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.

Late integration of vision and proprioception during perturbed reaches.

The motor system corrects rapidly, but selectively, for perturbations to ongoing reaching movements, depending on the constraints of the task. To account for such sophistication, it has been postulated that corrections are based on an estimated limb state that integrates all sensory changes caused by the perturbation, taking into account their processing delays. Here, we asked if information from different sensory modalities is integrated immediately or processed separately in the early phase of a response. We perturbed the estimated state of the limb with both unimodal and bimodal visual and proprioceptive perturbations without changing the actual limb state. For visual perturbations, a cursor representing the hand was shifted to the left or the right relative to the true hand location. For proprioceptive perturbations, the biceps or triceps muscles were vibrated, which induced illusory limb-state changes to the right or the left. In the bimodal condition, the perturbations to vision and proprioception were either congruent or incongruent in their directions. Response latencies show that it takes ∼100 ms longer to respond to unimodal visual perturbations than to unimodal proprioceptive perturbations. Responses to bimodal perturbations show that it takes an additional ∼100 ms beyond the response to unimodal visual perturbations for intermodal consistency to impact the response. These results suggest that visual and proprioceptive signals are initially processed separately for state estimation and only combined at the level of the limb's motor output, instead of being immediately integrated into a single state estimate of the limb.NEW & NOTEWORTHY Both visual and proprioceptive signals provide information about arm state during reaching. By perturbing the perceived, but not the actual, position of the hand in both modalities using visual disturbances and muscle vibration, we examined multimodal integration and state estimation during reaching. Our results suggest that the early reach corrections are based on separate state estimates from the two sensory modalities and only later are based on a combined state estimate.

Natural statistics of head roll: implications for Bayesian inference in spatial orientation.

We previously proposed a Bayesian model of multisensory integration in spatial orientation (Clemens IAH, de Vrijer M, Selen LPJ, van Gisbergen JAM, Medendorp WP. J Neurosci 31: 5365-5377, 2011). Using a Gaussian prior, centered on an upright head orientation, this model could explain various perceptual observations in roll-tilted participants, such as the subjective visual vertical, the subjective body tilt (Clemens IAH, de Vrijer M, Selen LPJ, van Gisbergen JAM, Medendorp WP. J Neurosci 31: 5365-5377, 2011), the rod-and-frame effect (Alberts BBGT, de Brouwer AJ, Selen LPJ, Medendorp WP. eNeuro 3: ENEURO.0093-16.2016, 2016), as well as their clinical (Alberts BBGT, Selen LPJ, Verhagen WIM, Medendorp WP. Physiol Rep 3: e12385, 2015) and age-related deficits (Alberts BBGT, Selen LPJ, Medendorp WP. J Neurophysiol 121: 1279-1288, 2019). Because it is generally assumed that the prior reflects an accumulated history of previous head orientations, and recent work on natural head motion suggests non-Gaussian statistics, we examined how the model would perform with a non-Gaussian prior. In the present study, we first experimentally generalized the previous observations in showing that also the natural statistics of head orientation are characterized by long tails, best quantified as a t-location-scale distribution. Next, we compared the performance of the Bayesian model and various model variants using such a t-distributed prior to the original model with the Gaussian prior on their accounts of previously published data of the subjective visual vertical and subjective body tilt tasks. All of these variants performed substantially worse than the original model, suggesting a special value of the Gaussian prior. We provide computational and neurophysiological reasons for the implementation of such a prior, in terms of its associated precision-accuracy trade-off in vertical perception across the tilt range.NEW & NOTEWORTHY It has been argued that the brain uses Bayesian computations to process multiple sensory cues in vertical perception, including a prior centered on upright head orientation which is usually taken to be Gaussian. Here, we show that non-Gaussian prior distributions, although more akin to the statistics of head orientation during natural activities, provide a much worse explanation of such perceptual observations than a Gaussian prior.

Assessing corticospinal excitability and reaching hand choice during whole body motion.

Behavioral studies have shown that humans account for inertial acceleration in their decisions of hand choice when reaching during body motion. Physiologically, it is unclear at what stage of movement preparation information about body motion is integrated with the process of hand selection. Here, we addressed this question by applying transcranial magnetic stimulation over left motor cortex (M1) of human participants who performed a preferential reach task while they were sinusoidally translated on a linear motion platform. If M1 only represents a read-out of the final hand choice, we expect the body motion not to affect the motor-evoked potential (MEP) amplitude. If body motion biases the hand selection process before target onset, we expect corticospinal excitability to be influenced by the phase of the motion, with larger MEP amplitudes for phases that show a bias to using the right hand. Behavioral results replicate our earlier findings of a sinusoidal modulation of hand choice bias with motion phase. MEP amplitudes also show a sinusoidal modulation with motion phase, suggesting that body motion influences corticospinal excitability, which may ultimately reflect changes of hand preference. The modulation being present before target onset suggests that competition between hands is represented throughout the corticospinal tract. Its phase relationship with the motion profile indicates that other processes after target onset take up time until the hand selection process has been completely resolved, and the reach is initiated.NEW & NOTEWORTHY Full body-motion biases decisions of hand choice. We examined the signatures of this bias in hand preference in corticospinal excitability before a reach target was presented. Our results show that behavior and corticospinal excitability modulate depending on the state of the body in motion. This suggests that information about body motion penetrates deeply within the motor system.

Even well-practiced movements benefit from repetition.

Professional golfers spend years practicing, but will still perform one or two practice swings without a ball before executing the actual swing. Why do they do this? In this study, we tested the hypothesis that repeating a well-practiced movement leads to a reduction of movement variability. To operationalize this hypothesis, participants were tested in a center-out reaching task with four different targets, on four different days. To probe the effect of repetition they performed random sequences from one to six movements to the same target. Our findings show that, with repetition, movements are not only initiated earlier but their variability is reduced across the entire movement trajectory. Furthermore, this effect is present within and across the four sessions. Together, our results suggest that movement repetition changes the tradeoff between movement initiation and movement precision.NEW & NOTEWORTHY Professional athletes practice movements that they have performed thousands of times in training just before it is their turn in a game. Why do they do this? Our results indicate that both initial and endpoint variability reduce with repetition in a short sequence of reaching movements. This means that even well-practiced movements benefit from practice.

Cortical beta-band power modulates with uncertainty in effector selection during motor planning.

Although beta-band activity during motor planning is known to be modulated by uncertainty about where to act, less is known about its modulations to uncertainty about how to act. To investigate this issue, we recorded oscillatory brain activity with EEG while human participants (n = 17) performed a hand choice reaching task. The reaching hand was either predetermined or of participants' choice, and the target was close to one of the two hands or at about equal distance from both. To measure neural activity in a motion artifact-free time window, the location of the upcoming target was cued 1,000-1,500 ms before the presentation of the target, whereby the cue was valid in 50% of trials. As evidence for motor planning during the cuing phase, behavioral observations showed that the cue affected later hand choice. Furthermore, reaction times were longer in the choice trials than in the predetermined trials, supporting the notion of a competitive process for hand selection. Modulations of beta-band power over central cortical regions, but not alpha-band or theta-band power, were in line with these observations. During the cuing period, reaches in predetermined trials were preceded by larger decreases in beta-band power than reaches in choice trials. Cue direction did not affect reaction times or beta-band power, which may be due to the cue being invalid in 50% of trials, retaining effector uncertainty during motor planning. Our findings suggest that effector uncertainty modulates beta-band power during motor planning.NEW & NOTEWORTHY Although reach-related beta-band power in central cortical areas is known to modulate with the number of potential targets, here we show, using a cuing paradigm, that the power in this frequency band, but not in the alpha or theta band, is also modulated by the uncertainty of which hand to use. This finding supports the notion that multiple possible effector-specific actions can be specified in parallel up to the level of motor preparation.

Movement preparation time determines movement variability.

Faster movements are typically more variable-a speed-accuracy trade-off known as Fitts' law. Are movements that are initiated faster also more variable? Neurophysiological work has associated larger neural variability during motor preparation with longer reaction time (RT) and larger movement variability, implying that movement variability decreases with increasing RT. Here, we recorded over 30,000 reaching movements in 11 human participants who moved to visually cued targets. Half of the visual cues were accompanied by a beep to evoke a wide RT range in each participant. Results show that initial reach variability decreases with increasing RT, for voluntarily produced RTs up to ∼300 ms, whereas other kinematic aspects and endpoint accuracy remained unaffected. We conclude that movement preparation time determines initial movement variability. We suggest that the chosen movement preparation time reflects a trade-off between movement initiation and precision.NEW & NOTEWORTHY Fitts' law describes the speed-accuracy trade-off in the execution of human movements. We examined whether there is also a trade-off between movement planning time and initial movement precision. We show that shorter reaction times result in higher initial movement variability. In other words, movement preparation time determines movement variability.

Correction: Reward abundance interferes with error-based learning in a visuomotor adaptation task.

[This corrects the article DOI: 10.1371/journal.pone.0193002.].

Vestibular modulation of visuomotor feedback gains in reaching.

Humans quickly and sophisticatedly correct their movements in response to changes in the world, such as when reaching to a target that abruptly changes its location. The vigor of these movement corrections is time-dependent, increasing if the time left to make the correction decreases, which can be explained by optimal feedback control (OFC) theory as an increase of optimal feedback gains. It is unknown whether corrections for changes in the world are as sophisticated under full-body motion. For successful visually probed motor corrections during full-body motion, not only the motion of the hand relative to the body needs to be taken into account, but also the motion of the hand in the world should be considered, because their relative positions are changing. Here, in two experiments, we show that visuomotor feedback corrections in response to target jumps are more vigorous for faster passive full-body translational acceleration than for slower acceleration, suggesting that vestibular information modulates visuomotor feedback gains. Interestingly, these corrections do not demonstrate the time-dependent characteristics that body-stationary visuomotor feedback gains typically show, such that an optimal feedback control model fell short to explain them. We further show that the vigor of corrections generally decreased over the course of trials within the experiment, suggesting that the sensorimotor system adjusted its gains when learning to integrate the vestibular input into hand motor control.NEW & NOTEWORTHY Vestibular information is used in the control of reaching movements to world-stationary visual targets, while the body moves. Here, we show that vestibular information also modulates the corrective reach responses when the target changes position during the body motion: visuomotor feedback gains increase for faster body acceleration. Our results suggest that vestibular information is integrated into fast visuomotor control of reaching movements.

Reward abundance interferes with error-based learning in a visuomotor adaptation task.

The brain rapidly adapts reaching movements to changing circumstances by using visual feedback about errors. Providing reward in addition to error feedback facilitates the adaptation but the underlying mechanism is unknown. Here, we investigate whether the proportion of trials rewarded (the 'reward abundance') influences how much participants adapt to their errors. We used a 3D multi-target pointing task in which reward alone is insufficient for motor adaptation. Participants (N = 423) performed the pointing task with feedback based on a shifted hand-position. On a proportion of trials we gave them rewarding feedback that their hand hit the target. Half of the participants only received this reward feedback. The other half also received feedback about endpoint errors. In different groups, we varied the proportion of trials that was rewarded. As expected, participants who received feedback about their errors did adapt, but participants who only received reward-feedback did not. Critically, participants who received abundant rewards adapted less to their errors than participants who received less reward. Thus, reward abundance negatively influences how much participants learn from their errors. Probably participants used a mechanism that relied more on the reward feedback when the reward was abundant. Because participants could not adapt to the reward, this interfered with adaptation to errors.

State Estimation for Early Feedback Responses in Reaching: Intramodal or Multimodal?

Humans are highly skilled in controlling their reaching movements, making fast and task-dependent movement corrections to unforeseen perturbations. To guide these corrections, the neural control system requires a continuous, instantaneous estimate of the current state of the arm and body in the world. According to Optimal Feedback Control theory, this estimate is multimodal and constructed based on the integration of forward motor predictions and sensory feedback, such as proprioceptive, visual and vestibular information, modulated by context, and shaped by past experience. But how can a multimodal estimate drive fast movement corrections, given that the involved sensory modalities have different processing delays, different coordinate representations, and different noise levels? We develop the hypothesis that the earliest online movement corrections are based on multiple single modality state estimates rather than one combined multimodal estimate. We review studies that have investigated online multimodal integration for reach control and offer suggestions for experiments to test for the existence of intramodal state estimates. If proven true, the framework of Optimal Feedback Control needs to be extended with a stage of intramodal state estimation, serving to drive short-latency movement corrections.

Cerebellar tDCS dissociates the timing of perceptual decisions from perceptual change in speech.

Neuroimaging studies suggest that the cerebellum might play a role in both speech perception and speech perceptual learning. However, it remains unclear what this role is: does the cerebellum help shape the perceptual decision, or does it contribute to the timing of perceptual decisions? To test this, we used transcranial direct current stimulation (tDCS) in combination with a speech perception task. Participants experienced a series of speech perceptual tests designed to measure and then manipulate (via training) their perception of a phonetic contrast. One group received cerebellar tDCS during speech perceptual learning, and a different group received sham tDCS during the same task. Both groups showed similar learning-related changes in speech perception that transferred to a different phonetic contrast. For both trained and untrained speech perceptual decisions, cerebellar tDCS significantly increased the time it took participants to indicate their decisions with a keyboard press. By analyzing perceptual responses made by both hands, we present evidence that cerebellar tDCS disrupted the timing of perceptual decisions, while leaving the eventual decision unaltered. In support of this conclusion, we use the drift diffusion model to decompose the data into processes that determine the outcome of perceptual decision-making and those that do not. The modeling suggests that cerebellar tDCS disrupted processes unrelated to decision-making. Taken together, the empirical data and modeling demonstrate that right cerebellar tDCS dissociates the timing of perceptual decisions from perceptual change. The results provide initial evidence in healthy humans that the cerebellum critically contributes to speech timing in the perceptual domain.

Competition between movement plans increases motor variability: evidence of a shared resource for movement planning.

Do movement plans, like representations in working memory, share a limited pool of resources? If so, the precision with which each individual movement plan is specified should decrease as the total number of movement plans increases. To explore this, human participants made speeded reaching movements toward visual targets. We examined if preparing one movement resulted in less variability than preparing two movements. The number of planned movements was manipulated in a delayed response cueing procedure that limited planning to a single target (experiment 1) or hand (experiment 2) or required planning of movements toward two targets (or with two hands). For both experiments, initial movement direction variability was higher in the two-plan condition than in the one-plan condition, demonstrating a cost associated with planning multiple movements, consistent with the limited resource hypothesis. In experiment 3, we showed that the advantage in initial variability of preparing a single movement was present only when the trajectory could be fully specified. This indicates that the difference in variability between one and two plans reflects the specification of full motor plans, not a general preparedness to move. The precision cost related to concurrent plans represents a novel constraint on motor preparation, indicating that multiple movements cannot be planned independently, even if they involve different limbs.

Exposing sequence learning in a double-step task.

Is it possible to learn to perform a motor sequence without awareness of the sequence? In two experiments, we presented participants with the most elementary sequence: an alternation between two options. We used a double-step pointing task in which the final position of the target alternated between two quite similar values. The task forced participants to start moving before the final target was visible, allowing us to determine participants' expectations about the final target position without explicitly asking them. We tracked participants' expectations (and thus motor sequence learning) by measuring the direction of the initial part of the movement, before any response to the final step. We found that participants learnt to anticipate the average size of the final step, but that they did not learn the sequence. In a second experiment, we extended the duration of the learning period and increased the difference in size between the target position changes. Some participants started anticipating the step size in accordance with the sequence at some time during the experiment. These participants reported having noticed the simple sequence. The participants who had not noticed the sequence did not move in anticipation of the sequence. This suggests that participants who did not learn this very simple sequence explicitly also did not learn it implicitly.

Movement Adjustments Have Short Latencies Because There is No Need to Detect Anything.

We can adjust an ongoing movement to a change in the target's position with a latency of about 100 ms, about half of the time that is needed to start a new movement in response to the same change in target position (reaction time). In this opinion paper, we discuss factors that could explain the difference in latency between initiating and adjusting a movement in response to target displacements. We consider the latency to be the sum of the durations of various stages in information processing. Many of these stages are identical for adjusting and initiating a movement; however, for movement initiation, it is essential to detect that something has changed to respond, whereas adjustments to movements can be based on updated position information without detecting that the position has changed. This explanation for the shorter latency for movement adjustments also explains why we can respond to changes that we do not detect.

Reacting With or Without Detecting.

We begin our response by clarifying the concept of detection, and explaining why this is needed for initiating, but not for adjusting a movement. We present a simulation to illustrate this difference. Several commentators referred to studies with results that might seem in conflict with our proposal that movement adjustments have short latencies because there is no need to detect anything. In the last part of our response, we discuss how we interpret these studies as being in line with our proposal.

Evidence for Optimal Integration of Visual Feature Representations across Saccades.

We explore the visual world through saccadic eye movements, but saccades also present a challenge to visual processing by shifting externally stable objects from one retinal location to another. The brain could solve this problem in two ways: by overwriting preceding input and starting afresh with each fixation or by maintaining a representation of presaccadic visual features in working memory and updating it with new information from the remapped location. Crucially, when multiple objects are present in a scene the planning of eye movements profoundly affects the precision of their working memory representations, transferring limited memory resources from fixation toward the saccade target. Here we show that when humans make saccades, it results in an update of not just the precision of representations but also their contents. When multiple item colors are shifted imperceptibly during a saccade the perceived colors are found to fall between presaccadic and postsaccadic values, with the weight given to each input varying continuously with item location, and fixed relative to saccade parameters. Increasing sensory uncertainty, by adding color noise, biases updating toward the more reliable input, which is consistent with an optimal integration of presaccadic working memory with a postsaccadic updating signal. We recover this update signal and show it to be tightly focused on the vicinity of the saccade target. These results reveal how the nervous system accumulates detailed visual information from multiple views of the same object or scene. SIGNIFICANCE STATEMENT: This study examines the consequences of saccadic eye movements for the internal representation of visual objects. A saccade shifts the image of a stable visual object from one part of the retina to another. We show that visual representations are built up over these different views of the same object, by combining information obtained before and after each saccade. The weights given to presaccadic and postsaccadic information are determined by the relative reliability of each input. This provides evidence that the visual system combines inputs over time in a statistically optimal way.

Online manual movement adjustments in response to target position changes and apparent target motion.

This study set out to determine whether the fastest online hand movement corrections are only responses to changing judgments of the targets' position or whether they are also influenced by the apparent target motion. Introducing a gap between when a target disappears and when it reappears at a new position in a double-step paradigm disrupts the apparent motion, so we examined the influence of such a gap on the intensity of the response. We found that responses to target perturbations with disrupted apparent motion were less vigorous. The response latency was 10 ms shorter when there was a gap, which might be related to the gap effect that has previously been described for initiating eye and hand movements.

Analysis of methods to determine the latency of online movement adjustments.

When studying online movement adjustments, one of the interesting parameters is their latency. We set out to compare three different methods of determining the latency: the threshold, confidence interval, and extrapolation methods. We simulated sets of movements with different movement times and amplitudes of movement adjustments, all with the same known latency. We applied the three different methods in order to determine when the position, velocity, and acceleration of the adjusted movements started to deviate from the values for unperturbed movements. We did so both for averaged data and for the data of individual trials. We evaluated the methods on the basis of their accuracy and precision, and according to whether the latency was influenced by the intensity of the movement adjustment. The extrapolation method applied to average acceleration data gave the most reliable estimates of latency, according to these criteria.