Evolution, biomechanics, and neurobiology converge to explain selective finger motor control.

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity.

Cortically Evoked Movement in Humans Reflects History of Prior Executions, Not Plan for Upcoming Movement.

Human motor behavior involves planning and execution of actions, some more frequently. Manipulating probability distribution of a movement through intensive direction-specific repetition causes physiological bias toward that direction, which can be cortically evoked by transcranial magnetic stimulation (TMS). However, because evoked movement has not been used to distinguish movement execution and plan histories to date, it is unclear whether the bias is because of frequently executed movements or recent planning of movement. Here, in a cohort of 40 participants (22 female), we separately manipulate the recent history of movement plans and execution and probe the resulting effects on physiological biases using TMS and on the default plan for goal-directed actions using a timed-response task. Baseline physiological biases shared similar low-level kinematic properties (direction) to a default plan for upcoming movement. However, manipulation of recent execution history via repetitions toward a specific direction significantly affected physiological biases, but not plan-based goal-directed movement. To further determine whether physiological biases reflect ongoing motor planning, we biased plan history by increasing the likelihood of a specific target location and found a significant effect on the default plan for goal-directed movements. However, TMS-evoked movement during preparation did not become biased toward the most frequent plan. This suggests that physiological biases may either provide a readout of the default state of primary motor cortex population activity in the movement-related space, but not ongoing neural activation in the planning-related space, or that practice induces sensitization of neurons involved in the practiced movement, calling into question the relevance of cortically evoked physiological biases to voluntary movements.SIGNIFICANCE STATEMENT Human motor performance depends not only on ability to make movements relevant to the environment/body's current state, but also on recent action history. One emerging approach to study recent movement history effects on the brain is via physiological biases in cortically-evoked involuntary movements. However, because prior movement execution and plan histories were indistinguishable to date, to what extent physiological biases are due to pure execution-dependent history, or to prior planning of the most probable action, remains unclear. Here, we show that physiological biases are profoundly affected by recent movement execution history, but not ongoing movement planning. Evoked movement, therefore, provides a readout of the default state within the movement space, but not of ongoing activation related to voluntary movement planning.

Generalization indicates asymmetric and interactive control networks for multi-finger dexterous movements.

Finger dexterity is manifested by coordinated patterns of muscle activity and generalization of learning across contexts. Some fingers flex, others extend, and some are immobile. Whether or not the neural control processes of these direction-specific actions are independent remains unclear. We characterized behavioral principles underlying learning and generalization of dexterous flexion and extension movements, within and across hands, using an isometric dexterity task that precisely measured finger individuation, force accuracy, and temporal synchronization. Two cohorts of participants trained for 3 days in either the flexion or extension direction. All dexterity measures in both groups showed post-training improvement, although finger extension exhibited inferior dexterity. Surprisingly, learning of finger extension generalized to the untrained flexion direction, but not vice versa. This flexion bias was also evident in the untrained hand. Our study indicates direction-specific control circuits for learning of finger flexion and extension that interact by partially, but asymmetrically, transferring between directions.

No Polarity-specific Modulation of Prefrontal-to-M1 Interhemispheric Inhibition by Transcranial Direct Current Stimulation Over the Lateral Prefrontal Cortex.

The lateral prefrontal cortex (PFC) plays a variety of crucial roles in higher-order cognitive functions. Previous works have attempted to modulate lateral PFC function by applying non-invasive transcranial direct current stimulation (tDCS) and demonstrated positive effects on performance of tasks involving cognitive processes. The neurophysiological underpinning of the stimulation effects, however, remain poorly understood. Here, we explored the neurophysiological after-effects of tDCS over the lateral PFC by assessing changes in the magnitude of interhemispheric inhibition from the lateral PFC to the contralateral primary motor cortex (PFC-M1 IHI). Using a dual-site transcranial magnetic stimulation paradigm, we assessed PFC-M1 IHI before and after the application of tDCS over the right lateral PFC. We conducted a double-blinded, crossover, and counterbalanced design where 15 healthy volunteers participated in three sessions during which they received either anodal, cathodal, and sham tDCS. In order to determine whether PFC-M1 IHI could be modulated at all, we completed the same assessment on a separate group of 15 participants as they performed visuo-motor reaction tasks that likely engage the lateral PFC. The results showed that tDCS over the right lateral PFC did not modulate the magnitude of PFC-M1 IHI, whereas connectivity changed when Go/NoGo decisions were implemented in reactions during the motor tasks. Although PFC-M1 IHI is sensitive enough to be modulated by behavioral manipulations, tDCS over the lateral PFC does not have substantial modulatory effects on PFC to M1 functional connectivity, or at least not to the degree that can be detected with this measure.

Separation of multiple motor memories through implicit and explicit processes.

Acquisition of multiple motor skills without interference is a remarkable ability in daily life. During adaptation to opposing perturbations, a common paradigm to study this ability, each perturbation can be successfully learned when a contextual follow-through movement is associated with the direction of the perturbation. It is still unclear, however, to what extent this learning engages the cognitive explicit process and the implicit process. Here, we untangled the individual contributions of the explicit and implicit components while participants learned opposing visuomotor perturbations, with a second unperturbed follow-through movement. In experiment 1, we replicated previous adaptation results and showed that follow-through movements also allow learning for opposing visuomotor rotations. For one group of participants in experiment 2, we isolated strategic explicit learning, while for another group we isolated the implicit component. Our data showed that opposing perturbations could be fully learned by explicit strategies, but when strategy was restricted, distinct implicit processes contributed to learning. In experiment 3, we examined whether learning is influenced by the disparity between the follow-through contexts. We found that the location of follow-through targets had little effect on total learning, yet it led to more instances in which participants failed to learn the task. In experiment 4, we explored the generalization capability to untrained targets. Participants showed near-flat generalization of the implicit and explicit processes. Overall, our results indicate that follow-through contextual cues might activate, in part, top-down cognitive factors that influence not only the dynamics of the explicit learning but also the implicit process.NEW & NOTEWORTHY Acquisition of multiple motor skills is a remarkable ability in everyday activities. Yet, the contribution of implicit and explicit learning processes during learning of multiple motor skills is still unclear. We sought to dissociate this ability during learning opposing visuomotor rotations, each associated with a contextual follow-through movement. We show that follow-through contextual cues influence activity of both implicit and explicit processes, suggesting that context-based top-down cognitive factors influence not only the explicit learning but also the implicit process.

Pushing the Rehabilitation Boundaries: Hand Motor Impairment Can Be Reduced in Chronic Stroke.

Background. Stroke is one of the most common causes of physical disability worldwide. The majority of survivors experience impairment of movement, often with lasting deficits affecting hand dexterity. To date, conventional rehabilitation primarily focuses on training compensatory maneuvers emphasizing goal completion rather than targeting reduction of motor impairment. Objective. We aim to determine whether finger dexterity impairment can be reduced in chronic stroke when training on a task focused on moving fingers against abnormal synergies without allowing for compensatory maneuvers. Methods. We recruited 18 chronic stroke patients with significant hand motor impairment. First, participants underwent baseline assessments of hand function, impairment, and finger individuation. Then, participants trained for 5 consecutive days, 3 to 4 h/d, on a multifinger piano-chord-like task that cannot be performed by compensatory actions of other body parts (e.g., arm). Participants had to learn to simultaneously coordinate and synchronize multiple fingers to break unwanted flexor synergies. To test generalization, we assessed performance in trained and nontrained chords and clinical measures in both the paretic and the nonparetic hands. To evaluate retention, we repeated the assessments 1 day, 1 week, and 6 months post-training. Results. Our results showed that finger impairment assessed by the individuation task was reduced after training. The reduction of impairment was accompanied by improvements in clinical hand function, including precision pinch. Notably, the effects were maintained for 6 months following training. Conclusion. Our findings provide preliminary evidence that chronic stroke patient can reduce hand impairment when training against abnormal flexor synergies, a change that was associated with meaningful clinical benefits.

Locomotor Adaptation Is Associated with Microstructural Properties of the Inferior Cerebellar Peduncle.

In sensorimotor adaptation paradigms, participants learn to adjust their behavior in response to an external perturbation. Locomotor adaptation and reaching adaptation depend on the cerebellum and are accompanied by changes in functional connectivity in cortico-cerebellar circuits. In order to gain a better understanding of the particular cerebellar projections involved in locomotor adaptation, we assessed the contribution of specific white matter pathways to the magnitude of locomotor adaptation and to long-term motor adaptation effects (recall and relearning). Diffusion magnetic resonance imaging with deterministic tractography was used to delineate the inferior and superior cerebellar peduncles (ICP, SCP) and the corticospinal tract (CST). Correlations were calculated to assess the association between the diffusivity values along the tracts and behavioral measures of locomotor adaptation. The results point to a significant correlation between the magnitude of adaptation and diffusivity values in the left ICP. Specifically, a higher magnitude of adaptation was associated with higher mean diffusivity and with lower anisotropy values in the left ICP, but not in other pathways. Post hoc analysis revealed that the effect stems from radial, not axial, diffusivity. The magnitude of adaptation was further associated with the degree of ICP lateralization, such that greater adaptation magnitude was correlated with increased rightward asymmetry of the ICP. Our findings suggest that the magnitude of locomotor adaptation depends on afferent signals to the cerebellum, transmitted via the ICP, and point to the contribution of error detection to locomotor adaptation rate.

Interactions between motor exploration and reinforcement learning.

Motor exploration, a trial-and-error process in search for better motor outcomes, is known to serve a critical role in motor learning. This is particularly relevant during reinforcement learning, where actions leading to a successful outcome are reinforced while unsuccessful actions are avoided. Although early on motor exploration is beneficial to finding the correct solution, maintaining high levels of exploration later in the learning process might be deleterious. Whether and how the level of exploration changes over the course of reinforcement learning, however, remains poorly understood. Here we evaluated temporal changes in motor exploration while healthy participants learned a reinforcement-based motor task. We defined exploration as the magnitude of trial-to-trial change in movements as a function of whether the preceding trial resulted in success or failure. Participants were required to find the optimal finger-pointing direction using binary feedback of success or failure. We found that the magnitude of exploration gradually increased over time when participants were learning the task. Conversely, exploration remained low in participants who were unable to correctly adjust their pointing direction. Interestingly, exploration remained elevated when participants underwent a second training session, which was associated with faster relearning. These results indicate that the motor system may flexibly upregulate the extent of exploration during reinforcement learning as if acquiring a specific strategy to facilitate subsequent learning. Also, our findings showed that exploration affects reinforcement learning and vice versa, indicating an interactive relationship between them. Reinforcement-based tasks could be used as primers to increase exploratory behavior leading to more efficient subsequent learning.NEW & NOTEWORTHY Motor exploration, the ability to search for the correct actions, is critical to learning motor skills. Despite this, whether and how the level of exploration changes over the course of training remains poorly understood. We showed that exploration increased and remained high throughout training of a reinforcement-based motor task. Interestingly, elevated exploration persisted and facilitated subsequent learning. These results suggest that the motor system upregulates exploration as if learning a strategy to facilitate subsequent learning.

Movement Repetition Facilitates Response Preparation.

Our sensorimotor system appears to be influenced by the recent history of our movements. Repeating movements toward a particular direction is known to have a dramatic effect on involuntary movements elicited by cortical stimulation-a phenomenon that has been termed use-dependent plasticity. However, analogous effects of repetition on behavior have proven elusive. Here, we show that movement repetition enhances the generation of similar movements in the future by reducing the time required to select and prepare the repeated movement. We further show that this reaction time advantage for repeated movements is attributable to more rapid, but still flexible, preparation of the repeated movement rather than anticipation and covert advance preparation of the previously repeated movement. Our findings demonstrate a powerful and beneficial effect of movement repetition on response preparation, which may represent a behavioral counterpart to use-dependent plasticity effects in primary motor cortex.

Learning Similar Actions by Reinforcement or Sensory-Prediction Errors Rely on Distinct Physiological Mechanisms.

Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning.

Representing delayed force feedback as a combination of current and delayed states.

To adapt to deterministic force perturbations that depend on the current state of the hand, internal representations are formed to capture the relationships between forces experienced and motion. However, information from multiple modalities travels at different rates, resulting in intermodal delays that require compensation for these internal representations to develop. To understand how these delays are represented by the brain, we presented participants with delayed velocity-dependent force fields, i.e., forces that depend on hand velocity either 70 or 100 ms beforehand. We probed the internal representation of these delayed forces by examining the forces the participants applied to cope with the perturbations. The findings showed that for both delayed forces, the best model of internal representation consisted of a delayed velocity and current position and velocity. We show that participants relied initially on the current state, but with adaptation, the contribution of the delayed representation to adaptation increased. After adaptation, when the participants were asked to make movements with a higher velocity for which they had not previously experienced with the delayed force field, they applied forces that were consistent with current position and velocity as well as delayed velocity representations. This suggests that the sensorimotor system represents delayed force feedback using current and delayed state information and that it uses this representation when generalizing to faster movements.NEW & NOTEWORTHY The brain compensates for forces in the body and the environment to control movements, but it is unclear how it does so given the inherent delays in information transmission and processing. We examined how participants cope with delayed forces that depend on their arm velocity 70 or 100 ms beforehand. After adaptation, participants applied opposing forces that revealed a partially correct representation of the perturbation using the current and the delayed information.

Motor Learning Enhances Use-Dependent Plasticity.

Motor behaviors are shaped not only by current sensory signals but also by the history of recent experiences. For instance, repeated movements toward a particular target bias the subsequent movements toward that target direction. This process, called use-dependent plasticity (UDP), is considered a basic and goal-independent way of forming motor memories. Most studies consider movement history as the critical component that leads to UDP (Classen et al., 1998; Verstynen and Sabes, 2011). However, the effects of learning (i.e., improved performance) on UDP during movement repetition have not been investigated. Here, we used transcranial magnetic stimulation in two experiments to assess plasticity changes occurring in the primary motor cortex after individuals repeated reinforced and nonreinforced actions. The first experiment assessed whether learning a skill task modulates UDP. We found that a group that successfully learned the skill task showed greater UDP than a group that did not accumulate learning, but made comparable repeated actions. The second experiment aimed to understand the role of reinforcement learning in UDP while controlling for reward magnitude and action kinematics. We found that providing subjects with a binary reward without visual feedback of the cursor led to increased UDP effects. Subjects in the group that received comparable reward not associated with their actions maintained the previously induced UDP. Our findings illustrate how reinforcing consistent actions strengthens use-dependent memories and provide insight into operant mechanisms that modulate plastic changes in the motor cortex.SIGNIFICANCE STATEMENT Performing consistent motor actions induces use-dependent plastic changes in the motor cortex. This plasticity reflects one of the basic forms of human motor learning. Past studies assumed that this form of learning is exclusively affected by repetition of actions. However, here we showed that success-based reinforcement signals could affect the human use-dependent plasticity (UDP) process. Our results indicate that learning augments and interacts with UDP. This effect is important to the understanding of the interplay between the different forms of motor learning and suggests that reinforcement is not only important to learning new behaviors, but can shape our subsequent behavior via its interaction with UDP.

Formation of Long-Term Locomotor Memories Is Associated with Functional Connectivity Changes in the Cerebellar-Thalamic-Cortical Network.

Although motor adaptation is typically rapid, accumulating evidence shows that it is also associated with long-lasting behavioral and neuronal changes. Two processes were suggested to explain the formation of long-term motor memories: recall, reflecting a retrieval of previous motor actions, and faster relearning, reflecting an increased sensitivity to errors. Although these manifestations of motor memories were initially demonstrated in the context of adaptation experiments in reaching, indications of long-term motor memories were also demonstrated recently in other kinds of adaptation such as in locomotor adaptation. Little is known about the neural processes that underlie these distinct aspects of memory. We hypothesize that recall and faster relearning reflect different learning processes that operate at the same time and depend on different neuronal networks. Seventeen subjects performed a multisession locomotor adaptation experiment in the laboratory, together with resting-state and localizer fMRI scans, after the baseline and the locomotor adaptation sessions. We report a modulation of the cerebellar-thalamic-cortical and cerebellar-basal ganglia networks after locomotor adaptation. Interestingly, whereas thalamic-cortical baseline connectivity was correlated with recall, cerebellar-thalamic baseline connectivity was correlated with faster relearning. Our results suggest that separate neuronal networks underlie error sensitivity and retrieval components. Individual differences in baseline resting-state connectivity can predict idiosyncratic combination of these components. SIGNIFICANCE STATEMENT: The ability to shape our motor behavior rapidly in everyday activity, such as when walking on sand, suggests the existence of long-term motor memories. It was suggested recently that this ability is achieved by the retrieval of previous motor actions and by enhanced relearning capacity. Little is known about the neural mechanisms that underlie these memory processes. We studied the modularity in long-term motor memories in the context of locomotor adaptation using resting-state fMRI. We show that retrieval and relearning effects are associated with separate locomotor control networks and that intersubject variability in learning and in the generation of motor memories could be predicted from baseline resting-state connectivity in locomotor-related networks.

Stimulation of PPC Affects the Mapping between Motion and Force Signals for Stiffness Perception But Not Motion Control.

How motion and sensory inputs are combined to assess an object's stiffness is still unknown. Here, we provide evidence for the existence of a stiffness estimator in the human posterior parietal cortex (PPC). We showed previously that delaying force feedback with respect to motion when interacting with an object caused participants to underestimate its stiffness. We found that applying theta-burst transcranial magnetic stimulation (TMS) over the PPC, but not the dorsal premotor cortex, enhances this effect without affecting movement control. We explain this enhancement as an additional lag in force signals. This is the first causal evidence that the PPC is not only involved in motion control, but also has an important role in perception that is disassociated from action. We provide a computational model suggesting that the PPC integrates position and force signals for perception of stiffness and that TMS alters the synchronization between the two signals causing lasting consequences on perceptual behavior. SIGNIFICANCE STATEMENT: When selecting an object such as a ripe fruit or sofa, we need to assess the object's stiffness. Because we lack dedicated stiffness sensors, we rely on an as yet unknown mechanism that generates stiffness percepts by combining position and force signals. Here, we found that the posterior parietal cortex (PPC) contributes to combining position and force signals for stiffness estimation. This finding challenges the classical view about the role of the PPC in regulating position signals only for motion control because we highlight a key role of the PPC in perception that is disassociated from action. Altogether this sheds light on brain mechanisms underlying the interaction between action and perception and may help in the development of better teleoperation systems and rehabilitation of patients with sensory impairments.

Increased Adaptation Rates and Reduction in Trial-by-Trial Variability in Subjects with Cerebral Palsy Following a Multi-session Locomotor Adaptation Training.

Cerebral Palsy (CP) results from an insult to the developing brain and is associated with deficits in locomotor and manual skills and in sensorimotor adaptation. We hypothesized that the poor sensorimotor adaptation in persons with CP is related to their high execution variability and does not reflect a general impairment in adaptation learning. We studied the interaction between performance variability and adaptation deficits using a multi-session locomotor adaptation design in persons with CP. Six adolescents with diplegic CP were exposed, during a period of 15 weeks, to a repeated split-belt treadmill perturbation spread over 30 sessions and were tested again 6 months after the end of training. Compared to age-matched healthy controls, subjects with CP showed poor adaptation and high execution variability in the first exposure to the perturbation. Following training they showed marked reduction in execution variability and an increase in learning rates. The reduction in variability and the improvement in adaptation were highly correlated in the CP group and were retained 6 months after training. Interestingly, despite reducing their variability in the washout phase, subjects with CP did not improve learning rates during washout phases that were introduced only four times during the experiment. Our results suggest that locomotor adaptation in subjects with CP is related to their execution variability. Nevertheless, while variability reduction is generalized to other locomotor contexts, the development of savings requires both reduction in execution variability and multiple exposures to the perturbation.

Reward gain model describes cortical use-dependent plasticity.

Consistent repetitions of an action lead to plastic change in the motor cortex and cause shift in the direction of future movements. This process is known as use-dependent plasticity (UDP), one of the basic forms of the motor memory. We have recently demonstrated in a physiological study that success-related reinforcement signals could modulate the strength of UDP. We tested this idea by developing a computational approach that modeled the shift in the direction of future action as a change in preferred direction of population activity of neurons in the primary motor cortex. The rate of the change follows a modified temporal difference reinforcement learning algorithm, in which the learning policy is based on comparison between what reward the population experiences on a particular trial, and what it had expected on the basis of its previous learning. By using this model, we were able to characterize the nature of learning and retention of UDP. Exploring the relationship between reinforcement and UDP constitutes a crucial step toward understanding the basic blocks involved in the formation of motor memories.

Savings in locomotor adaptation explained by changes in learning parameters following initial adaptation.

Faster relearning of an external perturbation, savings, offers a behavioral linkage between motor learning and memory. To explain savings effects in reaching adaptation experiments, recent models suggested the existence of multiple learning components, each shows different learning and forgetting properties that may change following initial learning. Nevertheless, the existence of these components in rhythmic movements with other effectors, such as during locomotor adaptation, has not yet been studied. Here, we study savings in locomotor adaptation in two experiments; in the first, subjects adapted to speed perturbations during walking on a split-belt treadmill, briefly adapted to a counter-perturbation and then readapted. In a second experiment, subjects readapted after a prolonged period of washout of initial adaptation. In both experiments we find clear evidence for increased learning rates (savings) during readaptation. We show that the basic error-based multiple timescales linear state space model is not sufficient to explain savings during locomotor adaptation. Instead, we show that locomotor adaptation leads to changes in learning parameters, so that learning rates are faster during readaptation. Interestingly, we find an intersubject correlation between the slow learning component in initial adaptation and the fast learning component in the readaptation phase, suggesting an underlying mechanism for savings. Together, these findings suggest that savings in locomotion and in reaching may share common computational and neuronal mechanisms; both are driven by the slow learning component and are likely to depend on cortical plasticity.

Kinetic adaptation during locomotion on a split-belt treadmill.

It has been suggested that a feedforward control mechanism drives the adaptation of the spatial and temporal interlimb locomotion variables. However, the internal representation of limb kinetics during split-belt locomotion has not yet been studied. In hand movements, it has been suggested that kinetic and kinematic parameters are controlled by separate neural processes; therefore, it is possible that separate neural processes are responsible for kinetic and kinematic locomotion parameters. In the present study, we assessed the adaptation of the limb kinetics by analyzing the ground reaction forces (GRFs) as well as the center of pressure (COP) during adaptation to speed perturbation, using a split-belt treadmill with an integrated force plate. We found that both the GRF of each leg at initial contact and the COP changed gradually and showed motor aftereffects during early postadaptation, suggesting the use of a feedforward predictive mechanism. However, the GRF of each leg in the single-support period used a feedback control mechanism. It changed rapidly during the adaptation phase and showed no motor aftereffect when the speed perturbation was removed. Finally, we found that the motor adaptation of the GRF and the COP are mediated by a dual-rate process. Our results suggest two important contributions to neural control of locomotion. First, different control mechanisms are responsible for forces at single- and double-support periods, as previously reported for kinematic variables. Second, our results suggest that motor adaptation during split-belt locomotion is mediated by fast and slow adaptation processes.

Adaptation to sequence force perturbation during vertical and horizontal reaching movement-averaging the past or predicting the future?

Several studies conducted during the past decade have suggested that episodic memory is better equipped to handle the future than the past. Here, we consider this premise in the context of motor memory. State-of-the-art computational models for trial-by-trial motor adaptation to constant and stochastic force field perturbations in a horizontal reaching paradigm have shown that motor memory registers a weighted sum of past experiences to predict force perturbation in a subsequent trial. In the current study, we used the standard horizontal reaching movement paradigm and a novel vertical reaching movement paradigm to test motor memory function during adaptation to force fields increasing in magnitude in a simple predictable linear series. We found that adaptation to constant and sequence force fields are similar in vertical and horizontal reaching. For both horizontal and vertical reaching, we found that the expectation in a particular trial was the average of the previous few trials rather than an expectation of a larger perturbation, as would be expected from a simple extrapolation. These findings are not consistent with those of our previous studies on lifting and grasping tasks, in which we found that the grip force is correctly adjusted to the next weight in a series of tasks with gradually increasing weights, thus predicting the future rather than averaging the past. The results of the current study devoted to reaching movements and of our previous study addressing a lifting task suggest that the brain can generate at least two different types of motor representation, either addressing the past in reaching or predicting the future in lifting. We propose that prior experience and the effect of environment's variability are the reasons for the observed differences in expectation during lifting and reaching. Finally, we discuss these two types of memory mechanisms with respect to the distinct neural circuits responsible for lifting and reaching.