The cerebellum, internal models and prediction in 'non-motor' aspects of language: A critical review

The emergence of studies on cerebellar contributions in 'non-motor' aspects of predictive language processing has long been awaited by researchers investigating the neural foundations of language and cognition. Despite (i) progress in research implicating the cerebellum in language processing, (ii) the widely-accepted nature of the uniform, multi-modal computation that the cerebellum implements in the form of internal models, as well as (iii) the long tradition of psycholinguistic studies addressing prediction mechanisms, research directly addressing cerebellar contributions to 'non-motor' predictive language processing has only surfaced in the last five years. This paper provides the first review of this novel field, along with a critical assessment of the studies conducted so far. While encouraging, the evidence for cerebellar involvement in 'non-motor' aspects of predictive language processing remains inconclusive under further scrutiny. Future directions are finally discussed with respect to outstanding questions in this novel field of research.

The sequencing process generated by the cerebellum crucially contributes to social interactions

The capacity to understand another person's emotions, intentions, beliefs and personality traits, based on observed or communicated behaviors, is termed social cognition. During the last decade, social neuroscience has made great progress in understanding the neural correlates of social cognition. However, because the cerebellum is traditionally viewed as only involved in motor processing, the contribution of this major part of the brain in social processing has been largely ignored and its specific role in social cognition remains unclear. Nevertheless, recent meta-analyses have made its crucial contribution to social cognition evident. This raises the question: What is the exact function of the cerebellum in social cognition? We hypothesize that the cerebellum builds internal action models of our social inter-actions to predict how other people's actions will be executed, what our most likely responses are to these actions, so that we can automatize our interactions and instantly detect disruptions in these action sequences. This mechanism likely allows to better anticipate action sequences during social interactions in an automatic and intuitive way and to fine-tune these anticipations, making it easier to understand behaviors and to detect violations. This hypothesis has major implications in neurological disorders affecting the cerebellum such as autism, with detrimental effects on social functionality, especially on more complex and abstract social cognitive processes. Because the fundamental anatomical organization of the cerebellum is identical in many species (cerebellar microcomplexes), this hypothesis could have major impacts to elucidate social interactions in social animals.

Reciprocal information flow and role distribution support joint action coordination

Many joint actions require task partners to temporally coordinate actions that follow different spatial patterns. This creates the need to find trade-offs between temporal coordination and spatial alignment. To study coordination under incongruent spatial and temporal demands, we devised a novel coordination task that required task partners to synchronize their actions while tracing different shapes that implied conflicting velocity profiles. In three experiments, we investigated whether coordination under incongruent demands is best achieved through mutually coupled predictions or through a clear role distribution with only one task partner adjusting to the other. Participants solved the task of trading off spatial and temporal coordination demands equally well when mutually perceiving each other’s actions without any role distribution, and when acting in a leader-follower configuration where the leader was unable to see the follower’s actions. Coordination was significantly worse when task partners who had been assigned roles could see each other’s actions. These findings make three contributions to our understanding of coordination mechanisms in joint action. First, they show that mutual prediction facilitates coordination under incongruent demands, demonstrating the importance of coupled predictive models in a wide range of coordination contexts. Second, they show that mutual alignment of velocity profiles in the absence of a leader-follower dynamic is more wide-spread than previously thought. Finally, they show that role distribution can result in equally effective coordination as mutual prediction without role assignment, provided that the role distribution is not arbitrarily imposed but determined by (lack of) perceptual access to a partner’s actions.

Development of interactions between sensorimotor representations in school-aged children

Reliable sensory-motor integration is a pre-requisite for optimal movement control; the functionality of this integration changes during development. Previous research has shown that motor performance of school-age children is characterized by higher variability, particularly under conditions where vision is not available, and movement planning and control is largely based on kinesthetic input. The purpose of the current study was to determine the characteristics of how kinesthetic-motor internal representations interact with visuo-motor representations during development. To this end, we induced a visuo-motor adaptation in 59 children, ranging from 5 to 12years of age, as well as in a group of adults, and measured initial directional error (IDE) and endpoint error (EPE) during a subsequent condition where visual feedback was not available, and participants had to rely on kinesthetic input. Our results show that older children (age range 9-12years) de-adapted significantly more than younger children (age range 5-8years) over the course of 36 trials in the absence of vision, suggesting that the kinesthetic-motor internal representation in the older children was utilized more efficiently to guide hand movements, and was comparable to the performance of the adults.

Auditory feedback perturbation in children with developmental speech sound disorders

BACKGROUND/PURPOSE

Several studies indicate a close relation between auditory and speech motor functions in children with speech sound disorders (SSD). The aim of this study was to investigate the ability to compensate and adapt for perturbed auditory feedback in children with SSD compared to age-matched normally developing children.

METHOD

17 normally developing children aged 4.1-8.7 years (mean=5.5, SD=1.4), and 11 children with SSD aged 3.9-7.5 years (mean=5.1, SD=1.0) participated in the study. Auditory feedback was perturbed by real-time shifting the first and second formant of the vowel /e/ during the production of CVC words in a five-step paradigm (practice/familiarization; start/baseline; ramp; hold; end/release).

RESULTS

At the group level, the normally developing children were better able to compensate and adapt, adjusting their formant frequencies in the direction opposite to the perturbation, while the group of children with SSD followed (amplifying) the perturbation. However, large individual differences lie underneath. Furthermore, strong correlations were found between the amount of compensation and performance on oral motor movement non-word repetition tasks.

CONCLUSIONS

Results suggested that while most children with SSD can detect incongruencies in auditory feedback and can adapt their target representations, they are unable to compensate for perturbed auditory feedback. These findings suggest that impaired auditory-motor integration may play a key role in SSD.

LEARNING OUTCOMES

The reader will be able to: (1) describe the potential role of auditory feedback control in developmental speech disorders (SSD); (2) identify the neural control subsystems involved in feedback based speech motor control; (3) describe the differences between compensation and adaptation for perturbed auditory feedback; (4) explain why auditory-motor integration may play a key role in SSD.

Mathematical models for dynamic, multisensory spatial orientation perception

Mathematical models have been proposed for how the brain interprets sensory information to produce estimates of self-orientation and self-motion. This process, spatial orientation perception, requires dynamically integrating multiple sensory modalities, including visual, vestibular, and somatosensory cues. Here, we review the progress in mathematical modeling of spatial orientation perception, focusing on dynamic multisensory models, and the experimental paradigms in which they have been validated. These models are primarily "black box" or "as if" models for how the brain processes spatial orientation cues. Yet, they have been effective scientifically, in making quantitative hypotheses that can be empirically assessed, and operationally, in investigating aircraft pilot disorientation, for example. The primary family of models considered, the observer model, implements estimation theory approaches, hypothesizing that internal models (i.e., neural systems replicating the behavior/dynamics of physical systems) are used to produce expected sensory measurements. Expected signals are then compared to actual sensory afference, yielding sensory conflict, which is weighted to drive central perceptions of gravity, angular velocity, and translation. This approach effectively predicts a wide range of experimental scenarios using a small set of fixed free parameters. We conclude with limitations and applications of existing mathematical models and important areas of future work.

Behavioral characterization of prediction and internal models in adolescents with autistic spectrum disorders

Autism has been considered as a deficit in prediction of the upcoming event or of the sensory consequences of our own movements. To test this hypothesis, we recorded eye movements from high-functioning autistic adolescents and from age-matched controls during a blanking paradigm. In this paradigm, adolescents were instructed to follow a moving target with their eyes even during its transient disappearance. Given the absence of visual information during the blanking period, eye movements during this period are solely controlled on the basis of the prediction of the ongoing target motion. Typical markers of predictive eye movements such as the number and accuracy of predictive saccades and the predictive reacceleration before target reappearance were identical in the two populations. In addition, the synergy of predictive saccades and smooth pursuit observed during the blanking periods, which is a marker for the quality of internal models about target/eye motions, was comparable between these two populations. These results suggest that, in our large population of high-functioning autistic adolescent, both predictive abilities and internal models are left intact in Autism, at least for low-level sensorimotor transformations.

Motor invariants in action execution and perception

The nervous system is sensitive to statistical regularities of the external world and forms internal models of these regularities to predict environmental dynamics. Given the inherently social nature of human behavior, being capable of building reliable predictive models of others' actions may be essential for successful interaction. While social prediction might seem to be a daunting task, the study of human motor control has accumulated ample evidence that our movements follow a series of kinematic invariants, which can be used by observers to reduce their uncertainty during social exchanges. Here, we provide an overview of the most salient regularities that shape biological motion, examine the role of these invariants in recognizing others' actions, and speculate that anchoring socially-relevant perceptual decisions to such kinematic invariants provides a key computational advantage for inferring conspecifics' goals and intentions.

The Role of the Cerebellar and Vestibular Networks in Anxiety Disorders and Depression: the Internal Model Hypothesis

Clinical data and animal studies confirmed that the cerebellum and the vestibular system are involved in emotions. Nowadays, no real consensus has really emerged to explain the clinical symptoms in humans and behavioral deficits in the animal models. We envisage here that the cerebellum and the vestibular system play complementary roles in emotional reactivity. The cerebellum integrates a large variety of exteroceptive and proprioceptive information necessary to elaborate and to update the internal model: in emotion, as in motor processes, it helps our body and self to adapt to the environment, and to anticipate any changes in such environment in order to produce a time-adapted response. The vestibular system provides relevant environmental stimuli (i.e., gravity, self-position, and movement) and is involved in self-perception. Consequently, cerebellar or vestibular disorders could generate « internal fake news» (due to lack or false sensory information and/or integration) that could, in turn, generate potential internal model deficiencies. In this case, the alterations provoke false anticipation of motor command and external sensory feedback, associated with unsuited behaviors. As a result, the individual becomes progressively unable to cope with the environmental solicitation. We postulate that chronically unsuited, and potentially inefficient, behavioral and visceral responses to environmental solicitations lead to stressful situations. Furthermore, this inability to adapt to the context of the situation generates chronic anxiety which could precede depressive states.

How music training enhances working memory: a cerebrocerebellar blending mechanism that can lead equally to scientific discovery and therapeutic efficacy in neurological disorders

Background

Following in the vein of studies that concluded that music training resulted in plastic changes in Einstein’s cerebral cortex, controlled research has shown that music training (1) enhances central executive attentional processes in working memory, and (2) has also been shown to be of significant therapeutic value in neurological disorders. Within this framework of music training-induced enhancement of central executive attentional processes, the purpose of this article is to argue that: (1) The foundational basis of the central executive begins in infancy as attentional control during the establishment of working memory, (2) In accordance with Akshoomoff, Courchesne and Townsend’s and Leggio and Molinari’s cerebellar sequence detection and prediction models, the rigors of volitional control demands of music training can enhance voluntary manipulation of information in thought and movement, (3) The music training-enhanced blending of cerebellar internal models in working memory as can be experienced as intuition in scientific discovery (as Einstein often indicated) or, equally, as moments of therapeutic advancement toward goals in the development of voluntary control in neurological disorders, and (4) The blending of internal models as in (3) thus provides a mechanism by which music training enhances central executive processes in working memory that can lead to scientific discovery and improved therapeutic outcomes in neurological disorders.

Results

Within the framework of Leggio and Molinari’s cerebellar sequence detection model, it is determined that intuitive steps forward that occur in both scientific discovery and during therapy in those with neurological disorders operate according to the same mechanism of adaptive error-driven blending of cerebellar internal models.

Conclusion

It is concluded that the entire framework of the central executive structure of working memory is a product of the cerebrocerebellar system which can, through the learning of internal models, incorporate the multi-dimensional rigor and volitional-control demands of music training and, thereby, enhance voluntary control. It is further concluded that this cerebrocerebellar view of the music training-induced enhancement of central executive control in working memory provides a needed mechanism to explain both the highest level of scientific discovery and the efficacy of music training in the remediation of neurological impairments.

Nine-month-old infants update their predictive models of a changing environment

Humans generate internal models of their environment to predict events in the world. As the environments change, our brains adjust to these changes by updating their internal models. Here, we investigated whether and how 9-month-old infants differentially update their models to represent a dynamic environment. Infants observed a predictable sequence of stimuli, which were interrupted by two types of cues. Following the update cue, the pattern was altered, thus, infants were expected to update their predictions for the upcoming stimuli. Because the pattern remained the same after the no-update cue, no subsequent updating was required. Infants showed an amplified negative central (Nc) response when the predictable sequence was interrupted. Late components such as the PSW were also evoked in response to unexpected stimuli; however, we found no evidence for a differential response to the informational value of surprising cues at later stages of processing. Infants rather learned that surprising cues always signal a change in the environment that requires updating. Interestingly, infants responded with an amplified neural response to the absence of an expected change, suggesting a top-down modulation of early sensory processing in infants. Our findings corroborate emerging evidence showing that infants build predictive models early in life.

Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning

Although primary motor cortex (M1) is intimately involved in the dynamics of limb movement, its inputs may be more closely related to higher-order aspects of movement and multi-modal sensory feedback. Motor learning is thought to result from the adaption of internal models that compute transformations between these representations. While the psychophysics of motor learning has been studied in many experiments, the particular role of M1 in the process remains the subject of debate. Studies of learning-related changes in the spatial tuning of M1 neurons have yielded conflicting results. To resolve the discrepancies, we recorded from M1 during curl field adaptation in a reaching task. Our results suggest that aside from the addition of the load itself, the relation of M1 to movement dynamics remains unchanged as monkeys adapt behaviorally. Accordingly, we implemented a musculoskeletal model to generate synthetic neural activity having a fixed dynamical relation to movement and showed that these simulated neurons reproduced the observed behavior of the recorded M1 neurons. The stable representation of movement dynamics in M1 suggests that behavioral changes are mediated through progressively altered recruitment of M1 neurons, while the output effect of those neurons remained largely unchanged.

Implicit anticipation of probabilistic regularities: Larger CNV emerges for unpredictable events

Anticipation of upcoming events plays a crucial role in automatic behaviors. It is, however, still unclear whether the event-related brain potential (ERP) markers of anticipation could track the implicit acquisition of probabilistic regularities that can be considered as building blocks of automatic behaviors. Therefore, in a four-choice reaction time (RT) task performed by young adults (N = 36), the contingent negative variation (CNV) as an ERP marker of anticipation was measured from the onset of a cue stimulus until the presentation of a target stimulus. Due to the probability structure of the task, target stimuli were either predictable or unpredictable, but this was unknown to participants. The cue did not contain predictive information on the upcoming target. Results showed that the CNV amplitude during response preparation was larger before the unpredictable than before the predictable target stimuli. In addition, although RTs increased, the P3 amplitude decreased for the unpredictable as compared with the predictable target stimuli, possibly due to the stronger response preparation that preceded stimulus presentation. These results suggest that enhanced attentional resources are allocated to the implicit anticipation and processing of unpredictable events. This might originate from the formation of internal models on the probabilistic regularities of the stimulus stream, which primarily facilitates the processing of predictable events. Overall, we provide ERP evidence that supports the role of implicit anticipation and predictive processes in the acquisition of probabilistic regularities.

Sensorimotor contributions to working memory differ between the discrimination of Same and Different syllable pairs

Sensorimotor activity during speech perception is both pervasive and highly variable, changing as a function of the cognitive demands imposed by the task. The purpose of the current study was to evaluate whether the discrimination of Same (matched) and Different (unmatched) syllable pairs elicit different patterns of sensorimotor activity as stimuli are processed in working memory. Raw EEG data recorded from 42 participants were decomposed with independent component analysis to identify bilateral sensorimotor mu rhythms from 36 subjects. Time frequency decomposition of mu rhythms revealed concurrent event related desynchronization (ERD) in alpha and beta frequency bands across the peri- and post-stimulus time periods, which were interpreted as evidence of sensorimotor contributions to working memory encoding and maintenance. Left hemisphere alpha/beta ERD was stronger in Different trials than Same trials during the post-stimulus period, while right hemisphere alpha/beta ERD was stronger in Same trials than Different trials. A between-hemispheres contrast revealed no differences during Same trials, while post-stimulus alpha/beta ERD was stronger in the left hemisphere than the right during Different trials. Results were interpreted to suggest that predictive coding mechanisms lead to repetition suppression effects in Same trials. Mismatches arising from predictive coding mechanisms in Different trials shift subsequent working memory processing to the speech-dominant left hemisphere. Findings clarify how sensorimotor activity differentially supports working memory encoding and maintenance stages during speech discrimination tasks and have potential to inform sensorimotor models of speech perception and working memory.

The visual representations of motion and of gravity are functionally independent: Evidence of a differential effect of smooth pursuit eye movements

The memory for the final position of a moving object which suddenly disappears has been found to be displaced forward, in the direction of motion, and downwards, in the direction of gravity. These phenomena were coined, respectively, Representational Momentum and Representational Gravity. Although both these and similar effects have been systematically linked with the functioning of internal representations of physical variables (e.g. momentum and gravity), serious doubts have been raised for a cognitively based interpretation, favouring instead a major role of oculomotor and perceptual factors which, more often than not, were left uncontrolled and even ignored. The present work aims to determine the degree to which Representational Momentum and Representational Gravity are epiphenomenal to smooth pursuit eye movements. Observers were required to indicate the offset locations of targets moving along systematically varied directions after a variable imposed retention interval. Each participant completed the task twice, varying the eye movements' instructions: gaze was either constrained or left free to track the targets. A Fourier decomposition analysis of the localization responses was used to disentangle both phenomena. The results show unambiguously that constraining eye movements significantly eliminates the harmonic components which index Representational Momentum, but have no effect on Representational Gravity or its time course. The found outcomes offer promising prospects for the study of the visual representation of gravity and its neurological substrates.

Does exercise-induced muscle damage impair subsequent motor skill learning?

Motor skill learning is a fundamental aspect of human behavior based on the calibration of internal models via sensory information such as proprioception. Some conditions, as exercise-induced muscle damage (EIMD), disrupt proprioceptive information, and may cause learning impairment. Such possible relation between EIMD and motor skill learning has not yet been investigated and it is the aim of this study. For this purpose, thirty male university students (19.3 ± 1.8 years) were equally assigned to two groups: EIMD and CON group. The EIMD group received a treatment to induce muscle damage consisting of a weight lifting protocol directed to the agonist muscles related to the task prior to the pretest and to the learning sessions. EIMD was verified and compared between groups and along the process (0-168 h) by means of the degree of delayed onset muscle soreness (DOMS), perceived total quality recovery and maximal isometric strength (MIS). To investigate motor skill learning, both groups practiced a dart throwing task for four sessions with 150 trials in each session. Recovery status and DOMS were recovered at 96 h in the EIMD group, and MIS was not recovered throughout 168 h. In contrast, muscle damage parameters were not altered across 168 h in the CON group. Accuracy and consistency were compared within and between groups in a pretest posttest design. The EIMD group showed less accurate and consistent results on the long term (delayed posttest). Results confirmed our hypothesis that EIMD, a common condition in sports and in rehab practices, may hinder motor skill learning, possibly due to neurological aspects such as proprioceptive information, its relation to central nervous system reorganization and internal model consolidation.

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot's body and its peripersonal space. The internal models (of each robot's body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms.

Sensori-motor adaptation to novel limb dynamics influences the representation of peripersonal space

Peripersonal space can be considered as the interface between the body and the environment, where objects can be reached and which may serve as a reference for the central nervous system with regard to possible actions. Peripersonal space can be studied by assessing the perception of the reachable space, which depends on the body's physical characteristics (i.e., arm length) since their modifications have been shown to be associated with a change in peripersonal space representation. However, it remains unclear whether the representation of limb dynamics also influences the representation of peripersonal space. The present study investigated this issue by perturbing the force-field environment. A novel force field was created by rotating an experimental platform where participants were seated while they reached towards visual targets. Manual reaching performance was assessed before, during and after platform rotation. Crucially, perception of peripersonal space was also assessed, with reachability judgments, before and after platform rotation. As expected, sensori-motor adaptation to the perturbed force field was observed. Our principal finding is that peripersonal space was systematically perceived as closer to the body after force-field adaptation. Two control experiments showed no significant difference in reachability judgments when no reaching movements were performed during platform rotation or when reaching movements were performed without platform rotation, suggesting that the change in perceived peripersonal space resulted from exposure to new limb dynamics. Overall, our findings show that sensori-motor adaptation of reaching movements to a new force field, which does not directly influence arm length but results in the updating of the arm's internal model of limb dynamics, interacts with the perceptual categorisation of space, supporting a motor contribution to the representation of peripersonal space.

The underpinnings of cerebellar ataxias

The human cerebellum contains more than 60% of all neurons of the brain. Anatomically, the cerebellum is divided into 10 lobules (I-X). The cerebellar cortex is arranged into three layers: the molecular layer (external), the Purkinje cell layer and the granular layer (internal). Purkinje neurons and interneurons are inhibitory, except for granule cells. The layer of Purkinje neurons inhibit cerebellar nuclei, the sole output of the cerebellar circuitry, as well as vestibular nuclei. The cerebellum is arranged into a series of olivo-cortico-nuclear modules arranged longitudinally in the rostro-caudal plane. The cerebro-cerebellar connectivity is organized into multiple loops running in parallel. From the clinical standpoint, it is now considered that cerebellar symptoms can be gathered into 3 cerebellar syndromes: a cerebellar motor syndrome (CMS), a vestibulocerebellar syndrome (VCS) and a cerebellar cognitive affective syndrome/Schmahmann syndrome (CCAS/SS). CMS remains a cornerstone of modern clinical ataxiology, and relevant lesions involve lobules I-V, VI and VIII. The core feature of cerebellar symptoms is dysmetria, covering motor dysmetria (errors in the metrics of motion) and dysmetria of thought. The cerebellar circuitry plays a key-role in the generation and maintenance of internal models which correspond to neural representations reproducing the dynamic properties of the body. These models allow predictive computations for motor, cognitive, social, and affective operations. Cerebellar circuitry is endowed with noticeable plasticity properties.

Feeling of control of an action after supra and subliminal haptic distortions

Here we question the mechanisms underlying the emergence of the feeling of control that can be modulated even when the feeling of being the author of one's own action is intact. With a haptic robot, participants made series of vertical pointing actions on a virtual surface, which was sometimes postponed by a small temporal delay (15 or 65 ms). Subjects then evaluated their subjective feeling of control. Results showed that after temporal distortions, the hand-trajectories were adapted effectively but that the feeling of control decreased significantly. This was observed even in the case of subliminal distortions for which subjects did not consciously detect the presence of a distortion. Our findings suggest that both supraliminal and subliminal temporal distortions that occur within a healthy perceptual-motor system impact the conscious experience of the feeling of control of self-initiated motor actions.

Motor imagery helps updating internal models during microgravity exposure

Skilled movements result from a mixture of feedforward and feedback mechanisms conceptualized by internal models. These mechanisms subserve both motor execution and motor imagery. Current research suggests that imagery allows updating feedforward mechanisms, leading to better performance in familiar contexts. Does this still hold in radically new contexts? Here, we test this ability by asking participants to imagine swinging arm movements around shoulder in normal gravity condition and in microgravity in which studies showed that movements slow down. We timed several cycles of actual and imagined arm pendular movements in three groups of subjects during parabolic flight campaign. The first, control, group remained on the ground. The second group was exposed to microgravity but did not imagine movements inflight. The third group was exposed to microgravity and imagined movements inflight. All groups performed and imagined the movements before and after the flight. We predicted that a mere exposure to microgravity would induce changes in imagined movement duration. We found this held true for the group who imagined the movements, suggesting an update of internal representations of gravity. However, we did not find a similar effect in the group exposed to microgravity despite the fact participants lived the same gravitational variations as the first group. Overall, these results suggest that motor imagery contributes to update internal representations of movement in unfamiliar environments, while a mere exposure proved to be insufficient.

From internal models toward metacognitive AI

In several papers published in Biological Cybernetics in the 1980s and 1990s, Kawato and colleagues proposed computational models explaining how internal models are acquired in the cerebellum. These models were later supported by neurophysiological experiments using monkeys and neuroimaging experiments involving humans. These early studies influenced neuroscience from basic, sensory-motor control to higher cognitive functions. One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials. Consciousness and metacognition-the ability to monitor one's own thoughts, may be part of the solution to this enigma. Based on literature reviews of the past 20 years, here we propose a computational neuroscience model of metacognition. The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. In the prefrontal cortex, a distributed executive network called the "cognitive reality monitoring network" (CRMN) orchestrates conscious involvement of generative-inverse model pairs in perception and action. Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a "responsibility signal" that gates selection and learning of pairs in perception, action, and reinforcement learning. A high responsibility signal is given to the pairs that best capture the external world, that are competent in movements (small mismatch), and that are capable of reinforcement learning (small reward-prediction error). CRMN selects pairs with higher responsibility signals as objects of metacognition, and consciousness is determined by the entropy of responsibility signals across all pairs. This model could lead to new-generation AI, which exhibits metacognition, consciousness, dimension reduction, selection of modules and corresponding representations, and learning from small samples. It may also lead to the development of a new scientific paradigm that enables the causal study of consciousness by combining CRMN and decoded neurofeedback.

Motor adaptation with passive machines: a first study on the effect of real and virtual stiffness

Motor adaptation to novel force fields is considered as a key mechanism not only for the understanding of skills learning in healthy subjects but also for rehabilitation of neurological subjects. Several studies conducted over the last two decades used active robotic manipulanda to generate force fields capable of perturbing the baseline trajectories of both healthy and impaired subjects. Recent studies showed how motor adaptation to novel force fields can be induced also via virtual environments, whereas the effects of the force are projected onto a virtual hand, while the real hand remains constrained within a channel. This has great potentials of being translated into passive devices, rather than robotic ones, with clear benefits in terms of costs and availability of the devices. However, passive devices and virtual environments have received much less attention at least with regard to motor adaptation. This paper investigates the effects of both the real and virtual stiffness on motor adaptation. In particular, we tested 20 healthy subjects under two different real stiffness conditions (Stiff Channel vs Compliant Channel) and two different virtual conditions (Viscous vs Springy). Our main finding is that compliance of the channel favours a better adaptation featured with less lateral errors and longer retention of the after-effect. We posit that the physical compliance of the channel induces a proprioceptive feedback which is otherwise absent in a stiff condition.

Revealing individual differences in strategy selection through visual motion extrapolation

Humans are constantly challenged to make use of internal models to fill in missing sensory information. We measured human performance in a simple motion extrapolation task where no feedback was provided in order to elucidate the models of object motion incorporated into observers' extrapolation strategies. There was no "right" model for extrapolation in this task. Observers consistently adopted one of two models, linear or quadratic, but different observers chose different models. We further demonstrate that differences in motion sensitivity impact the choice of internal models for many observers. These results demonstrate that internal models and individual differences in those models can be elicited by unconstrained, predictive-based psychophysical tasks.

Seeing motion of controlled object improves grip timing in adults with autism spectrum condition: evidence for use of inverse dynamics in motor control

Previous studies (Haswell et al. in Nat Neurosci 12:970-972, 2009; Marko et al. in Brain J Neurol 138:784-797, 2015) reported that people with autism rely less on vision for learning to reach in a force field. This suggested a possibility that they have difficulties in extracting force information from visual motion signals, a process called inverse dynamics computation. Our recent study (Takamuku et al. in J Int Soc Autism Res 11:1062-1075, 2018) examined the ability of inverse computation with two perceptual tasks and found similar performances in typical and autistic adults. However, this tested the computation only in the context of sensory perception while it was possible that the suspected disability is specific to the motor domain. Here, in order to address the concern, we tested the use of inverse dynamics computation in the context of motor control by measuring changes in grip timing caused by seeing/not seeing a controlled object. The motion of the object was informative of its inertial force and typical participants improved their grip timing based on the visual feedback. Our interest was on whether the autism participants show the same improvement. While some autism participants showed atypical hand slowing when seeing the controlled object, we found no evidence of abnormalities in the inverse computation in our grip timing task or in a replication of the perceptual task. This suggests that the ability of inverse dynamics computation is preserved not only for sensory perception but also for motor control in adults with autism.