Movement adaptation in response to systematic motor perturbations exhibits distinct spatial and temporal properties. These characteristics are typically studied in isolation, leaving the interaction largely unknown. Here we examined how the temporal decay of visuomotor adaptation influences the spatial generalization of the motor recalibration. First, we quantified the extent to which adaptation decayed over time. Subjects reached to a peripheral target, and a rotation was applied to the visual feedback of the unseen motion. The retention of this adaptation over different delays (0-120 s) ) decreased by 29.0 ± 6.8% at the longest delay and) was represented by a simple exponential, with a time constant of 22.5 ± 5.6 s. On the basis of this relationship we simulated how the spatial generalization of adaptation would change with delay. To test this directly, we trained additional subjects with the same perturbation and assessed transfer to 19 different locations (spaced 15° apart, symmetric around the trained location) and examined three delays (~4, 12, and 25 s). Consistent with the simulation, we found that generalization around the trained direction (±15°) significantly decreased with delay and distance, while locations >60° displayed near-constant spatiotemporal transfer. Intermediate distances (30° and 45°) showed a difference in transfer across space, but this amount was approximately constant across time. Interestingly, the decay at the trained direction was faster than that based purely on time, suggesting that the spatial transfer of adaptation is modified by concurrent passive (time dependent) and active (movement dependent) processes. Short-term motor adaptation exhibits distinct spatial and temporal characteristics. Here we investigated the interaction of these features, utilizing a simple motor adaptation paradigm (recalibration of reaching arm movements in response to rotated visual feedback). We examined the changes in the spatial generalization of motor adaptation for different temporal manipulations and report that the spatiotemporal generalization of motor adaptation is generally local and is influenced by both passive (time dependent) and active (movement dependent) learning processes.

A critical aspect of behavior is that mobile organisms must be able to precisely determine where and when to move. A better understanding of the mechanisms underlying precise movement timing and action planning is therefore crucial to understanding how we interact with the world around us. Recent evidence suggests that our experience of time is directly and intrinsically computed within the motor system, consistent with the theory of embodied cognition. To investigate the role of the motor system, we tested human subjects ( n = 40) on a novel task combining reaching and time estimation. In this task, subjects were required to move a robotic manipulandum to one of two physical locations to categorize a concurrently timed suprasecond. Critically, subjects were divided into two groups: one in which movement during the interval was unrestricted and one in which they were restricted from moving until the stimulus interval had elapsed. Our results revealed a higher degree of precision for subjects in the free-moving group. A further experiment ( n = 14) verified that these findings were not due to proximity to the target, counting strategies, bias, or movement length. A final experiment ( n = 10) replicated these findings using a within-subjects design, performing a time reproduction task, in which movement during encoding of the interval led to more precise performance. Our findings suggest that time estimation may be instantiated within the motor system as an ongoing readout of timing judgment and confidence.

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Humans rapidly adapt reaching movements in response to perturbations (e.g., manipulations of movement dynamics or visual feedback). Following a break, when reexposed to the same perturbation, subjects demonstrate savings, a faster learning rate compared with the time course of initial training. Although this has been well studied, there are open questions on the extent early savings reflects the rapid recall of previous performance. To address this question, we examined how the properties of initial training (duration and final adaptive state) influence initial single-trial adaptation to force-field perturbations when training sessions were separated by 24 h. There were two main groups that were distinct based on the presence or absence of a washout period at the end of day 1 (with washout vs. without washout). We also varied the training duration on day 1 (15, 30, 90, or 160 training trials), resulting in 8 subgroups of subjects. We show that single-trial adaptation on day 2 scaled with training duration, even for similar asymptotic levels of learning on day 1 of training. Interestingly, the temporal force profile following the first perturbation on day 2 matched that at the end of day 1 for the longest training duration group that did not complete the washout. This correspondence persisted but was significantly lower for shorter training durations and the washout subject groups. Collectively, the results suggest that the adaptation observed very early in reexposure results from the rapid recall of the previously learned motor recalibration but is highly dependent on the initial training duration and final adaptive state. NEW & NOTEWORTHY The extent initial readaptation reflects the recall of previous motor performance is largely unknown. We examined early single-trial force-field adaptation on the second day of training and distinguished initial retention from recall. We found that the single-trial adaptation following the 24-h break matched that at the end of the first day, but this recall was modified by the training duration and final level of learning on the first day of training.

Interval timing is a fundamental component of action, and is susceptible to motor-related temporal distortions. Previous studies have shown that concurrent movement biases temporal estimates, but have primarily considered self-modulated movement only. However, real-world encounters often include situations in which movement is restricted or perturbed by environmental factors. In the following experiments, we introduced viscous movement environments to externally modulate movement and investigated the resulting effects on temporal perception. In two separate tasks, participants timed auditory intervals while moving a robotic arm that randomly applied four levels of viscosity. Results demonstrated that higher viscosity led to shorter perceived durations. Using a drift-diffusion model and a Bayesian observer model, we confirmed these biasing effects arose from perceptual mechanisms, instead of biases in decision making. These findings suggest that environmental perturbations are an important factor in movement-related temporal distortions, and enhance the current understanding of the interactions of motor activity and cognitive processes.

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Granger analysis (GA) is widely used to construct directed brain networks based on various physiological recordings, such as functional magnetic resonance imaging, and electroencephalogram (EEG). However, in real applications, EEGs are inevitably contaminated by unexpected artifacts that may distort the networks because of the L2 norm structure utilized in GAs when estimating directed links. Compared with the L2 norm, the Lp ( ) norm can compress outlier effects. In this paper, an extended GA is constructed by applying the Lp ( ) norm strategy to estimate robust causalities under outlier conditions, and a feasible iteration procedure is utilized to solve the new GA model. A quantitative evaluation using a predefined simulation network demonstrates smaller bias errors and higher linkage consistence for the Lp ( , 0.8, 0.6, 0.4, 0.2) -GAs compared with both the Lasso- and L2-GAs under various simulated outlier conditions. Applications in resting-state EEGs that contain ocular artifacts also show that the proposed GA can effectively compress the ocular outlier influence and recover the reliable networks. The proposed Lp-GA may be helpful in capturing the reliable network structure when EEGs are contaminated with artifacts in related studies.

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