Reach adaptation to online target error

Why do movements drift in the dark? Passive versus active mechanisms of error accumulation

When vision of the hand is unavailable, movements drift systematically away from their targets. It is unclear, however, why this drift occurs. We investigated whether drift is an active process, in which people deliberately modify their movements based on biased position estimates, causing the real hand to move away from the real target location, or a passive process, in which execution error accumulates because people have diminished sensory feedback and fail to adequately compensate for the execution error. In our study participants reached back and forth between two targets when vision of the hand, targets, or both the hand and targets was occluded. We observed the most drift when hand vision and target vision were occluded and equivalent amounts of drift when either hand vision or target vision was occluded. In a second experiment, we observed movement drift even when no visual target was ever present, providing evidence that drift is not driven by a visual-proprioceptive discrepancy. The observed drift in both experiments was consistent with a model of passive error accumulation in which the amount of drift is determined by the precision of the sensory estimate of movement error.

Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement ("jump") consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation.

Adapting to target error without visual feedback

What information is necessary for the motor system to adapt its behaviour? Visual hand-to-target error provides salient information about reach performance, but can learning proceed without this information? We investigated adaptation to an unperceived target perturbation under visual open-loop conditions. Participants looked and reached, without any vision of their hand, to a target that jumped rightward at saccade onset (Perturbation condition) or remained stationary throughout the trial (Stationary condition). The target jump in the Perturbation condition was tied to the saccade, such that participants were unaware that it had occurred. Each type of exposure was followed by a posttest, in which participants reached to a target that disappeared at saccade onset. In the posttest, participants reached farther following exposure to the perturbation than they did following exposure to the stationary target, indicating that participants had learned from systematic exposure to the jump. These findings imply that online error induces motor learning, even when participants receive no visual information about their performance.