Dissociation between Temporal and Spatial Anticipation in the Neural Dynamics of Goal-directed Movement Preparation.

It is well documented that providing advanced information regarding the spatial location of a target stimulus (i.e., spatial anticipation) or its timing of occurrence (i.e., temporal anticipation) influences reach preparation, reducing RTs. Yet, it remains unknown whether the RT gains attributable to temporal and spatial anticipation are subtended by similar preparatory dynamics. Here, this issue is addressed in humans by investigating EEG beta-band activity during reach preparation. Participants performed a reach RT task in which they initiated a movement as fast as possible toward visual targets following their appearance. Temporal anticipation was manipulated by having the target appear after a constant or variable delay period, whereas spatial anticipation was manipulated by precueing participants about the upcoming target location in advance or not. Results revealed that temporal and spatial anticipation both reduced reach RTs, with no interaction. Interestingly, temporal and spatial anticipation were associated with fundamentally different patterns of beta-band modulations. Temporal anticipation was associated with beta-band desynchronization over contralateral sensorimotor regions specifically around the expected moment of target onset, the magnitude of which was correlated with RT modulations across participants. In contrast, spatial anticipation did not influence sensorimotor activity but rather led to increased beta-band power over bilateral parieto-occipital regions during the entire delay period. These results argue for distinct states of preparation incurred by temporal and spatial anticipation. In particular, sensorimotor beta-band desynchronization may reflect the timely disinhibition of movement-related neuronal ensembles at the expected time of movement initiation, without reflecting its spatial parameters per se.

Variance in exposed perturbations impairs retention of visuomotor adaptation.

Sensorimotor control requires an accurate estimate of the state of the body. The brain optimizes state estimation by combining sensory signals with predictions of the sensory consequences of motor commands using a forward model. Given that both sensory signals and predictions are uncertain (i.e., noisy), the brain optimally weights the relative reliance on each source of information during adaptation. In support, it is known that uncertainty in the sensory predictions influences the rate and generalization of visuomotor adaptation. We investigated whether uncertainty in the sensory predictions affects the retention of a new visuomotor relationship. This was done by exposing three separate groups to a visuomotor rotation whose mean was common at 15° counterclockwise but whose variance around the mean differed (i.e., SD of 0°, 3.2°, or 4.5°). Retention was assessed by measuring the persistence of the adapted behavior in a no-vision phase. Results revealed that mean reach direction late in adaptation was similar across groups, suggesting it depended mainly on the mean of exposed rotations and was robust to differences in variance. However, retention differed across groups, with higher levels of variance being associated with a more rapid reversion toward nonadapted behavior. A control experiment ruled out the possibility that differences in retention were accounted for by differences in success rates. Exposure to variable rotations may have increased the uncertainty in sensory predictions, making the adapted forward model more labile and susceptible to change or decay.NEW & NOTEWORTHY The brain predicts the sensory consequences of motor commands through a forward model. These predictions are subject to uncertainty. We use visuomotor adaptation and modulate uncertainty in the sensory predictions by manipulating the variance in exposed rotations. Results reveal that variance does not influence the final extent of adaptation but selectively impairs the retention of motor memories. These results suggest that a more uncertain forward model is more susceptible to change or decay.