Interacting adaptive processes with different timescales underlie short-term motor learning

The role of explicit knowledge in compensating for a visuo-proprioceptive cue conflict

It is unclear how explicit knowledge of an externally imposed mismatch between visual and proprioceptive cues of hand position affects perceptual recalibration. The Bayesian causal inference framework might suggest such knowledge should abolish the visual and proprioceptive recalibration that occurs when individuals perceive these cues as coming from the same source (their hand), while the visuomotor adaptation literature suggests explicit knowledge of a cue conflict does not eliminate implicit compensatory processes. Here we compared visual and proprioceptive recalibration in three groups with varying levels of knowledge about the visuo-proprioceptive cue conflict. All participants estimated the position of visual, proprioceptive, or combined targets related to their left index fingertip, with a 70 mm visuo-proprioceptive offset gradually imposed. Groups 1, 2, and 3 received no information, medium information, and high information, respectively, about the offset. Information was manipulated using instructional and visual cues. All groups performed the task similarly at baseline in terms of variance, weighting, and integration. Results suggest the three groups recalibrated vision and proprioception differently, but there was no difference in variance or weighting. Participants who received only instructional cues about the mismatch (Group 2) did not recalibrate less, on average, than participants provided no information about the mismatch (Group 1). However, participants provided instructional cues and extra visual cues of their hands during the perturbation (Group 3) demonstrated significantly less recalibration than other groups. These findings are consistent with the idea that instructional cues alone are insufficient to override participants' intrinsic belief in common cause and reduce recalibration.

Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval

Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.

Comparison of adaptation characteristics between visually and memory-guided saccades

Saccade adaptation plays a crucial role in maintaining saccade accuracy. The behavioral characteristics and neural mechanisms of saccade adaptation for an externally cued movement, such as visually guided saccades (VGS), are well studied in nonhuman primates. In contrast, little is known about the saccade adaptation of an internally driven movement, such as memory-guided saccades (MGS), which are guided by visuospatial working memory. As the oculomotor plant changes because of growth, aging, or skeletomuscular problems, both types of saccades need to be adapted. Do both saccade types engage a common adaptation mechanism? In this study, we compared the characteristics of amplitude decrease adaptation in MGS with VGS in nonhuman primates. We found that the adaptation speed was faster for MGS than for VGS. Saccade duration changed during MGS adaptation, whereas saccade peak velocity changed during VGS adaptation. We also compared the adaptation field, that is, the gain change for saccade amplitudes other than the adapted. The gain change for MGS declines on both smaller and larger sides of adapted amplitude, more rapidly for larger than smaller amplitudes, whereas the decline in VGS was reversed. Thus, the differences between VGS and MGS adaptation characteristics support the previously suggested hypothesis that the adaptation mechanisms of VGS and MGS are distinct. Furthermore, the result suggests that the MGS adaptation site is a brain structure that influences saccade duration, whereas the VGS adaptation site influences saccade peak velocity. These results should be beneficial for future neurophysiological experiments.NEW & NOTEWORTHY Plasticity helps to overcome persistent motor errors. Such motor plasticity or adaptation can be investigated with saccades. Thus far our knowledge is primarily about visually guided saccades, an externally cued movement, which we can make only when the object is visible at the time of saccade. However, as the world is complex, we can make saccades even when the object is not visible. Here, we investigate the adaptation of an internally driven movement: the memory-guided saccade.

Automatic learning mechanisms for flexible human locomotion

Movement flexibility and automaticity are necessary to successfully navigate different environments. When encountering difficult terrains such as a muddy trail, we can change how we step almost immediately so that we can continue walking. This flexibility comes at a cost since we initially must pay deliberate attention to how we are moving. Gradually, after a few minutes on the trail, stepping becomes automatic so that we do not need to think about our movements. Canonical theory indicates that different adaptive motor learning mechanisms confer these essential properties to movement: explicit control confers rapid flexibility, while forward model recalibration confers automaticity. Here we uncover a distinct mechanism of treadmill walking adaptation - an automatic stimulus-response mapping - that confers both properties to movement. The mechanism is flexible as it learns stepping patterns that can be rapidly changed to suit a range of treadmill configurations. It is also automatic as it can operate without deliberate control or explicit awareness by the participants. Our findings reveal a tandem architecture of forward model recalibration and automatic stimulus-response mapping mechanisms for walking, reconciling different findings of motor adaptation and perceptual realignment.

Sensorimotor adaptation impedes perturbation detection in grasping

Humans achieve skilled actions by continuously correcting for motor errors or perceptual misjudgments, a process called sensorimotor adaptation. This can occur with the actor both detecting (explicitly) and not detecting the error (implicitly). We investigated how the magnitude of a perturbation and the corresponding error signal each contribute to the detection of a size perturbation during interaction with real-world objects. Participants grasped cuboids of different lengths in a mirror-setup allowing us to present different sizes for seen and felt cuboids, respectively. Visuo-haptic size mismatches (perturbations) were introduced either abruptly or followed a sinusoidal schedule. These schedules dissociated the error signal from the visuo-haptic mismatch: Participants could fully adapt their grip and reduce the error when a perturbation was introduced abruptly and then stayed the same, but not with a constantly changing sinusoidal perturbation. We compared participants' performance in a two-alternative forced choice (2AFC) task where participants judged these mismatches, and modelled error-correction in grasping movements by looking at changes in maximum grip apertures, measured using motion tracking. We found similar mismatch-detection performance with sinusoidal perturbation schedules and the first trial after an abrupt change, but decreasing performance over further trials for the latter. This is consistent with the idea that reduced error signals following adaptation make it harder to detect perturbations. Error-correction parameters indicated stronger error-correction in abruptly introduced perturbations. However, we saw no correlation between error-correction and overall mismatch-detection performance. This emphasizes the distinct contributions of the perturbation magnitude and the error signal in helping participants detect sensory perturbations.

Explicit Instruction May Impair the Transfer of Motor Adaptation in an Upper Extremity Motor Task

This study focused on explicit instruction and evaluated the differences in task performance between participants who were instructed to employ the change and those who were not. Ninety-three healthy young adults were assigned to the accurate information group (AG; n = 31), misinformation group (MG; n = 31), and non-information group (NG; n = 31). All participants manipulated a mouse to track a moving target on a screen with a cursor. The cursor was rotated to 60° in the clockwise direction from the actual mouse position during the 1st to 5th blocks (i.e., motor adaptation task). Subsequently, in the 6th block (i.e., transfer task), we gradually changed the angle of rotation from 60° to 80° to prevent from noticing the change. Participants in the AG were instructed accurate experimental information. Participants in the MG were instructed that the angle of rotation was 60° during the 1st to 6th blocks. Participants in the NG were instructed to manipulate the cursor movement only. The results indicated that an average error distance in the AG was significantly lower than that in the NG in the 6th block. This study suggested that explicit instruction may impair the transfer of motor adaptation in this setting.

Structural Learning Benefits in a Visuomotor Adaptation Task Generalize to a Contralateral Effector

Structural learning is characterized by facilitated adaptation following training on a set of sensory perturbations all belonging to the same structure (e.g., 'visuomotor rotations'). This generalization of learning is a core feature of the motor system and is often studied in the context of interlimb transfer. However, such transfer has only been demonstrated when participants learn to counter a specific perturbation in the sensory feedback of their movements; we determined whether structural learning in one limb generalized to the contralateral limb. We trained 13 participants to counter random visual feedback rotations between +/-90 degrees with the right hand and subsequently tested the left hand on a fixed rotation. The structural training group showed faster adaptation in the left hand in both feedforward and feedback components of reaching compared to 13 participants who trained with veridical reaching, with lower initial reaching error, and straighter, faster, and smoother movements than in the control group. The transfer was ephemeral - benefits were confined to roughly the first 20 trials. The results demonstrate that the motor system can extract invariant properties of seemingly random environments in one limb, and that this information can be accessed by the contralateral limb.

Perceptual error based on Bayesian cue combination drives implicit motor adaptation

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating one's effector, supported by Bayesian cue integration, underpins the sensorimotor system's implicit adaptation.

Goal-directed vocal planning in a songbird

Songbirds' vocal mastery is impressive, but to what extent is it a result of practice? Can they, based on experienced mismatch with a known target, plan the necessary changes to recover the target in a practice-free manner without intermittently singing? In adult zebra finches, we drive the pitch of a song syllable away from its stable (baseline) variant acquired from a tutor, then we withdraw reinforcement and subsequently deprive them of singing experience by muting or deafening. In this deprived state, birds do not recover their baseline song. However, they revert their songs toward the target by about 1 standard deviation of their recent practice, provided the sensory feedback during the latter signaled a pitch mismatch with the target. Thus, targeted vocal plasticity does not require immediate sensory experience, showing that zebra finches are capable of goal-directed vocal planning.

Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke

Motor learning involves both explicit and implicit processes that are fundamental for acquiring and adapting complex motor skills. However, stroke may damage the neural substrates underlying explicit and/or implicit learning, leading to deficits in overall motor performance. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke when tested during a task that elicits dissociable contributions from both. Here, we compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning (i.e., strategy-use) using visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. The removal of visual feedback after the first 40 strides of split-belt walking, combined with task instructions, minimized contributions from explicit learning for the remainder of the task. We utilized a multi-rate state-space model to characterize individual explicit and implicit process contributions to overall behavioral change. The computational and behavioral analyses revealed that, compared to controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning, a result that runs counter to prior work testing each process individually during gait. Since post-stroke locomotor rehabilitation involves interventions that rely on both explicit and implicit motor learning, future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning.

Reward actively engages both implicit and explicit components in dual force field adaptation

Motor learning occurs through multiple mechanisms, including unsupervised, supervised (error based), and reinforcement (reward based) learning. Although studies have shown that reward leads to an overall better motor adaptation, the specific processes by which reward influences adaptation are still unclear. Here, we examine how the presence of reward affects dual adaptation to novel dynamics and distinguish its influence on implicit and explicit learning. Participants adapted to two opposing force fields in an adaptation/deadaptation/error-clamp paradigm, where five levels of reward (a score and a digital face) were provided as participants reduced their lateral error. Both reward and control (no reward provided) groups simultaneously adapted to both opposing force fields, exhibiting a similar final level of adaptation, which was primarily implicit. Triple-rate models fit to the adaptation process found higher learning rates in the fast and slow processes and a slightly increased fast retention rate for the reward group. Whereas differences in the slow learning rate were only driven by implicit learning, the large difference in the fast learning rate was mainly explicit. Overall, we confirm previous work showing that reward increases learning rates, extending this to dual-adaptation experiments and demonstrating that reward influences both implicit and explicit adaptation. Specifically, we show that reward acts primarily explicitly on the fast learning rate and implicitly on the slow learning rates.NEW & NOTEWORTHY Here we show that rewarding participants' performance during dual force field adaptation primarily affects the initial rate of learning and the early timescales of adaptation, with little effect on the final adaptation level. However, reward affects both explicit and implicit components of adaptation. Whereas the learning rate of the slow process is increased implicitly, the fast learning and retention rates are increased through both implicit components and the use of explicit strategies.

Modeling Neuromotor Adaptation to Pulsed Torque Assistance During Walking

Multiple mechanisms of motor learning contribute to the response of individuals to robot-aided gait training, including error-based learning and use-dependent learning. Previous models described either of these mechanisms, but not both, and their relevance to gait training is unknown. In this paper, we establish the validity of existing models to describe the response of healthy individuals to robot-aided training of propulsion via a robotic exoskeleton, and propose a new model that accounts for both use-dependent and error-based learning. We formulated five state-space models to describe the stride-by-stride evolution of metrics of propulsion mechanics during and after robot-assisted training, applied by a hip/knee robotic exoskeleton for 200 consecutive strides. The five models included a single-state, a two-state, a two-state fast and slow, a use-dependent learning (UDL), and a newly-developed modified UDL model, requiring 4, 9, 5, 3, and 4 parameters, respectively. The coefficient of determination (R 2) and Akaike information criterion (AIC) values were calculated to quantify the goodness of fit of each model. Model fit was conducted both at the group and at the individual participant level. At the group level, the modified UDL model shows the best goodness-of-fit compared to other models in AIC values in 15/16 conditions. At the participant level, both the modified UDL model and the two-state model have significantly better goodness-of-fit compared to the other models. In summary, the modified UDL model is a simple 4-parameter model that achieves similar goodness-of-fit compared to a two-state model requiring 9 parameters. As such, the modified UDL model is a promising model to describe the effects of robot-aided gait training on propulsion mechanics.

Dissociating the Influence of Limb Posture and Visual Feedback Shifts on the Adaptation to Novel Movement Dynamics

Accurate movements of the upper limb require the integration of various forms of sensory feedback (e.g., visual and postural information). The influence of these different sensory modalities on reaching movements has been largely studied by assessing endpoint errors after selectively perturbing sensory estimates of hand location. These studies have demonstrated that both vision and proprioception make key contributions in determining the reach endpoint. However, their influence on motor output throughout movement remains unclear. Here we used separate perturbations of posture and visual information to dissociate their effects on reaching dynamics and temporal force profiles during point-to-point reaching movements. We tested human subjects (N = 32) and found that vision and posture modulate select aspects of reaching dynamics. Specifically, altering arm posture influences the relationship between temporal force patterns and the motion-state variables of hand position and acceleration, whereas dissociating visual feedback influences the relationship between force patterns and the motion-state variables of velocity and acceleration. Next, we examined the extent these baseline motion-state relationships influence motor adaptation based on perturbations of movement dynamics. We trained subjects using a velocity-dependent force-field to probe the extent arm posture-dependent influences persisted after exposure to a motion-state dependent perturbation. Changes in the temporal force profiles due to variations in arm posture were not reduced by adaptation to novel movement dynamics, but persisted throughout learning. These results suggest that vision and posture differentially influence the internal estimation of limb state throughout movement and play distinct roles in forming the response to external perturbations during movement.

Perception of task duration affects metabolic cost during split-belt adaptation

Humans continuously adapt locomotor patterns. Whether metabolic cost reduction is the primary objective or a by-product of the observed biomechanical changes during adaptation is not known. The main goal of our study is to determine if perception of task duration affects the adaptation of locomotor patterns to reduce energetic cost during split-belt walking. We tested the hypothesis that individuals who believe they will sustain a locomotor adaptation task for a prolonged time will reduce metabolic cost by adapting toward a walking pattern associated with lower mechanical work. N=14 participants walked on a split-belt treadmill for 10 minutes with knowledge of task duration (group K), while N=15 participants performed the task under the assumption that they would walk for 30 minutes (group U). Both groups walked for 10 minutes with the belts moving at 1.5 and 0.5 m/s, followed by 6 minutes of walking with both belts at 1.0 m/s. We observed a significant main effect of Time (p<0.001, observed power 1.0) and the interaction of Time×Group (p=0.004, observed power 0.84) on metabolic cost. Participants in the U group had a metabolic cost that was 12% lower during adaptation compared to the K group, which did not reduce metabolic cost during adaptation. The metabolic cost reduction observed in group U was not associated with biomechanical changes during adaptation. Our results indicate that metabolic cost reduction has a primary role in tasks that need to be sustained for a prolonged time, and this reduction is not only related to biomechanical factors.

Decision uncertainty as a context for motor memory

The current view of perceptual decision-making suggests that once a decision is made, only a single motor programme associated with the decision is carried out, irrespective of the uncertainty involved in decision making. In contrast, we show that multiple motor programmes can be acquired on the basis of the preceding uncertainty of the decision, indicating that decision uncertainty functions as a contextual cue for motor memory. The actions learned after making certain (uncertain) decisions are only partially transferred to uncertain (certain) decisions. Participants were able to form distinct motor memories for the same movement on the basis of the preceding decision uncertainty. Crucially, this contextual effect generalizes to novel stimuli with matched uncertainty levels, demonstrating that decision uncertainty is itself a contextual cue. These findings broaden the understanding of contextual inference in motor memory, emphasizing that it extends beyond direct motor control cues to encompass the decision-making process.

A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouse's lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.

Ouvrai opens access to remote virtual reality studies of human behavioural neuroscience

Modern virtual reality (VR) devices record six-degree-of-freedom kinematic data with high spatial and temporal resolution and display high-resolution stereoscopic three-dimensional graphics. These capabilities make VR a powerful tool for many types of behavioural research, including studies of sensorimotor, perceptual and cognitive functions. Here we introduce Ouvrai, an open-source solution that facilitates the design and execution of remote VR studies, capitalizing on the surge in VR headset ownership. This tool allows researchers to develop sophisticated experiments using cutting-edge web technologies such as WebXR to enable browser-based VR, without compromising on experimental design. Ouvrai's features include easy installation, intuitive JavaScript templates, a component library managing front- and backend processes and a streamlined workflow. It integrates with Firebase, Prolific and Amazon Mechanical Turk and provides data processing utilities for analysis. Unlike other tools, Ouvrai remains free, with researchers managing their web hosting and cloud database via personal Firebase accounts. Ouvrai is not limited to VR studies; researchers can also develop and run desktop or touchscreen studies using the same streamlined workflow. Through three distinct motor learning experiments, we confirm Ouvrai's efficiency and viability for conducting remote VR studies.

Influence of error-augmentation on the dynamics of visuomotor skill acquisition: insights from proxy-process models

Our study addresses the critical question of how learners acquire skills without the constant crutch of feedback, using a specialized training approach with intermittent feedback. Despite recognized benefits in skill retention, the underlying mechanisms of intermittent feedback in motor control neuroscience remain elusive. Leveraging a previously published dataset from visuomotor learning experiments with intermittent feedback, we tested a wide range of proxy-process models that posit the presence of an inferred error signal even when an explicit sensory performance is not present. The model structures encompassed a spectrum from first-order to higher-order variants, incorporating both constant and error-dependent rates of change in error. Furthermore, these proxy-process models investigated the impact of error-augmentation (EA) training on visuomotor learning dynamics. Rigorous cross-validation consistently identified a second-order proxy-process model structure accurately predicting motor learning across subjects and learning tasks. Model parameters elucidated the varying influences of EA settings on the rates of change in error, inter-trial variability, and steady-state performance. We then introduced a dynamic-Proxy support Multi-Rate Motor Learning (dPxMRML) model, which shed light on EA's effects on the fast and slow learning dynamics. The dPxMRML model accurately predicted subjects' performance during and beyond training phases, highlighting EA settings conducive to long-term retention. This research yields crucial insights for personalized training program design, applicable in neuro-rehabilitation, sports, and performance training.NEW & NOTEWORTHY Breaking new ground in motor learning, our research unveils the intricacies of skill acquisition without continuous feedback. By using a specialized training approach with intermittent feedback, our study reveals the previously elusive mechanisms behind this process. The introduction of innovative proxy-process models, particularly the dynamic-Proxy support Multi-Rate Motor Learning (dPxMRML) model, brings a fresh perspective to understanding the impact of error-augmentation (EA) training on learning and retention of motor skills.

Early-Stage Alzheimer's Disease Affects Fast But Not Slow Adaptive Processes in Motor Learning

Alzheimer's disease (AD) is characterized by an initial decline in declarative memory, while nondeclarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of nondeclarative memory, but recent findings suggest that it involves both fast, declarative, and slow, nondeclarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from an error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.

Oculomotor behavior can be adjusted on the basis of artificial feedback signals indicating externally caused errors

Whether a saccade is accurate and has reached the target cannot be evaluated during its execution, but relies on post-saccadic feedback. If the eye has missed the target object, a secondary corrective saccade has to be made to align the fovea with the target. If a systematic post-saccadic error occurs, adaptive changes to the oculomotor behavior are made, such as shortening or lengthening the saccade amplitude. Systematic post-saccadic errors are typically attributed internally to erroneous motor commands. The corresponding adaptive changes to the motor command reduce the error and the need for secondary corrective saccades, and, in doing so, restore accuracy and efficiency. However, adaptive changes to the oculomotor behavior also occur if a change in saccade amplitude is beneficial for task performance, or if it is rewarded. Oculomotor learning thus is more complex than reducing a post-saccadic position error. In the current study, we used a novel oculomotor learning paradigm and investigated whether human participants are able to adapt their oculomotor behavior to improve task performance even when they attribute the error externally. The task was to indicate the intended target object among several objects to a simulated human-machine interface by making eye movements. The participants were informed that the system itself could make errors. The decoding process depended on a distorted landing point of the saccade, resulting in decoding errors. Two different types of visual feedback were added to the post-saccadic scene and we compared how participants used the different feedback types to adjust their oculomotor behavior to avoid errors. We found that task performance improved over time, regardless of the type of feedback. Thus, error feedback from the simulated human-machine interface was used for post-saccadic error evaluation. This indicates that 1) artificial visual feedback signals and 2) externally caused errors might drive adaptive changes to oculomotor behavior.

Neural Underpinnings of Learning in Dementia Populations: A Review of Motor Learning Studies Combined with Neuroimaging

The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.

Prediction error in implicit adaptation during visually- and memory-guided reaching tasks

Human movements are adjusted by motor adaptation in order to maintain their accuracy. There are two systems in motor adaptation, referred to as explicit or implicit adaptation. It has been suggested that the implicit adaptation is based on the prediction error and has been used in a number of motor adaptation studies. This study aimed to examine the effect of visual memory on prediction error in implicit visuomotor adaptation by comparing visually- and memory-guided reaching tasks. The visually-guided task is thought to be implicit learning based on prediction error, whereas the memory-guided task requires more cognitive processes. We observed the adaptation to visuomotor rotation feedback that is gradually rotated. We found that the adaptation and retention rates were higher in the visually-guided task than in the memory-guided task. Furthermore, the delta-band power obtained by electroencephalography (EEG) in the visually-guided task was increased immediately following the visual feedback, which indicates that the prediction error was larger in the visually-guided task. Our results show that the visuomotor adaptation is enhanced in the visually-guided task because the prediction error, which contributes update of the internal model, was more reliable than in the memory-guided task. Therefore, we suggest that the processing of the prediction error is affected by the task-type, which in turn affects the rate of the visuomotor adaptation.

A cerebellar population coding model for sensorimotor learning

The cerebellum is crucial for sensorimotor adaptation, using error information to keep the sensorimotor system well-calibrated. Here we introduce a population-coding model to explain how cerebellar-dependent learning is modulated by contextual variation. The model consists of a two-layer network, designed to capture activity in both the cerebellar cortex and deep cerebellar nuclei. A core feature of the model is that within each layer, the processing units are tuned to both movement direction and the direction of movement error. The model captures a large range of contextual effects including interference from prior learning and the influence of error uncertainty and volatility. While these effects have traditionally been taken to indicate meta learning or context-dependent memory within the adaptation system, our results show that they are emergent properties that arise from the population dynamics within the cerebellum. Our results provide a novel framework to understand how the nervous system responds to variable environments.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Training-related changes in neural beta oscillations associated with implicit and explicit motor sequence learning

Many motor actions we perform have a sequential nature while learning a motor sequence involves both implicit and explicit processes. In this work, we developed a task design where participants concurrently learn an implicit and an explicit motor sequence across five training sessions, with EEG recordings at sessions 1 and 5. This intra-subject approach allowed us to study training-induced behavioral and neural changes specific to the explicit and implicit components. Based on previous reports of beta power modulations in sensorimotor networks related to sequence learning, we focused our analysis on beta oscillations at motor-cortical sites. On a behavioral level, substantial performance gains were evident early in learning in the explicit condition, plus slower performance gains across training sessions in both explicit and implicit sequence learning. Consistent with the behavioral trends, we observed a training-related increase in beta power in both sequence learning conditions, while the explicit condition displayed stronger beta power suppression during early learning. The initially stronger beta suppression and subsequent increase in beta power specific to the explicit component, correlated with enhanced behavioral performance, possibly reflecting higher cortical excitability. Our study suggests an involvement of motor-cortical beta oscillations in the explicit component of motor sequence learning.

Assistive sensory-motor perturbations influence learned neural representations

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.

Curiosity: primate neural circuits for novelty and information seeking

For many years, neuroscientists have investigated the behavioural, computational and neurobiological mechanisms that support value-based decisions, revealing how humans and animals make choices to obtain rewards. However, many decisions are influenced by factors other than the value of physical rewards or second-order reinforcers (such as money). For instance, animals (including humans) frequently explore novel objects that have no intrinsic value solely because they are novel and they exhibit the desire to gain information to reduce their uncertainties about the future, even if this information cannot lead to reward or assist them in accomplishing upcoming tasks. In this Review, I discuss how circuits in the primate brain responsible for detecting, predicting and assessing novelty and uncertainty regulate behaviour and give rise to these behavioural components of curiosity. I also briefly discuss how curiosity-related behaviours arise during postnatal development and point out some important reasons for the persistence of curiosity across generations.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties compared with adaptation to physical perturbations

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.

Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.

Memory consolidation of sequence learning and dynamic adaptation during wakefulness

Motor learning involves acquiring new movement sequences and adapting motor commands to novel conditions. Labile motor memories, acquired through sequence learning and dynamic adaptation, undergo a consolidation process during wakefulness after initial training. This process stabilizes the new memories, leading to long-term memory formation. However, it remains unclear if the consolidation processes underlying sequence learning and dynamic adaptation are independent and if distinct neural regions underpin memory consolidation associated with sequence learning and dynamic adaptation. Here, we first demonstrated that the initially labile memories formed during sequence learning and dynamic adaptation were stabilized against interference through time-dependent consolidation processes occurring during wakefulness. Furthermore, we found that sequence learning memory was not disrupted when immediately followed by dynamic adaptation and vice versa, indicating distinct mechanisms for sequence learning and dynamic adaptation consolidation. Finally, by applying patterned transcranial magnetic stimulation to selectively disrupt the activity in the primary motor (M1) or sensory (S1) cortices immediately after sequence learning or dynamic adaptation, we found that sequence learning consolidation depended on M1 but not S1, while dynamic adaptation consolidation relied on S1 but not M1. For the first time in a single experimental framework, this study revealed distinct neural underpinnings for sequence learning and dynamic adaptation consolidation during wakefulness, with significant implications for motor skill enhancement and rehabilitation.

Active perceptual learning involves motor exploration and adaptation of predictive sensory integration

Our ability to perceive both externally generated and self-generated sensory stimuli can be enhanced through training, known as passive and active perceptual learning (APL). Here, we sought to explore the mechanisms underlying APL by using active haptic training (AHT), which has been demonstrated to enhance the somatosensory perception of a finger in a trained motor skill. In total 120 pianists participated in this study. First, AHT reorganized the muscular coordination during the piano keystroke. Second, AHT increased the relative reliance on afferent sensory information relative to predicted one, in contrast to no increment of overall perceptual sensitivity. Finally, AHT improved feedback movement control of keystrokes. These results suggest that APL involves active exploration and adaptation of predictive sensory integration, which underlies the co-enhancement of active perception and feedback control of movements of well-trained individuals.

Dorsomedial frontal cortex damage impairs error-based, but not reinforcement-based motor learning in humans

We adapt our movements to new and changing environments through multiple processes. Sensory error-based learning counteracts environmental perturbations that affect the sensory consequences of movements. Sensory errors also cause the upregulation of reflexes and muscle co-contraction. Reinforcement-based learning enhances the selection of movements that produce rewarding outcomes. Although some findings have identified dissociable neural substrates of sensory error- and reinforcement-based learning, correlative methods have implicated dorsomedial frontal cortex in both. Here, we tested the causal contributions of dorsomedial frontal to adaptive motor control, studying people with chronic damage to this region. Seven human participants with focal brain lesions affecting the dorsomedial frontal and 20 controls performed a battery of arm movement tasks. Three experiments tested: (i) the upregulation of visuomotor reflexes and muscle co-contraction in response to unpredictable mechanical perturbations, (ii) sensory error-based learning in which participants learned to compensate predictively for mechanical force-field perturbations, and (iii) reinforcement-based motor learning based on binary feedback in the absence of sensory error feedback. Participants with dorsomedial frontal damage were impaired in the early stages of force field adaptation, but performed similarly to controls in all other measures. These results provide evidence for a specific and selective causal role for the dorsomedial frontal in sensory error-based learning.

Multisensory Calibration: A Variety of Slow and Fast Brain Processes Throughout the Lifespan

From before we are born, throughout development, adulthood, and aging, we are immersed in a multisensory world. At each of these stages, our sensory cues are constantly changing, due to body, brain, and environmental changes. While integration of information from our different sensory cues improves precision, this only improves accuracy if the underlying cues are unbiased. Thus, multisensory calibration is a vital and ongoing process. To meet this grand challenge, our brains have evolved a variety of mechanisms. First, in response to a systematic discrepancy between sensory cues (without external feedback) the cues calibrate one another (unsupervised calibration). Second, multisensory function is calibrated to external feedback (supervised calibration). These two mechanisms superimpose. While the former likely reflects a lower level mechanism, the latter likely reflects a higher level cognitive mechanism. Indeed, neural correlates of supervised multisensory calibration in monkeys were found in higher level multisensory cortical area VIP, but not in the relatively lower level multisensory area MSTd. In addition, even without a cue discrepancy (e.g., when experiencing stimuli from different sensory cues in series) the brain monitors supra-modal statistics of events in the environment and adapts perception cross-modally. This too comprises a variety of mechanisms, including confirmation bias to prior choices, and lower level cross-sensory adaptation. Further research into the neuronal underpinnings of the broad and diverse functions of multisensory calibration, with improved synthesis of theories is needed to attain a more comprehensive understanding of multisensory brain function.

Divisively normalized neuronal processing of uncertain visual feedback for visuomotor learning

When encountering a visual error during a reaching movement, the motor system improves the motor command for the subsequent trial. This improvement is impaired by visual error uncertainty, which is considered evidence that the motor system optimally estimates the error. However, how such statistical computation is accomplished remains unclear. Here, we propose an alternative scheme implemented with a divisive normalization (DN): the responses of neuronal elements are normalized by the summed activity of the population. This scheme assumes that when an uncertain visual error is provided by multiple cursors, the motor system processes the error conveyed by each cursor and integrates the information using DN. The DN model reproduced the patterns of learning response to 1-3 cursor errors and the impairment of learning response with visual error uncertainty. This study provides a new perspective on how the motor system updates motor commands according to uncertain visual error information.

A software tool for at-home measurement of sensorimotor adaptation

Sensorimotor adaptation is traditionally studied in well-controlled laboratory settings with specialized equipment. However, recent public health concerns such as the COVID-19 pandemic, as well as a desire to recruit a more diverse study population, have led the motor control community to consider at-home study designs. At-home motor control experiments are still rare because of the requirement to write software that can be easily used by anyone on any platform. To this end, we developed software that runs locally on a personal computer. The software provides audiovisual instructions and measures the ability of the subject to control the cursor in the context of visuomotor perturbations. We tested the software on a group of at-home participants and asked whether the adaptation principles inferred from in-lab measurements were reproducible in the at-home setting. For example, we manipulated the perturbations to test whether there were changes in adaptation rates (savings and interference), whether adaptation was associated with multiple timescales of memory (spontaneous recovery), and whether we could selectively suppress subconscious learning (delayed feedback, perturbation variability) or explicit strategies (limited reaction time). We found remarkable similarity between in-lab and at-home behaviors across these experimental conditions. Thus, we developed a software tool that can be used by research teams with little or no programming experience to study mechanisms of adaptation in an at-home setting.

Explicit learning based on reward prediction error facilitates agile motor adaptations

Error based motor learning can be driven by both sensory prediction error and reward prediction error. Learning based on sensory prediction error is termed sensorimotor adaptation, while learning based on reward prediction error is termed reward learning. To investigate the characteristics and differences between sensorimotor adaptation and reward learning, we adapted a visuomotor paradigm where subjects performed arm movements while presented with either the sensory prediction error, signed end-point error, or binary reward. Before each trial, perturbation indicators in the form of visual cues were presented to inform the subjects of the presence and direction of the perturbation. To analyse the interconnection between sensorimotor adaptation and reward learning, we designed a computational model that distinguishes between the two prediction errors. Our results indicate that subjects adapted to novel perturbations irrespective of the type of prediction error they received during learning, and they converged towards the same movement patterns. Sensorimotor adaptations led to a pronounced aftereffect, while adaptation based on reward consequences produced smaller aftereffects suggesting that reward learning does not alter the internal model to the same degree as sensorimotor adaptation. Even though all subjects had learned to counteract two different perturbations separately, only those who relied on explicit learning using reward prediction error could timely adapt to the randomly changing perturbation. The results from the computational model suggest that sensorimotor and reward learning operate through distinct adaptation processes and that only sensorimotor adaptation changes the internal model, whereas reward learning employs explicit strategies that do not result in aftereffects. Additionally, we demonstrate that when humans learn motor tasks, they utilize both learning processes to successfully adapt to the new environments.

Learning context shapes bimanual control strategy and generalization of novel dynamics

Bimanual movements are fundamental components of everyday actions, yet the underlying mechanisms coordinating adaptation of the two hands remain unclear. Although previous studies highlighted the contextual effect of kinematics of both arms on internal model formation, we do not know how the sensorimotor control system associates the learned memory with the experienced states in bimanual movements. More specifically, can, and if so, how, does the sensorimotor control system combine multiple states from different effectors to create and adapt a motor memory? Here, we tested motor memory formation in two groups with a novel paradigm requiring the encoding of the kinematics of the right hand to produce the appropriate predictive force on the left hand. While one group was provided with training movements in which this association was evident, the other group was trained on conditions in which this association was ambiguous. After adaptation, we tested the encoding of the learned motor memory by measuring the generalization to new movement combinations. While both groups adapted to the novel dynamics, the evident group showed a weighted encoding of the learned motor memory based on movements of the other (right) hand, whereas the ambiguous group exhibited mainly same (left) hand encoding in bimanual trials. Despite these differences, both groups demonstrated partial generalization to unimanual movements of the left hand. Our results show that motor memories can be encoded depending on the motion of other limbs, but that the training conditions strongly shape the encoding of the motor memory formation and determine the generalization to novel contexts.

Visuomotor adaptation, internal modelling, and compensatory movements in children with developmental coordination disorder

BACKGROUND

Developmental coordination disorder (DCD) is one of the most prevalent developmental disorders in school-aged children. The mechanisms and etiology underlying DCD remain somewhat unclear. Altered visuomotor adaptation and internal model deficits are discussed in the literature.

AIMS

The study aimed to investigate visuomotor adaptation and internal modelling to determine whether and to what extent visuomotor learning might be impaired in children with DCD compared to typically developing children (TD). Further, possible compensatory movements during visuomotor learning were explored.

METHODS AND PROCEDURES

Participants were 12 children with DCD (age 12.4 ± 1.8, four female) and 18 age-matched TD (12.3 ± 1.8, five female). Visuomotor learning was measured with the Motor task manager. Compensatory movements were parameterized by spatial and temporal variables.

OUTCOMES AND RESULTS

Despite no differences in visuomotor adaptation or internal modelling, significant main effects for group were found in parameters representing movement accuracy, motor speed, and movement variability between DCD and TD.

CONCLUSIONS AND IMPLICATIONS

Children with DCD showed comparable performances in visuomotor adaptation and internal modelling to TD. However, movement variability was increased, whereas movement accuracy and motor speed were reduced, suggesting decreased motor acuity in children with DCD.

Human-in-the-Loop Optimization of Wearable Robotic Devices to Improve Human-Robot Interaction: A Systematic Review

This article presents a systematic review on wearable robotic devices that use human-in-the-loop optimization (HILO) strategies to improve human-robot interaction. A total of 46 HILO studies were identified and divided into upper and lower limb robotic devices. The main aspects from HILO were identified, reviewed, and classified in four areas: 1) human-machine systems; 2) optimization methods; 3) control strategies; and 4) experimental protocols. A variety of objective functions (physiological, biomechanical, and subjective), optimization strategies, and optimized control parameters configurations used in different control strategies are presented and analyzed. An overview of experimental protocols is provided, including metrics, tasks, and conditions tested. Moreover, the relevance given to training or adaptation periods was explored. We outline an HILO framework that includes current wearable robots, optimization strategies, objective functions, control strategies, and experimental protocols. We conclude by highlighting current research gaps and defining future directions to improve the development of advanced HILO strategies in upper and lower limb wearable robots.

Cerebellar Degeneration Impairs Strategy Discovery but Not Strategy Recall

The cerebellum is recognized to play a critical role in the automatic and implicit process by which movement errors are used to keep the sensorimotor system precisely calibrated. However, its role in other learning processes frequently engaged during sensorimotor adaptation tasks remains unclear. In the present study, we tested the performance of individuals with cerebellar degeneration on a variant of a visuomotor adaptation task in which learning requires the use of strategic re-aiming, a process that can nullify movement errors in a rapid and volitional manner. Our design allowed us to assess two components of this learning process, the discovery of an appropriate strategy and the recall of a learned strategy. Participants were exposed to a 60° visuomotor rotation twice, with the initial exposure block assessing strategy discovery and the re-exposure block assessing strategy recall. Compared to age-matched controls, individuals with cerebellar degeneration were slower to derive an appropriate aiming strategy in the initial Discovery block but exhibited similar recall of the aiming strategy during the Recall block. This dissociation underscores the multi-faceted contributions of the cerebellum to sensorimotor learning, highlighting one way in which this subcortical structure facilitates volitional action selection.

Motor adaptation does not differ when a perturbation is introduced abruptly or gradually

People continuously adapt their movements to ever-changing circumstances, and particularly in skills training and rehabilitation, it is crucial that we understand how to optimize implicit adaptation in order for these processes to require as little conscious effort as possible. Although it is generally assumed that the way to do this is by introducing perturbations gradually, the literature is ambivalent on the effectiveness of this approach. Here, we tested whether there are differences in motor performance when adapting to an abrupt compared to a ramped visuomotor rotation. Using a within-subjects design, we tested this question under 3 different rotation sizes: 30-degrees, 45-degrees, and 60-degrees, as well as in 3 different populations: younger adults, older adults, and patients with mild cerebellar ataxia. We find no significant differences in either the behavioural outcomes, or model fits, between abrupt and gradual learning across any of the different conditions. Neither age, nor cerebellar ataxia had any significant effect on error-sensitivity either. These findings together indicate that error-sensitivity is not modulated by introducing a perturbation abruptly compared to gradually, and is also unaffected by age or mild cerebellar ataxia.

Embodied perspective-taking enhances interpersonal synchronization: A body-swap study

Humans exhibit a strong tendency to synchronize movements with each other, with visual perspective potentially influencing interpersonal synchronization. By manipulating the visual scenes of participants engaged in a joint finger-tapping task, we examined the effects of 1st person and 2nd person visual perspectives on their coordination dynamics. We hypothesized that perceiving the partner's movements from their 1st person perspective would enhance spontaneous interpersonal synchronization, potentially mediated by the embodiment of the partner's hand. We observed significant differences in attractor dynamics across visual perspectives. Specifically, participants in 1st person coupling were unable to maintain de-coupled trajectories as effectively as in 2nd person coupling. Our findings suggest that visual perspective influences coordination dynamics in dyadic interactions, engaging error-correction mechanisms in individual brains as they integrate the partner's hand into their body representation. Our results have the potential to inform the development of applications for motor training and rehabilitation.

FOESO-Net: A specific neural network for fast sensorless robot manipulator torque estimation

Contact torque sensing allows robot manipulators to cooperate with humans and detect accidental collisions in real time to ensure safety. Most sensorless torque estimation schemes, which are based on linear observer approaches, cannot compromise between non-negligible noise and high observation bandwidth. Therefore, fast time-varying nonlinear torque observation cannot be satisfied. To achieve this challenge, a customized network called FOESO-Net based on a novel fractional-order extended state observer is carefully designed in this paper. The network firstly chooses momentum as the benchmark state for torque estimation, which can avoid joint acceleration and model's inverse inertia matrix solution. Then, a fractional-order extended state observer (FOESO) is proposed from the perspective of momentum control to better adapt to the nonlinear fast time varying torque. In addition, a fractional-order neural network and a weight update neural network parallel architecture are constructed to enable fractional-order and dynamic weight-based adaptive learning of FOESO parameters. Formal analysis and proofs are made to show that the error of FOESO-Net is convergent. Finally, the effectiveness of the proposed method is verified by numerical simulations and a real collaborative robot platform. Moreover, compared with existing methods, the FOESO-Net based torque estimation method can reduce the estimation error and response time, which illustrates the superiority of the designed method.

Slower rates of prism adaptation but intact aftereffects in patients with early to mid-stage Parkinson's disease

There is currently mixed evidence on the effect of Parkinson's disease on motor adaptation. Some studies report that patients display adaptation comparable to age-matched controls, while others report a complete inability to adapt to novel sensory perturbations. Here, early to mid-stage Parkinson's patients were recruited to perform a prism adaptation task. When compared to controls, patients showed slower rates of initial adaptation but intact aftereffects. These results support the suggestion that patients with early to mid-stage Parkinson's disease display intact adaptation driven by sensory prediction errors, as shown by the intact aftereffect. But impaired facilitation of performance through cognitive strategies informed by task error, as shown by the impaired initial adaptation. These results support recent studies that suggest that patients with Parkinson's disease retain the ability to perform visuomotor adaptation, but display altered use of cognitive strategies to aid performance and generalises these previous findings to the classical prism adaptation task.

Error-independent effect of sensory uncertainty on motor learning when both feedforward and feedback control processes are engaged

Integrating sensory information during movement and adapting motor plans over successive movements are both essential for accurate, flexible motor behaviour. When an ongoing movement is off target, feedback control mechanisms update the descending motor commands to counter the sensed error. Over longer timescales, errors induce adaptation in feedforward planning so that future movements become more accurate and require less online adjustment from feedback control processes. Both the degree to which sensory feedback is integrated into an ongoing movement and the degree to which movement errors drive adaptive changes in feedforward motor plans have been shown to scale inversely with sensory uncertainty. However, since these processes have only been studied in isolation from one another, little is known about how they are influenced by sensory uncertainty in real-world movement contexts where they co-occur. Here, we show that sensory uncertainty may impact feedforward adaptation of reaching movements differently when feedback integration is present versus when it is absent. In particular, participants gradually adjust their movements from trial-to-trial in a manner that is well characterised by a slow and consistent envelope of error reduction. Riding on top of this slow envelope, participants exhibit large and abrupt changes in their initial movement vectors that are strongly correlated with the degree of sensory uncertainty present on the previous trial. However, these abrupt changes are insensitive to the magnitude and direction of the sensed movement error. These results prompt important questions for current models of sensorimotor learning under uncertainty and open up new avenues for future exploration in the field.

Time of day and sleep effects on motor acquisition and consolidation

We investigated the influence of the time-of-day and sleep on skill acquisition (i.e., skill improvement immediately after a training-session) and consolidation (i.e., skill retention after a time interval including sleep). Three groups were trained at 10 a.m. (G10am), 3 p.m. (G3pm), or 8 p.m. (G8pm) on a finger-tapping task. We recorded the skill (i.e., the ratio between movement duration and accuracy) before and immediately after the training to evaluate acquisition, and after 24 h to measure consolidation. We did not observe any difference in acquisition according to the time of the day. Interestingly, we found a performance improvement 24 h after the evening training (G8pm), while the morning (G10am) and the afternoon (G3pm) groups deteriorated and stabilized their performance, respectively. Furthermore, two control experiments (G8awake and G8sleep) supported the idea that a night of sleep contributes to the skill consolidation of the evening group. These results show a consolidation when the training is carried out in the evening, close to sleep, and forgetting when the training is carried out in the morning, away from sleep. This finding may have an important impact on the planning of training programs in sports, clinical, or experimental domains.

Effect of visuo-proprioceptive mismatch rate on recalibration in hand perception

We estimate our hand's position by combining relevant visual and proprioceptive cues. A cross-sensory spatial mismatch can be created by viewing the hand through a prism or, more recently, rotating a visual cursor that represents hand position. This is often done in the context of target-directed reaching to study motor adaptation, the systematic updating of motor commands in response to a systematic movement error. However, a visuo-proprioceptive mismatch also elicits recalibration in the relationship between the hand's seen and felt position. The principles governing visuo-proprioceptive recalibration are poorly understood, compared to motor adaptation. For example, motor adaptation occurs robustly whether the cursor is rotated quickly or slowly, although the former may involve more explicit processes. Here, we asked whether visuo-proprioceptive recalibration, in the absence of motor adaptation, works the same way. Three groups experienced a 70 mm visuo-proprioceptive mismatch about their hand at a Slow, Medium, or Fast rate (0.84, 1.67, or 3.34 mm every two trials, respectively), with no error feedback. Once attained, the 70 mm mismatch was maintained for the remaining trials. Total recalibration differed significantly across groups, with the Fast, Medium, and Slow groups recalibrating 63.7, 56.3, and 42.8 mm on average, respectively. This suggests a slower mismatch rate may be less effective at eliciting recalibration. In contrast to motor adaptation studies, no further recalibration was observed in the maintenance phase. This may be related to the distinct mechanisms thought to contribute to perceptual recalibration via cross-sensory cue conflict versus sensory prediction errors.

Task Instructions and the Need for Feedback Correction Influence the Contribution of Visual Errors to Reach Adaptation

Previous research has questioned whether motor adaptation is shaped by an optimal combination of multisensory error signals. Here, we expanded on this work by investigating how the use of visual and somatosensory error signals during online correction influences single-trial adaptation. To this end, we exposed participants to a random sequence of force-field perturbations and recorded their corrective responses as well as the after-effects exhibited during the subsequent unperturbed movement. In addition to the force perturbation, we artificially decreased or increased visual errors by multiplying hand deviations by a gain smaller or larger than one. Corrective responses to the force perturbation clearly scaled with the size of the visual error, but this scaling did not transfer one-to-one to motor adaptation and we observed no consistent interaction between limb and visual errors on adaptation. However, reducing visual errors during perturbation led to a small reduction of after-effects and this residual influence of visual feedback was eliminated when we instructed participants to control their hidden hand instead of the visual hand cursor. Taken together, our results demonstrate that task instructions and the need to correct for errors during perturbation are important factors to consider if we want to understand how the sensorimotor system uses and combines multimodal error signals to adapt movements.

A triple distinction of cerebellar function for oculomotor learning and fatigue compensation

The cerebellum implements error-based motor learning via synaptic gain adaptation of an inverse model, i.e. the mapping of a spatial movement goal onto a motor command. Recently, we modeled the motor and perceptual changes during learning of saccadic eye movements, showing that learning is actually a threefold process. Besides motor recalibration of (1) the inverse model, learning also comprises perceptual recalibration of (2) the visuospatial target map and (3) of a forward dynamics model that estimates the saccade size from corollary discharge. Yet, the site of perceptual recalibration remains unclear. Here we dissociate cerebellar contributions to the three stages of learning by modeling the learning data of eight cerebellar patients and eight healthy controls. Results showed that cerebellar pathology restrains short-term recalibration of the inverse model while the forward dynamics model is well informed about the reduced saccade change. Adaptation of the visuospatial target map trended in learning direction only in control subjects, yet without reaching significance. Moreover, some patients showed a tendency for uncompensated oculomotor fatigue caused by insufficient upregulation of saccade duration. According to our model, this could induce long-term perceptual compensation, consistent with the overestimation of target eccentricity found in the patients' baseline data. We conclude that the cerebellum mediates short-term adaptation of the inverse model, especially by control of saccade duration, while the forward dynamics model was not affected by cerebellar pathology.