the way the nervous system controls sensorimotor interactions and further improve the human-likeness of robotic handshakes.

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The brain controls rhythmic movement through neural circuits combining visual information with proprioceptive information from the limbs. Although rhythmic movements are fundamental to everyday activities the specific details of the responsible control mechanisms remain elusive. We tested 39 young adults who performed flexion/extension movements of the forearm. We provided them with explicit knowledge of the amplitude and the speed of their movements, whereas frequency information was only implicitly available. In a series of 3 experiments, we demonstrate a tighter control of frequency compared with amplitude or speed. We found that in the absence of visual feedback, movements had larger amplitude and higher peak speed while maintaining the same frequency as when visual feedback was available; this was the case even when participants were aware of performing overly large and fast movements. Finally, when participants were asked to modulate continuously movement frequency, but not amplitude, we found the local coefficient of variability of movement frequency to be lower than that of amplitude. We suggest that a misperception of the generated amplitude in the absence of visual feedback, coupled with a highly accurate perception of generated frequency, leads to the performance of larger and faster movements with the same frequency when visual feedback is not available. Relatively low local coefficient of variability of frequency in a task that calls for continuous change in movement frequency suggests that we tend to operate at a constant frequency at the expense of variation in amplitude and peak speed.

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In everyday life, we frequently alternate between performing discrete and rhythmic movements. When performing a periodic movement, two distinct movement types can be distinguished: highly harmonic vs. discrete-like. The harmonicity of the movement is used to classify it as one or the other. We asked: (1) whether the frequency at which a periodic movement is performed affects the harmonicity of the resultant movement; and (2) what underlies switching between these movement types. To answer these questions, we studied horizontal flexion/extension forearm movements in 13 young adults over a wide range of frequencies. Movements were performed either at a fixed frequency, or at gradually increasing or decreasing target frequencies. We found movement harmonicity to depend on the frequency of the movement. Furthermore, we found a reverse hysteresis behavior, where participants switched movement type in anticipation of the future-required frequency. These findings suggest that predictive control is employed in switching between movement types.

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We studied the effect of delay on perception and action in contact with a force field that emulates elastic soft tissue with a rigid nonlinear boundary. Such a field is similar to forces exerted on a needle during teleoperated needle insertion. We found that delay causes motor underestimation of the stiffness of this nonlinear soft tissue, without perceptual change. These experimental results are supported by simulation of a simplified mechanical model of the arm and neural controller, and a model for perception of stiffness, which is based on regression in the force-position space. In addition, we show that changing the gain of the teleoperation channel cancels the motor effect of delay without adding perceptual distortion. We conclude that it is possible to achieve perceptual and motor transparency in virtual one-dimensional remote needle insertion task.

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BACKGROUND: Proprioception provides feedback which is essential for adequate motor control. Despite having detrimental functional implications, the assessment of proprioception deficits in current clinical practice is mostly qualitative and inadequate for diagnosis and longitudinal monitoring of subtle impairments and their effect on motor function. PURPOSE: To evaluate a novel quantitative approach to the assessment of proprioception deficits in stroke patients. METHOD: We designed and implemented an automated protocol where a magnetic motion tracking system and a sensor attached to each of the patient's hands, enables registration of trajectories in 3D coordinates. In this protocol the patient's affected and healthy hands are placed respectively below and above a square board. With vision blocked, the subject's affected hand is passively moved to one of four locations, and then the patient is instructed to actively position the healthy hand directly above his/her perceived location of the affected hand. The positional difference between the two hands is automatically recorded by the system. This procedure is repeated several times and the magnitude and direction of errors are used to quantify the proprioception deficit. The data for this pilot study was collected in a sample of 22 stroke patients and an age-matched group of neurologically intact subjects. RESULTS: Stroke patients had significantly higher mean distance error compared with the control group (average values of 7.9 and 5.3 cm, respectively), and showed higher instability (variance) in repeated performance (average values of the standard deviation of errors 3.4 and 1.8 cm, respectively). Significant correlation was found between the mean distance error and the results of semi-quantitative clinical tests of proprioception. CONCLUSION: The system provides a reliable quantitative measure of upper limb proprioception, offering considerable advantage over the traditional means applied in the clinic.

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The stiffness of the environment with which we come in contact is the local derivative of a force field. The boundary of an elastic field is a singular region where local stiffness is ill-defined. We found that subjects interacting with delayed force fields tend to underestimate stiffness if they do not move across the boundary. In contrast, they tend to overestimate stiffness when they move across the elastic field boundary. We propose a unifying computational model of stiffness perception based on an active process that combines the concurrent operations of a force and of a position-control system.

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It has recently been suggested that the nervous system employs forward models for the purpose of motor control. The evidence for this hypothetical computational structure comes from various sources, theoretical and experimental. The purpose of this commentary is to distinguish between three different structures that are used to support the forward model hypothesis: (1) output predictor, (2) state estimator, and (3) distal teacher. It is possible that the brain employs all these structures for control. However, the general term forward model could be misleading since evidence for one structure could be misinterpreted as supporting evidence for the other structures.

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Studies of arm movements have shown that subjects learn to compensate predictable mechanical perturbations by developing a representation of the relation between the state of motion of the arm and the perturbing forces. Here, we tested the hypothesis that subjects construct internal representations of two different force fields and switch between them when presented with an alternating sequence of these fields. Our results do not support this hypothesis. Subjects performed reaching movements in four sessions over 4 days. On the 1st day the robotic manipulandum perturbed the movement by perpendicular force that alternated its direction after each movement. Subjects were unable to construct the two underlying models and switch between them. On the 2nd day only one field was applied and well learned. On the 3rd day only the other field was applied and well learned. Then the experiment of the 1st day was repeated on the 4th day. Even after this extensive training subjects showed no signs of improved performance with alternating fields. This result combined with previous studies suggests that the central nervous system has a strong tendency to employ a single internal model when dealing with a sequence of perturbations.

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The construction of a feed-forward controller frequently requires the estimation of an inverse function. Two possible methods to achieve this are: (i) learning the best estimated inverse (BEI), a method that is sometimes referred to as direct inverse learning and (ii) learning the inverse of the best estimator (IBE), a method that is sometimes referred to as indirect inverse learning. We analyze a general control problem, in the presence of noise, and show analytically that these two methods are asymptotically significantly different, even for simple linear non-redundant systems. We further demonstrate that the IBE method is typically superior for control purposes.

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Rapid human movements exhibit a quasilinear relationship between their amplitude and maximum velocity and a log-like relationship between their amplitude and duration. The authors demonstrate that those well-observed relations can be obtained with a simple nonlinear muscle model and a pulse-step control scheme. That result encourages the use of nonlinear musculoskeletal models with simple control schemes for modeling human ballistic movements.

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Reaching movement is a fast movement towards a given target. The main characteristics of such a movement are straight path and a bell-shaped speed profile. In this work a mathematical model for the control of the human arm during ballistic reaching movements is presented. The model of the arm contains a 2 degrees of freedom planar manipulator, and a Hill-type, non-linear mechanical model of six muscles. The arm model is taken from the literature with minor changes. The nervous system is modeled as an adjustable pattern generator that creates the control signals to the muscles. The control signals in this model are rectangular pulses activated at various amplitudes and timings, that are determined according to the given target. These amplitudes and timings are the parameters that should be related to each target and initial conditions in the work-space. The model of the nervous system consists of an artificial neural net that maps any given target to the parameter space of the pattern generator. In order to train this net, the nervous system model includes a sensitivity model that transforms the error from the arm end-point coordinates to the parameter coordinates. The error is assessed only at the termination of the movement from knowledge of the results. The role of the non-linearity in the muscle model and the performance of the learning scheme are analysed, illustrated in simulations and discussed. The results of the present study demonstrate the central nervous system's (CNS) ability to generate typical reaching movements with a simple feedforward controller that controls only the timing and amplitude of rectangular excitation pulses to the muscles and adjusts these parameters based on knowledge of the results. In this scheme, which is based on the adjustment of only a few parameters instead of the whole trajectory, the dimension of the control problem is reduced significantly. It is shown that the non-linear properties of the muscles are essential to achieve this simple control. This conclusion agrees with the general concept that motor control is the result of an interaction between the nervous system and the musculoskeletal dynamics.

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