Monitoring of finger manipulation without disturbing the inherent functionalities is critical to understand the sense of natural touch. However, worn or attached sensors affect the natural feeling of the skin. We developed nanomesh pressure sensors that can monitor finger pressure without detectable effects on human sensation. The effect of the sensor on human sensation was quantitatively investigated, and the sensor-applied finger exhibits comparable grip forces with those of the bare finger, even though the attachment of a 2-micrometer-thick polymeric film results in a 14% increase in the grip force after adjusting for friction. Simultaneously, the sensor exhibits an extreme mechanical durability against cyclic shearing and friction greater than hundreds of kilopascals.

During interaction with objects, we form an internal representation of their mechanical properties. This representation is used for perception and for guiding actions, such as in precision grip, where grip force is modulated with the predicted load forces. In this study, we explored the relationship between grip force adjustment and perception of stiffness during interaction with linear elastic force fields. In a forced-choice paradigm, participants probed pairs of virtual force fields while grasping a force sensor that was attached to a haptic device. For each pair, they were asked which field had higher level of stiffness. In half of the pairs, the force feedback of one of the fields was delayed. Participants underestimated the stiffness of the delayed field relatively to the nondelayed, but their grip force characteristics were similar in both conditions. We analyzed the magnitude of the grip force and the lag between the grip force and the load force in the exploratory probing movements within each trial. Right before answering which force field had higher level of stiffness, both magnitude and lag were similar between delayed and nondelayed force fields. These results suggest that an accurate internal representation of environment stiffness and time delay was used for adjusting the grip force. However, this representation did not help in eliminating the bias in stiffness perception. We argue that during performance of a perceptual task that is based on proprioceptive feedback, separate neural mechanisms are responsible for perception and action-related computations in the brain.

When manipulating objects, we use kinesthetic and tactile information to form an internal representation of their mechanical properties for cognitive perception and for preventing their slippage using predictive control of grip force. A major challenge in understanding the dissociable contributions of tactile and kinesthetic information to perception and action is the natural coupling between them. Unlike previous studies that addressed this question either by focusing on impaired sensory processing in patients or using local anesthesia, we used a behavioral study with a programmable mechatronic device that stretches the skin of the fingertips to address this issue in the intact sensorimotor system. We found that artificial skin-stretch increases the predictive grip force modulation in anticipation of the load force. Moreover, the stretch causes an immediate illusion of touching a harder object that does not depend on the gradual development of the predictive modulation of grip force.

To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddle’s movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping.

Daily interaction with the environment consists of moving with or without objects. Increasing interest in both types of movements drove the creation of computational models to describe reaching movements and, later, to describe a simplified version of object manipulation. The previously suggested models for object manipulation rely on the same optimization criteria as models for reaching movements, yet there is no single model accounting for both tasks that does not require reminimization of the criterion for each environment. We suggest a unified model for both cases: minimum acceleration with constraints for the center of mass (MACM). For point-to-point reaching movement, the model predicts the typical rectilinear path and bell-shaped speed profile as previous criteria. We have derived the predicted trajectories for the case of manipulating a mass-on-spring and show that the predicted trajectories match the observations of a few independent previous experimental studies of human arm movement during a mass-on-spring manipulation. Moreover, the previously reported "unusual" trajectories are also well accounted for by the proposed MACM. We have tested the predictions of the MACM model in 3 experiments with 12 subjects, where we demonstrated that the MACM model is equal or better (Wilcoxon sign-rank test, P < 0.001) in accounting for the data than three other previously proposed models in the conditions tested. Altogether, the MACM model is currently the only model accounting for reaching movements with or without external degrees of freedom. Moreover, it provides predictions about the intermittent nature of the neural control of movements and about the dominant control variable.