Hierarchical motor adaptations negotiate failures during force field learning.

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of 'internal model adaptation'. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.

Haptic communication between humans is tuned by the hard or soft mechanics of interaction.

To move a hard table together, humans may coordinate by following the dominant partner's motion [1-4], but this strategy is unsuitable for a soft mattress where the perceived forces are small. How do partners readily coordinate in such differing interaction dynamics? To address this, we investigated how pairs tracked a target using flexion-extension of their wrists, which were coupled by a hard, medium or soft virtual elastic band. Tracking performance monotonically increased with a stiffer band for the worse partner, who had higher tracking error, at the cost of the skilled partner's muscular effort. This suggests that the worse partner followed the skilled one's lead, but simulations show that the results are better explained by a model where partners share movement goals through the forces, whilst the coupling dynamics determine the capacity of communicable information. This model elucidates the versatile mechanism by which humans can coordinate during both hard and soft physical interactions to ensure maximum performance with minimal effort.

The second me: Seeing the real body during humanoid robot embodiment produces an illusion of bi-location.

Whole-body embodiment studies have shown that synchronized multi-sensory cues can trick a healthy human mind to perceive self-location outside the bodily borders, producing an illusion that resembles an out-of-body experience (OBE). But can a healthy mind also perceive the sense of self in more than one body at the same time? To answer this question, we created a novel artificial reduplication of one's body using a humanoid robot embodiment system. We first enabled individuals to embody the humanoid robot by providing them with audio-visual feedback and control of the robot head movements and walk, and then explored the self-location and self-identification perceived by them when they observed themselves through the embodied robot. Our results reveal that, when individuals are exposed to the humanoid body reduplication, they experience an illusion that strongly resembles heautoscopy, suggesting that a healthy human mind is able to bi-locate in two different bodies simultaneously.

Organotypic culture of human brain explants as a preclinical model for AI-driven antiviral studies.

Viral neuroinfections represent a major health burden for which the development of antivirals is needed. Antiviral compounds that target the consequences of a brain infection (symptomatic treatment) rather than the cause (direct-acting antivirals) constitute a promising mitigation strategy that requires to be investigated in relevant models. However, physiological surrogates mimicking an adult human cortex are lacking, limiting our understanding of the mechanisms associated with viro-induced neurological disorders. Here, we optimized the Organotypic culture of Post-mortem Adult human cortical Brain explants (OPAB) as a preclinical platform for Artificial Intelligence (AI)-driven antiviral studies. OPAB shows robust viability over weeks, well-preserved 3D cytoarchitecture, viral permissiveness, and spontaneous local field potential (LFP). Using LFP as a surrogate for neurohealth, we developed a machine learning framework to predict with high confidence the infection status of OPAB. As a proof-of-concept, we showed that antiviral-treated OPAB could partially restore LFP-based electrical activity of infected OPAB in a donor-dependent manner. Together, we propose OPAB as a physiologically relevant and versatile model to study neuroinfections and beyond, providing a platform for preclinical drug discovery.

Brain exposure to SARS-CoV-2 virions perturbs synaptic homeostasis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with short- and long-term neurological complications. The variety of symptoms makes it difficult to unravel molecular mechanisms underlying neurological sequalae after coronavirus disease 2019 (COVID-19). Here we show that SARS-CoV-2 triggers the up-regulation of synaptic components and perturbs local electrical field potential. Using cerebral organoids, organotypic culture of human brain explants from individuals without COVID-19 and post-mortem brain samples from individuals with COVID-19, we find that neural cells are permissive to SARS-CoV-2 to a low extent. SARS-CoV-2 induces aberrant presynaptic morphology and increases expression of the synaptic components Bassoon, latrophilin-3 (LPHN3) and fibronectin leucine-rich transmembrane protein-3 (FLRT3). Furthermore, we find that LPHN3-agonist treatment with Stachel partially restored organoid electrical activity and reverted SARS-CoV-2-induced aberrant presynaptic morphology. Finally, we observe accumulation of relatively static virions at LPHN3-FLRT3 synapses, suggesting that local hindrance can contribute to synaptic perturbations. Together, our study provides molecular insights into SARS-CoV-2-brain interactions, which may contribute to COVID-19-related neurological disorders.

Effect of connection induced upper body movements on embodiment towards a limb controlled by another during virtual co-embodiment.

Even if we cannot control them, or when we receive no tactile or proprioceptive feedback from them, limbs attached to our bodies can still provide indirect proprioceptive and haptic stimulations to the body parts they are attached to simply due to the physical connections. In this study we investigated whether such indirect movement and haptic feedbacks from a limb contribute to a feeling of embodiment towards it. To investigate this issue, we developed a 'Joint Avatar' setup in which two individuals were given full control over the limbs in different sides (left and right) of an avatar during a reaching task. The backs of the two individuals were connected with a pair of solid braces through which they could exchange forces and match the upper body postures with one another. Coupled with the first-person view, this simulated an experience of the upper body being synchronously dragged by the partner-controlled virtual arm when it moved. We observed that this passive synchronized upper-body movement significantly reduced the feeling of the partner-controlled limb being owned or controlled by another. In summary, our results suggest that even in total absence of control, connection induced upper body movements synchronized with the visible limb movements can positively affect the sense of embodiment towards partner-controlled or autonomous limbs.

Granger Geweke Causality reveals information exchange during physical interaction is modulated by task difficulty.

The haptic sense is an important mode of communication during physical interactions, and it is known to enable humans to estimate key features of their partner's behavior. It is proposed that such estimations are based upon the exchange of information mediated by the interaction forces, resulting in role distribution and coordination between partners. In the present study, we examined whether the information exchange is functionally modified to adapt to the task, or whether it is a fixed process, leaving the adaptation to individual's behaviors. We analyzed the forces during an empirical dyadic interaction task using Granger-Geweke causality analysis, which allowed us to quantify the causal influence of each individual's forces on their partner's. The dynamics of relative phase were also examined. We observed an increase of inter-partner influence with an increase in the spatial accuracy required by the task, demonstrating an adaptation of information flow to the task. This increase of exchange with the spatial accuracy constraint was accompanied by an increase of errors and of the variability of the relative phase between forces. The influence was dominated by participants in a specific role, showing a clear role division as well as task division between the dyad partners. Moreover, the influence occurred in the [2.15-7] Hz frequency band, demonstrating its importance as a frequency band of interest during cooperation involving haptic interaction. Several interpretations are introduced, ranging from sub-division of motion control to phase-amplitude coupling.

Competition Increases the Effort Put Into a Physical Interaction Task.

Physical interaction can enhance motor learning, but it remains unclear what type of interaction is best suited to increasing the active effort put into a task, which should support learning. Here, we used the same interactive tracking task with different instructions to induce three training conditions: competition, collaboration, and self-improvement, where partners improve their own performance while interacting haptically with each other. The effort was gauged by measuring the total normalized muscle activity. Feedback of task performance and the haptic dynamics were identical in all three training conditions, so the effort needed to complete the task was the same. Only the instructions to 'compete with the partner', 'improve your and your partner's accuracy' and 'improve your accuracy' were different among the competition, collaboration, and self-improvement conditions, respectively. Despite having the same goal of maximizing self-performance during competition and self-improvement, participants exerted significantly more effort during competition, and their tracking accuracy was highest during competitive practice. Least effort was put into collaboration but tracking accuracy during collaboration was comparable to self-improvement. Our results suggest that interactive haptic competition can induce higher active drive or effort than either collaborative training or self-focused practice.

Differential neural correlates of reciprocal activation and cocontraction control in dorsal and ventral premotor cortices.

Efficient control of reciprocal activation and cocontraction of the muscles are critical to perform skillful actions with suitable force and impedance. However, it remains unclear how the brain controls force and impedance while recruiting the same set of muscles as actuators. Does control take place at the single muscle level leading to force and impedance, or are there higher-order centers dedicated to controlling force and impedance? We addressed this question using functional MRI during voluntary isometric wrist contractions with online electromyogram feedback. Comparison of the brain activity between the conditions requiring control of either wrist torque or cocontraction demonstrates that blood oxygen level-dependent activity in the caudo-dorsal premotor cortex (PMd) correlates well with torque, whereas the activity in the ventral premotor cortex (PMv) correlates well with the level of cocontraction. This suggests distinct roles of the PMd and PMv in the voluntary control of reciprocal activation and cocontraction of muscles, respectively.

Embodiment modifies attention allotment for the benefit of dual task performance.

Many everyday tasks, like walking down a street, require us to dual task to also avoid collisions of our swinging arms with other pedestrians. The collision avoidance is possible with ease because humans attend to all our (embodied) limbs. But how does the level of embodiment affect attention distribution, and consequently task performance in dual tasks? Here we examined this question with a dual task that required participants to perform a cued button-press (main task) with their right hand, while reacting to possible collisions by a moving object with a left 'robot' hand (secondary task). We observed that participants consistently improve main task performance when they perceived the robot hand to be embodied, compared to when they don't. The secondary task performance could be maintained in both cases. Our results suggest that embodiment of a limb modifies attention allotment for the benefit of dual motor task performance using limbs.

Bodily ownership of an independent supernumerary limb: an exploratory study.

Can our brain perceive a sense of ownership towards an independent supernumerary limb; one that can be moved independently of any other limb and provides its own independent movement feedback? Following the rubber-hand illusion experiment, a plethora of studies have shown that the human representation of "self" is very plastic. But previous studies have almost exclusively investigated ownership towards "substitute" artificial limbs, which are controlled by the movements of a real limb and/or limbs from which non-visual sensory feedback is provided on an existing limb. Here, to investigate whether the human brain can own an independent artificial limb, we first developed a novel independent robotic "sixth finger." We allowed participants to train using the finger and examined whether it induced changes in the body representation using behavioral as well as cognitive measures. Our results suggest that unlike a substitute artificial limb (like in the rubber hand experiment), it is more difficult for humans to perceive a sense of ownership towards an independent limb. However, ownership does seem possible, as we observed clear tendencies of changes in the body representation that correlated with the cognitive reports of the sense of ownership. Our results provide the first evidence to show that an independent supernumerary limb can be embodied by humans.

Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization.

A significant problem in brain-computer interface (BCI) research is decoding - obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain activity, have shortcomings in terms of performance, computational expense and usage burden. Here, a new methodology called prediction error decoding was used for motor imagery (MI) detection and compared with direct intention decoding. Galvanic vestibular stimulation (GVS) was used to induce subliminal sensory feedback between the forehead and mastoids without any burden. Prediction errors were generated between the GVS-induced sensory feedback and the MI direction. The corresponding prediction error decoding of the front/back MI task was validated. A test decoding accuracy of 77.83-78.86% (median) was achieved during GVS for every 100[Formula: see text]ms interval. A nonzero weight parameter-based channel screening (WPS) method was proposed to select channels individually and commonly during GVS. When the WPS common-selected mode was compared with the WPS individual-selected mode and a classical channel selection method based on correlation coefficients (CCS), a satisfactory decoding performance of the selected channels was observed. The results indicated the positive impact of measuring common specific channels of the BCI.

Binary Semantic Classification Using Cortical Activation with Pavlovian-Conditioned Vestibular Responses in Healthy and Locked-In Individuals.

To develop a more reliable brain-computer interface (BCI) for patients in the completely locked-in state (CLIS), here we propose a Pavlovian conditioning paradigm using galvanic vestibular stimulation (GVS), which can induce a strong sensation of equilibrium distortion in individuals. We hypothesized that associating two different sensations caused by two-directional GVS with the thoughts of "yes" and "no" by individuals would enable us to emphasize the differences in brain activity associated with the thoughts of yes and no and hence help us better distinguish the two from electroencephalography (EEG). We tested this hypothesis with 11 healthy and 1 CLIS participant. Our results showed that, first, conditioning of GVS with the thoughts of yes and no is possible. And second, the classification of whether an individual is thinking "yes" or "no" is significantly improved after the conditioning, even in the absence of subsequent GVS stimulations. We observed average classification accuracy of 73.0% over 11 healthy individuals and 85.3% with the CLIS patient. These results suggest the establishment of GVS-based Pavlovian conditioning and its usability as a noninvasive BCI.

Individuals Prioritize the Reach Straightness and Hand Jerk of a Shared Avatar over Their Own.

Cyber space enables us to "share" bodies whose movements are a consequence of movements by several individuals. But whether and how our motor behavior is affected during body sharing remains unclear. Here we examined this issue in arm reaching performed by a shared avatar, whose movement was generated by averaging the movements of two participants. We observed that participants exhibited improved reaction times with a shared avatar than alone. Moreover, the reach trajectory of the shared avatar was straighter than that of either participant and correlated with their subjective embodiment of the avatar. Finally, the jerk of the avatar's hand was less than either participant's own hand, both when they reached alone and in the shared body. Movement straightness and hand jerk are well known characteristics of human reach behavior, and our results suggest that during body sharing, humans prioritize these movement characteristics of the shared body over their own.

Activity in the dorsal ACC causes deterioration of sequential motor performance due to anxiety.

Performance anxiety can profoundly affect motor performance, even in experts such as professional athletes and musicians. Previously, the neural mechanisms underlying anxiety-induced performance deterioration have predominantly been investigated for individual one-shot actions. Sports and music, however, are characterized by action sequences, where many individual actions are assembled to develop a performance. Here, utilizing a novel differential sequential motor learning paradigm, we first show that performance at the junctions between pre-learnt action sequences is particularly prone to anxiety. Next, utilizing functional magnetic resonance imaging (fMRI), we reveal that performance deterioration at the junctions is parametrically correlated with activity in the dorsal anterior cingulate cortex (dACC). Finally, we show that 1 Hz repetitive transcranial magnetic stimulation of the dACC attenuates the performance deterioration at the junctions. These results demonstrate causality between dACC activity and impairment of sequential motor performance due to anxiety, and suggest new intervention techniques against the deterioration.

The where of handovers by humans: Effect of partner characteristics, distance and visual feedback.

Object handovers between humans are common in our daily life but the mechanisms underlying handovers are still largely unclear. A good understanding of these mechanisms is important not only for a better understanding of human social behaviors, but also for the prospect of an automatized society in which machines will need to perform similar objects exchanges with humans. In this paper, we analyzed how humans determine the location of object transfer during handovers- to determine whether they can predict the preferred handover location of a partner, the variation of this prediction in 3D space, and to examine how much of a role vision plays in the whole process. For this we developed a paradigm that allows us to compare handovers by humans with and without on-line visual feedback. Our results show that humans have the surprising ability to modulate their handover location according to partners they have just met such that the resulting handover errors are in the order of few centimeters, even in the absence of vision. The handover errors are least along the axis joining the two partners, suggesting a limited role for visual feedback in this direction. Finally, we show that the handover locations are explained very well by a linear model considering the heights, genders and social dominances of the two partners, and the distance between them. We developed separate models for the behavior of 'givers' and 'receivers' and discuss how the behavior of the same individual changes depending on his role in the handover.

More than just co-workers: Presence of humanoid robot co-worker influences human performance.

Does the presence of a robot co-worker influence the performance of humans around it? Studies of motor contagions during human-robot interactions have examined either how the observation of a robot affects a human's movement velocity, or how it affects the human's movement variance, but never both together. Performance however, has to be measured considering both task speed (or frequency) as well as task accuracy. Here we examine an empirical repetitive industrial task in which a human participant and a humanoid robot work near each other. We systematically varied the robot behavior, and observed whether and how the performance of a human participant is affected by the presence of the robot. To investigate the effect of physical form, we added conditions where the robot co-worker torso and head were covered, and only the moving arm was visible to the human participants. Finally, we compared these behaviors with a human co-worker, and examined how the observed behavioral affects scale with experience of robots. Our results show that human task frequency, but not task accuracy, is affected by the observation of a humanoid robot co-worker, provided the robot's head and torso are visible.

Prediction error induced motor contagions in human behaviors.

Motor contagions refer to implicit effects on one's actions induced by observed actions. Motor contagions are believed to be induced simply by action observation and cause an observer's action to become similar to the action observed. In contrast, here we report a new motor contagion that is induced only when the observation is accompanied by prediction errors - differences between actions one observes and those he/she predicts or expects. In two experiments, one on whole-body baseball pitching and another on simple arm reaching, we show that the observation of the same action induces distinct motor contagions, depending on whether prediction errors are present or not. In the absence of prediction errors, as in previous reports, participants' actions changed to become similar to the observed action, while in the presence of prediction errors, their actions changed to diverge away from it, suggesting distinct effects of action observation and action prediction on human actions.

Utilizing sensory prediction errors for movement intention decoding: A new methodology.

We propose a new methodology for decoding movement intentions of humans. This methodology is motivated by the well-documented ability of the brain to predict sensory outcomes of self-generated and imagined actions using so-called forward models. We propose to subliminally stimulate the sensory modality corresponding to a user's intended movement, and decode a user's movement intention from his electroencephalography (EEG), by decoding for prediction errors-whether the sensory prediction corresponding to a user's intended movement matches the subliminal sensory stimulation we induce. We tested our proposal in a binary wheelchair turning task in which users thought of turning their wheelchair either left or right. We stimulated their vestibular system subliminally, toward either the left or the right direction, using a galvanic vestibular stimulator and show that the decoding for prediction errors from the EEG can radically improve movement intention decoding performance. We observed an 87.2% median single-trial decoding accuracy across tested participants, with zero user training, within 96 ms of the stimulation, and with no additional cognitive load on the users because the stimulation was subliminal.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans.

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants' ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert's abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert's self-estimation is explained only by considering a change in the individual's forward model, showing that an improvement in an expert's ability to predict outcomes of observed actions affects the individual's forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.