Causal Role of Motor Preparation during Error-Driven Learning

A neurocomputational model of decision and confidence in object recognition task

How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confidence. In this circumstance, confidence is making a bridge between seeing and believing. Our study unveils how the brain processes visual information to make such decisions with an assessment of confidence, using a model inspired by the visual cortex. To computationally model the process, this study uses a spiking neural network inspired by the hierarchy of the visual cortex in mammals to investigate the dynamics of feedforward object recognition and decision-making in the brain. The model consists of two modules: a temporal dynamic object representation module and an attractor neural network-based decision-making module. Unlike traditional models, ours captures the evolution of evidence within the visual cortex, mimicking how confidence forms in the brain. This offers a more biologically plausible approach to decision-making when encountering real-world stimuli. We conducted experiments using natural stimuli and measured accuracy, reaction time, and confidence. The model's estimated confidence aligns remarkably well with human-reported confidence. Furthermore, the model can simulate the human change-of-mind phenomenon, reflecting the ongoing evaluation of evidence in the brain. Also, this finding offers decision-making and confidence encoding share the same neural circuit.

De novo motor learning creates structure in neural activity that shapes adaptation

Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.

Abnormalities in motor adaptation to different types of perturbations in schizophreniaperturbations in schizophrenia

Schizophrenia is a mental health disorder that often includes psychomotor disturbances, impacting how individuals adjust their motor output based on the cause of motor errors. While previous motor adaptation studies on individuals with schizophrenia have largely focused on large and consistent perturbations induced by abrupt experimental manipulations, such as donning prism goggles, the adaptation process to random perturbations, either caused by intrinsic motor noise or external disturbances, has not been examined - despite its ecological relevance. Here, we used a unified behavioral task paradigm to examine motor adaptation to perturbations of three causal structures among individuals in the remission stage of schizophrenia, youth with ultra-high risk of psychosis, adults with active symptoms, and age-matched controls. Results showed that individuals with schizophrenia had reduced trial-by-trial adaptation and large error variance when adapting to their own motor noise. When adapting to random but salient perturbations, they showed intact adaptation and normal causal inference of errors. This contrasted with reduced adaptation to large yet consistent perturbations, which could reflect difficulties in forming cognitive strategies rather than the often-assumed impairments in procedural learning or sense of agency. Furthermore, the observed adaptation effects were correlated with the severity of positive symptoms across the diagnosis groups. Our findings suggest that individuals with schizophrenia face challenges in accommodating intrinsic perturbations when motor errors are ambiguous but adapt with intact causal attribution when errors are salient.

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.

Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases

Actions are biased by the outcomes they can produce: Humans are more likely to show action under reward prospect, but hold back under punishment prospect. Such motivational biases derive not only from biased response selection, but also from biased learning: humans tend to attribute rewards to their own actions, but are reluctant to attribute punishments to having held back. The neural origin of these biases is unclear. Specifically, it remains open whether motivational biases arise primarily from the architecture of subcortical regions or also reflect cortical influences, the latter being typically associated with increased behavioral flexibility and control beyond stereotyped behaviors. Simultaneous EEG-fMRI allowed us to track which regions encoded biased prediction errors in which order. Biased prediction errors occurred in cortical regions (dorsal anterior and posterior cingulate cortices) before subcortical regions (striatum). These results highlight that biased learning is not a mere feature of the basal ganglia, but arises through prefrontal cortical contributions, revealing motivational biases to be a potentially flexible, sophisticated mechanism.

Methylphenidate modulates motor cortical dynamics and behavior

Methylphenidate (MPH, brand: Ritalin) is a common stimulant used both medically and non-medically. Though typically prescribed for its cognitive effects, MPH also affects movement. While it is known that MPH noncompetitively blocks the reuptake of catecholamines through inhibition of dopamine and norepinephrine transporters, a critical step in exploring how it affects behavior is to understand how MPH directly affects neural activity. This would establish an electrophysiological mechanism of action for MPH. Since we now have biologically-grounded network-level hypotheses regarding how populations of motor cortical neurons plan and execute movements, there is a unique opportunity to make testable predictions regarding how systemic MPH administration - a pharmacological perturbation - might affect neural activity in motor cortex. To that end, we administered clinically-relevant doses of MPH to Rhesus monkeys as they performed an instructed-delay reaching task. Concomitantly, we measured neural activity from dorsal premotor and primary motor cortex. Consistent with our predictions, we found dose-dependent and significant effects on reaction time, trial-by-trial variability, and movement speed. We confirmed our hypotheses that changes in reaction time and variability were accompanied by previously established population-level changes in motor cortical preparatory activity and the condition-independent signal that precedes movements. We expected changes in speed to be a result of changes in the amplitude of motor cortical dynamics and/or a translation of those dynamics in activity space. Instead, our data are consistent with a mechanism whereby the neuromodulatory effect of MPH is to increase the gain and/or the signal-to-noise of motor cortical dynamics during reaching. Continued work in this domain to better understand the brain-wide electrophysiological mechanism of action of MPH and other psychoactive drugs could facilitate more targeted treatments for a host of cognitive-motor disorders.

Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex

We used a dynamical systems perspective to understand decision-related neural activity, a fundamentally unresolved problem. This perspective posits that time-varying neural activity is described by a state equation with an initial condition and evolves in time by combining at each time step, recurrent activity and inputs. We hypothesized various dynamical mechanisms of decisions, simulated them in models to derive predictions, and evaluated these predictions by examining firing rates of neurons in the dorsal premotor cortex (PMd) of monkeys performing a perceptual decision-making task. Prestimulus neural activity (i.e., the initial condition) predicted poststimulus neural trajectories, covaried with RT and the outcome of the previous trial, but not with choice. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and fast initial conditions leading to the fastest choice-related dynamics. Together, these results suggest that initial conditions combine with sensory evidence to induce decision-related dynamics in PMd.

Combined low-frequency brain oscillatory activity and behavior predict future errors in human motor skill

Human skills are composed of sequences of individual actions performed with utmost precision. When occasional errors occur, they may have serious consequences, for example, when pilots are manually landing a plane. In such cases, the ability to predict an error before it occurs would clearly be advantageous. Here, we asked whether it is possible to predict future errors in a keyboard procedural human motor skill. We report that prolonged keypress transition times (KTTs), reflecting slower speed, and anomalous delta-band oscillatory activity in cingulate-entorhinal-precuneus brain regions precede upcoming errors in skill. Combined anomalous low-frequency activity and prolonged KTTs predicted up to 70% of future errors. Decoding strength (posterior probability of error) increased progressively approaching the errors. We conclude that it is possible to predict future individual errors in skill sequential performance.

Multidimensional cerebellar computations for flexible kinematic control of movements

Both the environment and our body keep changing dynamically. Hence, ensuring movement precision requires adaptation to multiple demands occurring simultaneously. Here we show that the cerebellum performs the necessary multi-dimensional computations for the flexible control of different movement parameters depending on the prevailing context. This conclusion is based on the identification of a manifold-like activity in both mossy fibers (MFs, network input) and Purkinje cells (PCs, output), recorded from monkeys performing a saccade task. Unlike MFs, the PC manifolds developed selective representations of individual movement parameters. Error feedback-driven climbing fiber input modulated the PC manifolds to predict specific, error type-dependent changes in subsequent actions. Furthermore, a feed-forward network model that simulated MF-to-PC transformations revealed that amplification and restructuring of the lesser variability in the MF activity is a pivotal circuit mechanism. Therefore, the flexible control of movements by the cerebellum crucially depends on its capacity for multi-dimensional computations.

F = ma. Is the macaque brain Newtonian?

Intuitive Physics, the ability to anticipate how the physical events involving mass objects unfold in time and space, is a central component of intelligent systems. Intuitive physics is a promising tool for gaining insight into mechanisms that generalize across species because both humans and non-human primates are subject to the same physical constraints when engaging with the environment. Physical reasoning abilities are widely present within the animal kingdom, but monkeys, with acute 3D vision and a high level of dexterity, appreciate and manipulate the physical world in much the same way humans do.

Parallel movement planning is achieved via an optimal preparatory state in motor cortex

How do patterns of neural activity in the motor cortex contribute to the planning of a movement? A recent theory developed for single movements proposes that the motor cortex acts as a dynamical system whose initial state is optimized during the preparatory phase of the movement. This theory makes important yet untested predictions about preparatory dynamics in more complex behavioral settings. Here, we analyze preparatory activity in non-human primates planning not one but two movements simultaneously. As predicted by the theory, we find that parallel planning is achieved by adjusting preparatory activity within an optimal subspace to an intermediate state reflecting a trade-off between the two movements. The theory quantitatively accounts for the relationship between this intermediate state and fluctuations in the animals' behavior down at the trial level. These results uncover a simple mechanism for planning multiple movements in parallel and further point to motor planning as a controlled dynamical process.

Sensorimotor feedback loops are selectively sensitive to reward

Although it is well established that motivational factors such as earning more money for performing well improve motor performance, how the motor system implements this improvement remains unclear. For instance, feedback-based control, which uses sensory feedback from the body to correct for errors in movement, improves with greater reward. But feedback control encompasses many feedback loops with diverse characteristics such as the brain regions involved and their response time. Which specific loops drive these performance improvements with reward is unknown, even though their diversity makes it unlikely that they are contributing uniformly. We systematically tested the effect of reward on the latency (how long for a corrective response to arise?) and gain (how large is the corrective response?) of seven distinct sensorimotor feedback loops in humans. Only the fastest feedback loops were insensitive to reward, and the earliest reward-driven changes were consistently an increase in feedback gains, not a reduction in latency. Rather, a reduction of response latencies only tended to occur in slower feedback loops. These observations were similar across sensory modalities (vision and proprioception). Our results may have implications regarding feedback control performance in athletic coaching. For instance, coaching methodologies that rely on reinforcement or 'reward shaping' may need to specifically target aspects of movement that rely on reward-sensitive feedback responses.

A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution

In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.

Transcranial magnetic stimulation on the dorsal premotor cortex facilitates human visuomotor adaptation

The premotor cortex is traditionally known to be involved in motor preparation and execution. More recently, evidence from neuroscience research shows that the dorsal premotor cortex (PMd) is also involved in sensory error-based motor adaptation and that invasive brain stimulation on PMd can attenuate adaptation in monkeys. The present study examines if adaptation can be modulated noninvasively in humans. Twenty-five healthy volunteers participated in a motor task in which rapid arm-reaching movements were made to hit a target, whereas the online cursor feedback about the hand position was visually rotated, inducing sensory error that drove motor adaptation. Transcranial magnetic stimulation (TMS) was delivered to PMd just before experiencing a sensory error, as in the previous study on monkeys. The degree of motor adaptation was measured as the change in the hand direction in response to the experienced error. TMS was found to increase adaptation compared with control conditions. Interestingly, the direction of modulation was opposite to the previous study on monkeys, which might originate from different methods and parameters of stimulation. The effect was also location-specific and was not a mere artifact of applying TMS because the facilitatory modulation occurred when stimulating PMd but not when stimulating the ventral premotor cortex, which was known for different roles and networks from PMd. Since noninvasive neuromodulation is a promising tool for research and clinical practice, the present study demonstrates that PMd is a feasible target region of neuromodulation to understand human motor adaptation and improve motor rehabilitation.

Selective modulation of cortical population dynamics during neuroprosthetic skill learning

Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.

Residual errors in visuomotor adaptation persist despite extended motor preparation periods

A consistent finding in sensorimotor adaptation is a persistent undershoot of full compensation, such that performance asymptotes with residual errors greater than seen at baseline. This behavior has been attributed to limiting factors within the implicit adaptation system, which reaches a suboptimal equilibrium between trial-by-trial learning and forgetting. However, recent research has suggested that allowing longer motor planning periods prior to movement eliminates these residual errors. The additional planning time allows required cognitive processes to be completed before movement onset, thus increasing accuracy. Here, we looked to extend these findings by investigating the relationship between increased motor preparation time and the size of imposed visuomotor rotation (30°, 45°, or 60°), with regard to the final asymptotic level of adaptation. We found that restricting preparation time to 0.35 s impaired adaptation for moderate and larger rotations, resulting in larger residual errors compared to groups with additional preparation time. However, we found that even extended preparation time failed to eliminate persistent errors, regardless of magnitude of cursor rotation. Thus, the asymptote of adaptation was significantly less than the degree of imposed rotation, for all experimental groups. In addition, there was a positive relationship between asymptotic error and implicit retention. These data suggest that a prolonged motor preparation period is insufficient to reliably achieve complete adaptation, and therefore, our results suggest that factors beyond that of planning time contribute to asymptotic adaptation levels.NEW & NOTEWORTHY Residual errors in sensorimotor adaptation are commonly attributed to an equilibrium between trial-by-trial learning and forgetting. Recent research suggested that allowing sufficient time for mental rotation eliminates these errors. In a number of experimental conditions, we show that although restricted motor preparation time does limit adaptation-consistent with mental rotation-extending preparation time fails to eliminate the residual errors in motor adaptation.

Cortical preparatory activity indexes learned motor memories

The brain's remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task¹ in a curl force field that elicited new muscle forces for some, but not all, movement directions^2,3. We found that in a neural subspace predictive of hand forces, changes in preparatory activity tracked the learned behavioural modifications and reassociated⁴ existing activity patterns with updated movements. Along a neural population dimension orthogonal to the force-predictive subspace, we discovered that preparatory activity shifted uniformly for all movement directions, including those unaltered by learning. During a washout period when the curl field was removed, preparatory activity gradually reverted in the force-predictive subspace, but the uniform shift persisted. These persistent preparatory activity patterns may retain a motor memory of the learned field^5,6 and support accelerated relearning of the same curl field. When a set of distinct curl fields was learned in sequence, we observed a corresponding set of field-specific uniform shifts which separated the associated motor memories in the neural state space^7-9. The precise geometry of these uniform shifts in preparatory activity could serve to index motor memories, facilitating the acquisition, retention and retrieval of a broad motor repertoire.

Direct and indirect cues can enable dual adaptation, but through different learning processes

Switching between motor tasks requires accurate adjustments for changes in dynamics (grasping a cup) or sensorimotor transformations (moving a computer mouse). Dual-adaptation studies have investigated how learning of context-dependent dynamics or transformations is enabled by sensory cues. However, certain cues, such as color, have shown mixed results. We propose that these mixed results may arise from two major classes of cues: "direct" cues, which are part of the dynamic state and "indirect" cues, which are not. We hypothesized that explicit strategies would primarily account for the adaptation of an indirect color cue but would be limited to simple tasks, whereas a direct visual separation cue would allow implicit adaptation regardless of task complexity. To test this idea, we investigated the relative contribution of implicit and explicit learning in relation to contextual cue type (colored or visually shifted workspace) and task complexity (1 or 8 targets) in a dual-adaptation task. We found that the visual workspace location cue enabled adaptation across conditions primarily through implicit adaptation. In contrast, we found that the color cue was largely ineffective for dual adaptation, except in a small subset of participants who appeared to use explicit strategies. Our study suggests that the previously inconclusive role of color cues in dual adaptation may be explained by differential contribution of explicit strategies across conditions.NEW & NOTEWORTHY We present evidence that learning of context-dependent dynamics proceeds via different processes depending on the type of sensory cue used to signal the context. Visual workspace location enabled learning different dynamics implicitly, presumably because it directly enters the dynamic state estimate. In contrast, a color cue was only successful where learners were apparently able to leverage explicit strategies to account for changed dynamics. This suggests a unification for the previously inconclusive role of color cues.

Cortical preparatory activity during motor learning reflects visuomotor retention deficits after punishment feedback

Previous studies have shown that reinforcement-based motor learning requires the brain to process feedback-related information after movement execution. However, whether reinforcement feedback changes how the brain processes motor preparation before movement execution is unclear. By using electroencephalography (EEG), this study investigates whether reinforcement feedback changes cortical preparatory activity to modulate motor learning and memory. Human subjects were divided in three groups [reward, punishment, control] to perform a visuomotor rotation task under different conditions that assess adaptation (learning) and retention (memory) during the task. Reinforcement feedback was provided in the form of points after each trial that signaled monetary gains (reward) or losses (punishment). EEG was utilized to evaluate the amplitude of movement readiness potentials (MRPs) at the beginning of each trial for each group during the adaptation and retention conditions of the task. The results show that punishment feedback significantly decreased MRPs amplitude during both task conditions compared to Reward and Control groups. Moreover, the punishment-related decrease in MRPs amplitude paralleled decreases in motor performance during the retention but not the adaptation condition. No changes in MRPs or motor performance were observed in the Reward group. These results support the idea that reinforcement feedback modulates motor preparation and suggest that changes in cortical preparatory activity contribute to the visuomotor retention deficits observed after punishment feedback.

Computation Through Neural Population Dynamics

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Measurement, manipulation and modeling of brain-wide neural population dynamics

Neural recording technologies increasingly enable simultaneous measurement of neural activity from multiple brain areas. To gain insight into distributed neural computations, a commensurate advance in experimental and analytical methods is necessary. We discuss two opportunities towards this end: the manipulation and modeling of neural population dynamics.

Prolonged response time helps eliminate residual errors in visuomotor adaptation

One persistent curiosity in visuomotor adaptation tasks is that participants often do not reach maximal performance. This incomplete asymptote has been explained as a consequence of obligatory computations within the implicit adaptation system, such as an equilibrium between learning and forgetting. A body of recent work has shown that in standard adaptation tasks, cognitive strategies operate alongside implicit learning. We reasoned that incomplete learning in adaptation tasks may primarily reflect a speed-accuracy tradeoff on time-consuming motor planning. Across three experiments, we find evidence supporting this hypothesis, showing that hastened motor planning may primarily lead to under-compensation. When an obligatory waiting period was administered before movement start, participants were able to fully counteract imposed perturbations (Experiment 1). Inserting the same delay between trials – rather than during movement planning – did not induce full compensation, suggesting that the motor planning interval influences the learning asymptote (Experiment 2). In the last experiment (Experiment 3), we asked participants to continuously report their movement intent. We show that emphasizing explicit re-aiming strategies (and concomitantly increasing planning time) also lead to complete asymptotic learning. Findings from all experiments support the hypothesis that incomplete adaptation is, in part, the result of an intrinsic speed-accuracy tradeoff, perhaps related to cognitive strategies that require parametric attentional reorienting from the visual target to the goal.

High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff

A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts’ law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration (MD) variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater MD variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts’ law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of ‘good’ control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.

Cortico-cerebellar interactions during goal-directed behavior

Preparatory activity is observed across multiple interconnected brain regions before goal-directed movement. Preparatory activity reflects discrete activity states representing specific future actions. It is unclear how this activity is mediated by multi-regional interactions. Recent evidence suggests that the cerebellum, classically associated with fine motor control, contributes to preparatory activity in the neocortex. We review recent advances and offer perspective on the function of cortico-cerebellar interactions during goal-directed behavior. We propose that the cerebellum learns to facilitate transitions between neocortical activity states. Transitions between activity states enable flexible and appropriately timed behavioral responses.

Motor training strengthens corticospinal suppression during movement preparation

Movement preparation involves a broad suppression in the excitability of the corticospinal pathway, a phenomenon called preparatory suppression. Here, we show that motor training strengthens preparatory suppression and that this strengthening is associated with faster reaction times. Our findings highlight a key role of preparatory suppression in training-driven behavioral improvements.

Preparatory suppression and facilitation of voluntary and involuntary responses to loud acoustic stimuli in an anticipatory timing task

In this study, we sought to characterize the effects of intense sensory stimulation on voluntary and involuntary behaviors at different stages of preparation for an anticipated action. We presented unexpected loud acoustic stimuli (LAS) at-rest and at three critical times during active movement preparation (-1,192, -392, and 0 ms relative to expected voluntary movement onset) to probe the state of the nervous system, and measured their effect on voluntary and involuntary motor actions (finger-press and eye-blink startle reflex, respectively). Voluntary responses were facilitated by LAS presented during active preparation, leading to earlier and more forceful responses compared to control and LAS at-rest. Notably, voluntary responses were significantly facilitated on trials where the LAS was presented early during preparation (-1,192 ms). Eye-blink reflexes to the LAS at -392 ms were significantly reduced and delayed compared to blinks elicited at other time-points, indicating suppression of sub-cortical excitability. However, voluntary responses on these trials were still facilitated by the LAS. The results provide insight into the mechanisms involved in preparing anticipatory actions. Induced activation can persist in the nervous system and can modulate subsequent actions for a longer time-period than previously thought, highlighting that movement preparation is a continuously evolving process that is susceptible to external influence throughout the preparation period. Suppression of sub-cortical excitability shortly before movement onset is consistent with previous work showing corticospinal suppression which may be a necessary step before the execution of any voluntary response.

Shared internal models for feedforward and feedback control of arm dynamics in non-human primates

Previous work has shown that humans account for and learn novel properties or the arm's dynamics, and that such learning causes changes in both the predictive (i.e., feedforward) control of reaching and reflex (i.e., feedback) responses to mechanical perturbations. Here we show that similar observations hold in old-world monkeys (Macaca fascicularis). Two monkeys were trained to use an exoskeleton to perform a single-joint elbow reaching and to respond to mechanical perturbations that created pure elbow motion. Both of these tasks engaged robust shoulder muscle activity as required to account for the torques that typically arise at the shoulder when the forearm rotates around the elbow joint (i.e., intersegmental dynamics). We altered these intersegmental arm dynamics by having the monkeys generate the same elbow movements with the shoulder joint either free to rotate, as normal, or fixed by the robotic manipulandum, which eliminates the shoulder torques caused by forearm rotation. After fixing the shoulder joint, we found a systematic reduction in shoulder muscle activity. In addition, after releasing the shoulder joint again, we found evidence of kinematic aftereffects (i.e., reach errors) in the direction predicted if failing to compensate for normal arm dynamics. We also tested whether such learning transfers to feedback responses evoked by mechanical perturbations and found a reduction in shoulder feedback responses, as appropriate for these altered arm intersegmental dynamics. Demonstrating this learning and transfer in non-human primates will allow the investigation of the neural mechanisms involved in feedforward and feedback control of the arm's dynamics.