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Key Points Trial-to-trial variability can result from both deterministic sources, such as complex dynamics or internal states, and randomness — that is, noise. This Review focuses on noise and its impact along the behavioural loop. Sensory noise is noise in sensory signals and sensory receptors. It limits the amount of information that is available to other areas of the CNS. Cellular noise is an underestimated contributor to neuronal variability. The stochastic nature of neuronal mechanisms becomes critical in the many small structures of the CNS. Electrical noise in neurons, especially channel noise from voltage-gated ion channels, limits neuronal reliability and cell size, producing millisecond variability in action-potential initiation and propagation. Synaptic noise results from the noisy biochemical processes that underlie synaptic transmission. Adding up these noise sources can account for the observed postsynaptic-response variability. Noise build-up in neural networks can be contained by appropriate network layouts, homeostatic mechanisms and the threshold-like nature of neurons. Motor noise results when neural signals are converted into forces. The architecture of motor neurons and their muscle fibres makes the conversion noisy. The brain organizes movements to minimize the effects of motor noise on movement variability. Beneficial effects of noise include stochastic resonance in specific cases of sensory processing and forcing neural networks to be more robust and explore more states. Behavioural variability, as observed in sensory estimation and movement tasks, appears to be mainly produced by noise. The principle of averaging is one of two fundamental principles applied by the CNS to compensate for noise by summing over sources of redundant information. The principle of prior knowledge is the other fundamental principle: it exploits the expected nature of signals and noise. The CNS often applies it in combination with averaging, such as in Bayesian cue combination in sensory processing. Noise contributes significantly to neuronal and behavioural trial-to-trial variability. Faisal and colleagues discuss the sources and effects of noise in the nervous system as well as the principles used to counter its detrimental effects. Noise — random disturbances of signals — poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.

Multisensory integration has primarily been studied in static environments, where optimal integration relies on the precision of the respective sensory modalities. However, in numerous situations, sensory information is dynamic and changes over time, due to changes in our bodily state and the surrounding environment. Given that different sensory modalities have different delays, this suggests that optimal integration may not solely depend on sensory precision but may also be affected by the delays associated with each sensory system. To investigate this hypothesis, participants (n = 22, 16 female) engaged in a continuous steering task. Participants sat on a motion platform facing a screen that displayed a cartoonish traffic scene, featuring a car traveling along a road. In the visuoinertial condition, where vestibular and somatosensory feedback were available, they were tasked with counteracting an external multi-frequency perturbation signal, which laterally perturbed the platform and the car, such that the car was kept within the center of the road. In the visual condition, the visual car was perturbed, while the motion platform remained stationary. We show that participants compensate better for the perturbation in the visuoinertial than the visual condition, particularly in the high frequency range of the perturbation. Using computational modelling, we demonstrate that this enhanced performance is partially due to the shorter delay of the vestibular modality. In this condition, participants rely more on the vestibular information, which is less delayed than the more precise but longer delayed, visual information.

When we reach for an object during a passive whole body rotation, a tangential Coriolis force is generated on the arm. Yet, within a few trials, the brain adapts to this force so it does not disrupt the reach. Is this adaptation governed by a single-rate or dual-rate learning process? Here, guided by state-space modeling, we studied human reach adaptation in a fully-enclosed rotating room. After 90 pre-rotation reaches (baseline), participants were trained to make 240 to-and-fro reaches while the room rotated at 10 rpm (block A), then performed 6 reaches under opposite room rotation (block B), and subsequently made 100 post-rotation reaches (washout). A control group performed the same paradigm, but without the reaches during rotation block B. Single-rate and dual-rate models can be best dissociated if there would be full un-learning of compensation A during block B, but minimal learning of B. From the perspective of a dual-rate model, the un-learning observed in block B would mainly be caused by the faster state, such that the washout reaches would show retention effects of the slower state, called spontaneous recovery. Alternatively, following a single-rate model, the same state would govern the learning in block A and un-learning in block B, such that the washout reaches mimic the baseline reaches. Our results do not provide clear signs of spontaneous recovery in the washout reaches. Model fits further show that a single-rate process outperformed a dual-rate process. We suggest that a single-rate process underlies Coriolis force reach adaptation, perhaps because these forces relate to familiar body dynamics and are assigned to an internal cause.

When navigating through the environment, our brain needs to infer how far we move and in which direction we are heading. In this estimation process, the brain may rely on multiple sensory modalities, including the visual and vestibular systems. Previous research has mainly focused on heading estimation, showing that sensory cues are combined by weighting them in proportion to their reliability, consistent with statistically optimal integration. But while heading estimation could improve with the ongoing motion, due to the constant flow of information, the estimate of how far we move requires the integration of sensory information across the whole displacement. In this study, we investigate whether the brain optimally combines visual and vestibular information during a displacement estimation task, even if their reliability varies from trial to trial. Participants were seated on a linear sled, immersed in a stereoscopic virtual reality environment. They were subjected to a passive linear motion involving visual and vestibular cues with different levels of visual coherence to change relative cue reliability and with cue discrepancies to test relative cue weighting. Participants performed a two-interval two-alternative forced-choice task, indicating which of two sequentially perceived displacements was larger. Our results show that humans adapt their weighting of visual and vestibular information from trial to trial in proportion to their reliability. These results provide evidence that humans optimally integrate visual and vestibular information in order to estimate their body displacement.

Many sensorimotor activities have a time constraint for successful completion. In this case, any time devoted to sensory processing is at the expense of time available for motor execution. Earlier studies have explored how this competition between sensory processing and motor execution is resolved by using experimental designs that segregate the sensing and acting phase of the task. It was found that participants switch from the sensing to the acting stage such that the overall (sensorimotor) uncertainty in the outcome of the task is minimized. An unexplained observation in these studies is the substantial variability in switching times. We investigated the variability in switching time by correlating it with the underlying sensorimotor uncertainty. To this end, we used a modified version of the virtual ball catching paradigm proposed by Faisal & Wolpert (2009). Subjects were instructed to catch a ball, but as soon as they initiated their movement the ball disappeared. We extended the range of horizontal velocities and used two different start positions of the ball to quantify the dependence of sensory uncertainty on ball velocity. Velocity dependence of sensory uncertainty allowed us to manipulate sensory uncertainty and hence the sensorimotor uncertainty. We found that the variability in switching times is correlated with two factors. Firstly, variability in switching times is greater when variation in switching time has only minimal effects on sensorimotor uncertainty. Secondly, variability in switching times is greater when the sensorimotor uncertainty is larger. Our results suggest that the variability in switching time reflects an uncertainty-driven exploratory process.

Evidence accumulation processes during decision‐making are thought to continuously feed into the motor system, preparing multiple competing motor plans, of which one is executed when the evidence is complete. Previously, the state of this accumulation process has been studied by reading out the preparatory state of the motor system with evoked responses, once per trial. In this study, we aim to continuously track the sensorimotor decision during the trial using corticomuscular (CMC) and intermuscular coherence (IMC). We recorded EEG and EMG of healthy young adults (n = 34) who viewed random dot motion stimuli, with varying strengths across trials, and indicated their perceived motion direction by reaching towards one of two targets, requiring either flexion or extension of the elbow. Coherence was computed in the beta band. After stimulus presentation, both CMC and IMC show an initial phasic pattern, which is followed by sustained coherence patterns at a level that depends on stimulus strength for CMC. Prior to reach onset, the CMC for different stimulus strengths had a tendency to settle at similar levels. This tendency tentatively marks a stimulus‐independent decision bound. We conclude that CMC, and to a lesser extent IMC, track the evidence accumulation process on a single trial.

While slowness of movement is an obligatory characteristic of Parkinson’s disease (PD), there are conditions in which patients move uncharacteristically fast, attributed to deficient motor inhibition. Here we investigate deficient inhibition in an optimal sensory-motor integration framework, using a game in which subjects used a paddle to catch a virtual ball. Display of the ball was extinguished as soon as the catching movement started, segregating the task into a sensing and acting phase. We analyzed the behavior of 9 PD patients (ON medication) and 10 age-matched controls (HC). The switching times (between sensing and acting phase) were compared to the predicted optimal switching time, based on the individual estimates of sensory and motor uncertainties. The comparison showed that deviation from predicted optimal switching times were similar between groups. However, PD patients showed a weaker correlation between variability in switching time and sensory-motor uncertainty, indicating a reduced propensity to generate exploratory behavior for optimizing goal-directed movements. Analysis of the movement kinematics revealed that PD patients, compared to controls, used a lower peak velocity of the paddle and intercepted the ball with greater velocity. Adjusting the trial duration to the time for the paddle to stop moving, we found that PD patients spent a smaller proportion of the trial duration for observing the ball. Altogether, the results do not show the premature movement initiation and truncated sensory processing that we predicted to ensue from deficient inhibition in PD.

The human movement repertoire is characterized by the smooth coordination of several body parts, including arm movements and whole body motion. The neural control of this coordination is quite complex because the various body parts have their own kinematic and dynamic properties. Behavioral inferences about the neural solution to the coordination problem could be obtained by examining the emerging phase relationship and its stability. Here, we studied the phase relationships that characterize the coordination of arm-reaching movements with passively-induced whole-body motion. Participants were laterally translated using a vestibular chair that oscillated at a fixed frequency of 0.83 Hz. They were instructed to reach between two targets that were aligned either parallel or orthogonal to the whole body motion. During the first cycles of body motion, a metronome entrained either an in-phase or an anti-phase relationship between hand and body motion, which was released at later cycles to test phase stability. Results suggest that inertial forces play an important role when coordinating reaches with cyclic whole-body motion. For parallel reaches, we found a stable in-phase and an unstable anti-phase relationship. When the latter was imposed, it readily transitioned or drifted back toward an in-phase relationship at cycles without metronomic entrainment. For orthogonal reaches, we did not find a clear difference in stability between in-phase and anti-phase relationships. Computer simulations further show that cost models that minimize energy expenditure (i.e. net torques) or endpoint variance of the reach cannot fully explain the observed coordination patterns. We discuss how predictive control and impedance control processes could be considered important mechanisms underlying the rhythmic coordination of arm reaches and body motion.

Within predictive processing two kinds of learning can be distinguished: parameter learning and structure learning. In Bayesian parameter learning, parameters under a specific generative model are continuously being updated in light of new evidence. However, this learning mechanism cannot explain how new parameters are added to a model. Structure learning, unlike parameter learning, makes structural changes to a generative model by altering its causal connections or adding or removing parameters. Whilst these two types of learning have recently been formally differentiated, they have not been empirically distinguished. The aim of this research was to empirically differentiate between parameter learning and structure learning on the basis of how they affect pupil dilation. Participants took part in a within-subject computer-based learning experiment with two phases. In the first phase, participants had to learn the relationship between cues and target stimuli. In the second phase, they had to learn a conditional change in this relationship. Our results show that the learning dynamics were indeed qualitatively different between the two experimental phases, but in the opposite direction as we originally expected. Participants were learning more gradually in the second phase compared to the first phase. This might imply that participants built multiple models from scratch in the first phase (structure learning) before settling on one of these models. In the second phase, participants possibly just needed to update the probability distribution over the model parameters (parameter learning).

The neuromuscular system is inherently noisy and joint impedance may serve to filter this noise. In the present experiment, we investigated whether individuals modulate joint impedance to meet spatial accuracy demands. Twelve subjects were instructed to make rapid, time constrained, elbow extensions to three differently sized targets. Some trials (20 out of 140 for each target, randomly assigned) were perturbed mechanically at 75% of movement amplitude. Inertia, damping and stiffness were estimated from the torque and angle deviation signal using a forward simulation and optimization routine. Increases in endpoint accuracy were not always reflected in a decrease in trajectory variability. Only in the final quarter of the trajectory the variability decreased as target width decreased. Stiffness estimates increased significantly with accuracy constraints. Damping estimates only increased for perturbations that were initially directed against the movement direction. We concluded that joint impedance modulation is one of the strategies used by the neuromuscular system to generate accurate movements, at least during the final part of the movement.