Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties...

We recently showed that subjects can learn motion state-dependent changes to motor output (temporal force patterns) based on explicit visual feedback of the equivalent force field (i.e., without the physical perturbation). Here, we examined the spatiotemporal properties of this learning compared with learning based on physical perturbations. There were two human subject groups and two experimental paradigms. One group (n = 40) experienced physical perturbations (i.e., a velocity-dependent force field, vFF), whereas the second (n = 40) was given explicit visual feedback (EVF) of the force-velocity relationship. In the latter, subjects moved in force channels and we provided visual feedback of the lateral force exerted during the movement, as well as the required force pattern based on movement velocity. In the first paradigm (spatial generalization), following vFF or EVF training, generalization of learning was tested by requiring subjects to move to 14 untrained target locations (0° to ±135° around the trained location). In the second paradigm (temporal stability), following training, we examined the decay of learning over eight delay periods (0 to 90 s). Results showed that learning based on EVF did not generalize to untrained directions, whereas the generalization for the vFF was significant for targets ≤ 45° away. In addition, the decay of learning for the EVF group was significantly faster than the FF group (a time constant of 2.72 ± 1.74 s vs. 12.53 ± 11.83 s). Collectively, our results suggest that recalibrating motor output based on explicit motion state information, in contrast to physical disturbances, uses learning mechanisms with limited spatiotemporal properties.NEW & NOTEWORTHY Adjustment of motor output based on limb motion state information can be achieved based on explicit information or from physical perturbations. Here, we investigated the spatiotemporal characteristics of short-term motor learning to determine the properties of the respective learning mechanisms. Our results suggest that adjustments based on physical perturbations are more temporally stable and applied over a greater spatial range than the learning based on explicit visual feedback, suggesting largely separate learning mechanisms.