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Low‐level proprioceptive judgements involve a single frame of reference, whereas high‐level proprioceptive judgements are made across different frames of reference. The present study systematically compared low‐level (grasp→$\rightarrow$grasp) and high‐level (vision→$\rightarrow$grasp, grasp→$\rightarrow$vision) proprioceptive tasks, and quantified the consistency of grasp→$\rightarrow$vision and possible reciprocal nature of related high‐level proprioceptive tasks. Experiment 1 (n = 30) compared performance across vision→$\rightarrow$grasp, a grasp→$\rightarrow$vision and a grasp→$\rightarrow$grasp tasks. Experiment 2 (n = 30) compared performance on the grasp→$\rightarrow$vision task between hands and over time. Participants were accurate (mean absolute error 0.27 cm [0.20 to 0.34]; mean [95% CI]) and precise (R2$R^2$ = 0.95 [0.93 to 0.96]) for grasp→$\rightarrow$grasp judgements, with a strong correlation between outcomes (r = −0.85 [−0.93 to −0.70]). Accuracy and precision decreased in the two high‐level tasks (R2$R^2$ = 0.86 and 0.89; mean absolute error = 1.34 and 1.41 cm), with most participants overestimating perceived width for the vision→$\rightarrow$grasp task and underestimating it for grasp→$\rightarrow$vision task. There was minimal correlation between accuracy and precision for these two tasks. Converging evidence indicated performance was largely reciprocal (inverse) between the vision→$\rightarrow$grasp and grasp→$\rightarrow$vision tasks. Performance on the grasp→$\rightarrow$vision task was consistent between dominant and non‐dominant hands, and across repeated sessions a day or week apart. Overall, there are fundamental differences between low‐ and high‐level proprioceptive judgements that reflect fundamental differences in the cortical processes that underpin these perceptions. Moreover, the central transformations that govern high‐level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocal tasks. Key points: Low‐level proprioceptive judgements involve a single frame of reference (e.g. indicating the width of a grasped object by selecting from a series of objects of different width), whereas high‐level proprioceptive judgements are made across different frames of reference (e.g. indicating the width of a grasped object by selecting from a series of visible lines of different length).We highlight fundamental differences in the precision and accuracy of low‐ and high‐level proprioceptive judgements.We provide converging evidence that the neural transformations between frames of reference that govern high‐level proprioceptive judgements of grasp are personalised, stable and reciprocal for reciprocal tasks.This stability is likely key to precise judgements and accurate predictions in high‐level proprioception. [ABSTRACT FROM AUTHOR]

Developmental coordination disorder (DCD) impacts the quality of life and ability to perform coordinated actions in 5% of school‐aged children. The quality of body representations of individuals with DCD has been questioned, but never assessed. We hypothesize that children with DCD have imprecise body representations in the sensory and motor domains. Twenty neurotypical children, seventeen children with DCD (8–12 years old) and twenty neurotypical adults (25–45 years old) performed both sensory and motor body representation tasks: a limb identification and a limb movement task. We observed lower accuracy in the sensory task but not in the motor task. In both tasks, we observe a larger amplitude of errors, or synkinesis, in children with DCD than in neurotypical children. In neurotypical children, accuracy was lower than in neurotypical adults in the motor and sensory task, and the amplitude of sensory errors and synkinesis was higher than in neurotypical adults. Using a linear regression model, we showed that sensory accuracy is a good predictor of synkinesis production, and that synkinesis production is a good predictor of sensory accuracy, as can be expected by the perception‐action loop. Results support the hypothesis of an imprecision of body representation in DCD. We suggest that this imprecision arises from noise in the body representation used at the level of internal models of action. Future studies may assess whether slower plasticity of body representations, initial imprecision, or both may account for this observation. At the clinical level, prevention strategies targeting body representation in early childhood are strategically important to limit such impairments. Research Highlights: Body representation is impaired in children with DCD and has a significant cost in terms of the accuracy of sensory identification of body parts and associated movements.Inaccuracies in the body representation measured in perception and in action (error amplitude and synkinesis) are related in both NT children and adults.In typical development, we provide evidence of a strong link between body schema and body image. [ABSTRACT FROM AUTHOR]

Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires.

Whereas emotion theorists often keep their distance from the embodied approach, theorists of embodiment tend to treat emotion as a mainly physiologic process. However, intimate links between emotions and the body suggest that emotions are privileged phenomena to attempt to reintegrate mind and body and that the body helps the mind in shaping emotional responses. To date, research has favored the cerebrum over other parts of the brain as a substrate of embodied emotions. However, given the widely demonstrated contribution of the cerebellum to emotional processing, research in affective neuroscience should consider embodiment theory as a useful approach for evaluating the cerebellar role in emotion and affect. The aim of this review is to insert the cerebellum among the structures needed to embody emotions, providing illustrative examples of cerebellar involvement in embodied emotions (as occurring in empathic abilities) and in impaired identification and expression of embodied emotions (as occurring in alexithymia). [ABSTRACT FROM AUTHOR]

Autism spectrum disorders and developmental coordination disorders are both associated with sensorimotor impairments, yet their nature and specificity remain unknown. In order to clearly distinguish the specificity between the two disorders, children with autism spectrum disorder or developmental coordination disorder presenting the same degree of motor impairment, thus homogeneous profiles, were examined in a reach-to-displace paradigm, which allows the integrity of two main aspects of motor control (anticipation/feedforward control and movement correction/feedback control) to be separately interrogated. We manipulated children's previous knowledge of the weight of the object they were to displace: when known, participants could anticipate the consequences of the weight when reaching for the object, prior to contact with it, thus allowing for feedforward control. Conversely, when unknown prior to contact, participants had to cope with the object weight in the displacing phase of the movement, and use feedback control. Results revealed a preserved feedforward control, but an impaired movement execution (atypical slowness) in children with developmental coordination disorder, while children with autism spectrum disorder displayed the opposite pattern with an impaired feedforward control, but a preserved feedback one. These findings shed light on how specific motor impairments might differently characterize developmental disorders and call for motor rehabilitation programmes adapted to each population. A vast majority of individuals with autism spectrum disorder experience impairments in motor skills. Those are often labelled as additional developmental coordination disorder despite the lack of studies comparing both disorders. Consequently, motor skills rehabilitation programmes in autism are often not specific but rather consist in standard programmes for developmental coordination disorder. Here, we compared motor performance in three groups of children: a control group, an autism spectrum disorder group and a developmental coordination disorder group. Despite similar level of motor skills evaluated by the standard movement assessment battery for children, in a Reach-to-Displace Task, children with autism spectrum disorder and developmental coordination disorder showed specific motor control deficits. Children with autism spectrum disorder failed to anticipate the object properties, but could correct their movement as well as typically developing children. In contrast, children with developmental coordination disorder were atypically slow, but showed a spared anticipation. Our study has important clinical implications as motor skills rehabilitations are crucial to both populations. Specifically, our findings suggest that individuals with autism spectrum disorder would benefit from therapies aiming at improving their anticipation, maybe through the support of their preserved representations and use of sensory information. Conversely, individuals with developmental coordination disorder would benefit from a focus on the use of sensory information in a timely fashion. [ABSTRACT FROM AUTHOR]

Using neuroanatomical investigations in the macaque, Deepak Pandya and his colleagues have established the framework for auditory cortex organization, with subdivisions into core and belt areas. This has aided subsequent neurophysiological and imaging studies in monkeys and humans, and a nomenclature building on Pandya's work has also been adopted by the Human Connectome Project. The foundational work by Pandya and his colleagues is highlighted here in the context of subsequent and ongoing studies on the functional anatomy and physiology of auditory cortex in primates, including humans, and their relevance for understanding cognitive aspects of speech and language. [ABSTRACT FROM AUTHOR]

Effort perception is widely acknowledged to originate from central processes within the brain, mediated by the integration of an efference copy of motor commands in sensory areas. However, in this topical review, we aim to challenge this perspective by presenting evidence from neural mechanisms and empirical studies that suggest that reafferent signals from muscle spindles also play a significant role in effort perception. It is now imperative for future research (a) to investigate the precise mechanisms underlying the interactions between the efference copy and reafferent spindle signals in the generation of effort perception, and (b) to explore the potential for altering spindle sensitivity to affect perceived effort during ecological physical exercise and, subsequently, influence physical activity behaviours. [ABSTRACT FROM AUTHOR]

The cerebellum is classically associated with fine motor control, motor learning, and timing of actions. However, while its anatomy is well described and many synaptic plasticity have been identified, the computation performed by the cerebellar cortex is still debated. We, here, review recent advances on how the description of the functional synaptic connectivity between granule cells and Purkinje cells support the hypothesis that the cerebellum stores internal models of the body coordinates. We propose that internal models are specific of the task and of the locomotor context of each individual. [ABSTRACT FROM AUTHOR]

Abstract Talk of ”mental representations” is ubiquitous in the philosophy of mind, psychology, and cognitive science. A slogan common to many different approaches says that representations ”stand in for” the things they represent. We argue that this slogan is either false or uninformative. We then offer a new slogan that aims to do better. The new slogan ties the role of representations to the cognitive role played by the deliverances of perception. After clarifying the new slogan and warding off some misunderstandings, we discuss how the new slogan still captures the seed of truth in the old, point to some specific misunderstandings that can be avoided, and then suggest some ways that the new slogan is useful in the project of giving a satisfying philosophical theory of representation. [ABSTRACT FROM AUTHOR]

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. • Integrated view of motor invariants in human behavior. • The motor system may be tuned to the regularities of biological motion. • Internal models for action execution may be reused for action perception. • Motor invariants offer key computational advantages in decoding others' actions goals. [ABSTRACT FROM AUTHOR]

Prism adaptation (PA) is a useful method to investigate short-term sensorimotor plasticity. Following active exposure to prisms, individuals show consistent after-effects, probing that they have adapted to the perturbation. Whether after-effects are transferable to another task or remain specific to the task performed under exposure, represents a crucial interest to understand the adaptive processes at work. Motor imagery (MI, i.e., the mental representation of an action without any concomitant execution) offers an original opportunity to investigate the role of cognitive aspects of motor command preparation disregarding actual sensory and motor information related to its execution. The aim of the study was to test whether prism adaptation through MI led to transferable after-effects. Forty-four healthy volunteers were exposed to a rightward prismatic deviation while performing actual (Active group) versus imagined (MI group) pointing movements, or while being inactive (inactive group). Upon prisms removal, in the MI group, only participants with the highest MI abilities (MI+ group) showed consistent after-effects on pointing and, crucially, a significant transfer to throwing. This was not observed in participants with lower MI abilities and in the inactive group. However, a direct comparison of pointing after-effects and transfer to throwing between MI+ and the control inactive group did not show any significant difference. Although this interpretation requires caution, these findings suggest that exposure to intersensory conflict might be responsible for sensory realignment during prism adaptation which could be transferred to another task. This study paves the way for further investigations into MI's potential to develop robust sensorimotor adaptation. [ABSTRACT FROM AUTHOR]

In adaptation learning, visual feedback impacts how adaptation proceeds. With concurrent feedback, a more implicit/feedforward process is thought to be engaged, compared to feedback after movement, which promotes more explicit processes. Due to discrepancies across studies, related to timing and type of visual feedback, we isolated these conditions here. Four groups (N = 52) practiced aiming under rotated feedback conditions; feedback was provided concurrently, immediately after movement (visually or numerically), or visually after a 3 s delay. All groups adapted and only delayed feedback attenuated implicit adaptation as evidenced by post-practice after-effects. Contrary to some suggestions, immediately presented offline and numeric feedback resulted in implicit after-effects, potentially due to comparisons between feedforward information and seen or imagined feedback. [ABSTRACT FROM AUTHOR]

• Studied neural responses of action prediction in ecologically valid action repertoire. • Computer vision was employed to define action boundaries objectively. • Specific neural responses signalled probability-sensitive opening of option space. • Selection of most probable next step drew on hippocampus and midline areas. • Data reveal a two-step predictive process reflecting hierarchical action structure. Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In overt movement, internal models make predictions about the sensory consequences of a desired movement, generating the appropriate motor commands to achieve that movement. Using available sensory feedback, internal models are updated to allow for movement adaptation and in-turn better performance. Whether internal models are updated during motor imagery, the mental rehearsal of movement, is not well established. To investigate internal modelling during motor imagery, 66 participants were exposed to a leftwards prism shift while performing actual pointing movements (physical practice; PP), imagined pointing movements (motor imagery; MI), or no pointing movements (control). If motor imagery updates internal models, we hypothesized that aftereffects (pointing in the direction opposite the prism shift) would be observed in MI, like that of PP, and unlike that of control. After prism exposure, the magnitude of aftereffects was significant in PP (4.73° ± 1.56°), but not in MI (0.34° ± 0.96°) and control (0.34° ± 1.04°). Accordingly, PP differed significantly from MI and control. Our results show that motor imagery does not update internal models, suggesting that it is not a direct simulation of overt movement. Furthering our understanding of the mechanisms that underlie learning through motor imagery will lead to more effective applications of motor imagery., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Learning is commonplace in organisms such as ourselves and even in organisms as far distant as the bee and the octopus. Such learning is implemented by brains, or neuronal networks, and has been extensively studied within ethology, psychology, cognitive science, and neuroscience. Whether learning also takes place in nonneuronal settings has remained a matter of sustained controversy, too often dominated by ideological views. In this survey, I will explain how learning can be rigorously interpreted as a form of information processing and then explore the evidence for whether learning also takes place in organismal contexts outside the brain, such as physiology, development, and individual cells. I will try to explain why it is important to build bridges in this way between cognitive science and systems biology, why concepts and methods from various branches of engineering may be helpful in this task, and what the eventual impact may be on how we think about the organism. [ABSTRACT FROM AUTHOR]

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.

Humans tend to anticipate events when they synchronize their actions with sound (such as when they clap to music), which has puzzled scientists for decades. What accounts for this anticipation? We review two theoretical mechanisms for synchrony: predictive coding and dynamical systems. Both theories are grounded in neural activation patterns, but there are important distinctions. We contrast their assumptions, their computations, and their musical applications to anticipatory synchronization. [ABSTRACT FROM AUTHOR]

• Motor imagery and physical practices improve motor performances. • Movement smoothness discriminates between practices. • Online corrections through sensory feedback seems to be essential to improvement movement smoothness. Physical practice (PP) and motor imagery practice (MP) lead to the execution of fast and accurate arm movements. However, there is currently no information about the influence of MP on movement smoothness, nor about which performance parameters best discriminate these practices. In the current study, we assessed motor performances with an arm pointing task with constrained precision before and after PP (n = 15), MP (n = 15), or no practice (n = 15). We analyzed gains between Pre- and Post-Test for five performance parameters: movement duration, mean and maximal velocities, total displacements, and the number of velocity peaks characterizing movement smoothness. The results showed an improvement of performance after PP and MP for all parameters, except for total displacements. The gains for movement duration, and mean and maximal velocities were statistically higher after PP and MP than after no practice, and comparable between practices. However, motor gains for the number of velocity peaks were higher after PP than MP, suggesting that movements were smoother after PP than after MP. A discriminant analysis also identified the number of velocity peaks as the most relevant parameter that differentiated PP from MP. The current results provide evidence that PP and MP specifically modulate movement smoothness during arm reaching tasks. This difference may rely on online corrections through sensory feedback integration, available during PP but not during MP. [ABSTRACT FROM AUTHOR]

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 that the participants lived the same gravitational variations as the first group. Overall, these results suggest that motor imagery contributes to update internal representations of the considered movement in unfamiliar environments, while a mere exposure proved to be insufficient. NEW & NOTEWORTHY Gravity strongly affects the way movements are performed. How internal models process this information to adapt behavior to novel contexts is still unknown. The microgravity environment itself does not provide enough information to optimally adjust the period of natural arm swinging movements to microgravity. However, motor imagery of the task while immersed in microgravity was sufficient to update internal models. These results show that actually executing a task is not necessary to update graviception. [ABSTRACT FROM AUTHOR]

Multimodal perception is a key factor in obtaining a rich and meaningful representation of the world. However, how each stimulus combines to determine the overall percept remains a matter of research. The present work investigates the effect of sound on the bimodal perception of motion. A visual moving target was presented to the participants, associated with a concurrent sound, in a time reproduction task. Particular attention was paid to the structure of both the auditory and the visual stimuli. Four different laws of motion were tested for the visual motion, one of which is biological. Nine different sound profiles were tested, from an easier constant sound to more variable and complex pitch profiles, always presented synchronously with motion. Participants' responses show that constant sounds produce the worst duration estimation performance, even worse than the silent condition; more complex sounds, instead, guarantee significantly better performance. The structure of the visual stimulus and that of the auditory stimulus appear to condition the performance independently. Biological motion provides the best performance, while the motion featured by a constant-velocity profile provides the worst performance. Results clearly show that a concurrent sound influences the unified perception of motion; the type and magnitude of the bias depends on the structure of the sound stimulus. Contrary to expectations, the best performance is not generated by the simplest stimuli, but rather by more complex stimuli that are richer in information. [ABSTRACT FROM AUTHOR]

Prism adaptation (PA) is a useful method to investigate short-term sensorimotor plasticity. Following active exposure to prisms, individuals show consistent after-effects, probing that they have adapted to the perturbation. Whether after-effects are transferable to another task or remain specific to the task performed under exposure, represents a crucial interest to understand the adaptive processes at work. Motor imagery (MI, i.e., the mental representation of an action without any concomitant execution) offers an original opportunity to investigate the role of cognitive aspects of motor command preparation disregarding actual sensory and motor information related to its execution. The aim of the study was to test whether prism adaptation through MI led to transferable after-effects. Forty-four healthy volunteers were exposed to a rightward prismatic deviation while performing actual (Active group) versus imagined (MI group) pointing movements, or while being inactive (inactive group). Upon prisms removal, in the MI group, only participants with the highest MI abilities (MI+ group) showed consistent after-effects on pointing and, crucially, a significant transfer to throwing. This was not observed in participants with lower MI abilities and in the inactive group. However, a direct comparison of pointing after-effects and transfer to throwing between MI+ and the control inactive group did not show any significant difference. Although this interpretation requires caution, these findings suggest that exposure to intersensory conflict might be responsible for sensory realignment during prism adaptation which could be transferred to another task. This study paves the way for further investigations into MI’s potential to develop robust sensorimotor adaptation.

Multimodal perception is a key factor in obtaining a rich and meaningful representation of the world. However, how each stimulus combines to determine the overall percept remains a matter of research. The present work investigates the effect of sound on the bimodal perception of motion. A visual moving target was presented to the participants, associated with a concurrent sound, in a time reproduction task. Particular attention was paid to the structure of both the auditory and the visual stimuli. Four different laws of motion were tested for the visual motion, one of which is biological. Nine different sound profiles were tested, from an easier constant sound to more variable and complex pitch profiles, always presented synchronously with motion. Participants’ responses show that constant sounds produce the worst duration estimation performance, even worse than the silent condition; more complex sounds, instead, guarantee significantly better performance. The structure of the visual stimulus and that of the auditory stimulus appear to condition the performance independently. Biological motion provides the best performance, while the motion featured by a constant-velocity profile provides the worst performance. Results clearly show that a concurrent sound influences the unified perception of motion; the type and magnitude of the bias depends on the structure of the sound stimulus. Contrary to expectations, the best performance is not generated by the simplest stimuli, but rather by more complex stimuli that are richer in information.

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. [ABSTRACT FROM AUTHOR]

Catching a ball in a parabolic flight is a complex task in which the time and area of interception are strongly coupled, making interception possible for a short period. Although this makes the estimation of time-to-contact (TTC) from visual information in parabolic trajectories very useful, previous attempts to explain our precision in interceptive tasks circumvent the need to estimate TTC to guide our action. Obtaining TTC from optical variables alone in parabolic trajectories would imply very complex transformations from 2D retinal images to a 3D layout. We propose based on previous work and show by using simulations that exploiting prior distributions of gravity and known physical size makes these transformations much simpler, enabling predictive capacities from minimal early visual information. Optical information is inherently ambiguous, and therefore, it is necessary to explain how these prior distributions generate predictions. Here is where the role of prior information comes into play: it could help to interpret and calibrate visual information to yield meaningful predictions of the remaining TTC. The objective of this work is: (1) to describe the primary sources of information available to the observer in parabolic trajectories; (2) unveil how prior information can be used to disambiguate the sources of visual information within a Bayesian encoding-decoding framework; (3) show that such predictions might be robust against complex dynamic environments; and (4) indicate future lines of research to scrutinize the role of prior knowledge calibrating visual information and prediction for action control. [ABSTRACT FROM AUTHOR]

Distance perception for mobile agents is of great importance for safe navigation in unknown environments. Traditional methods make use of analytical solutions. Yet, according to some research hypothesis, distance perception is not the result of mathematical calculations, but an emergent consequence of an association process, where visual and tactile information acquire a central role. Designing models closer to natural cognition poses paramount challenges to artificial intelligence (AI), which call for a review of some of the foundations of current methods. Our work is framed in the embodied cognition paradigm, which highlights the importance of the body for the development of cognitive processes. We provide theoretical grounds and empirical evidence for an artificial account of distance perception through a multimodal association process. By learning multimodal sensorimotor schemes, an agent is capable of perceiving affordances related to distance perception without any non-body-based geometric knowledge. We let an agent interact with an environment cluttered with objects, while learning multimodal sensorimotor associations. The learned spatial relations are thoroughly characterized to show how the model depends on the agent's specific sensorimotor capabilities. The system is tested in a passability experiment and a navigation task, showing the agent anticipates undesired situations using the learned model predictions. [ABSTRACT FROM AUTHOR]

The flexible and efficient adaptation to dynamic, rapid changes in the auditory environment likely involves generating and updating of internal models. Such models arguably exploit connections between the neocortex and the cerebellum, supporting proactive adaptation. Here, we tested whether temporo-cerebellar disconnection is associated with the processing of sound at short timescales. First, we identify lesion-specific deficits for the encoding of short timescale spectro-temporal non-speech and speech properties in patients with left posterior temporal cortex stroke. Second, using lesion-guided probabilistic tractography in healthy participants, we revealed bidirectional temporo-cerebellar connectivity with cerebellar dentate nuclei and crura I/II. These findings support the view that the encoding and modeling of rapidly modulated auditory spectro-temporal properties can rely on a temporo-cerebellar interface. We discuss these findings in view of the conjecture that proactive adaptation to a dynamic environment via internal models is a generalizable principle.

Catching a ball in a parabolic flight is a complex task in which the time and area of interception are strongly coupled, making interception possible for a short period. Although this makes the estimation of time-to-contact (TTC) from visual information in parabolic trajectories very useful, previous attempts to explain our precision in interceptive tasks circumvent the need to estimate TTC to guide our action. Obtaining TTC from optical variables alone in parabolic trajectories would imply very complex transformations from 2D retinal images to a 3D layout. We propose based on previous work and show by using simulations that exploiting prior distributions of gravity and known physical size makes these transformations much simpler, enabling predictive capacities from minimal early visual information. Optical information is inherently ambiguous, and therefore, it is necessary to explain how these prior distributions generate predictions. Here is where the role of prior information comes into play: it could help to interpret and calibrate visual information to yield meaningful predictions of the remaining TTC. The objective of this work is: (1) to describe the primary sources of information available to the observer in parabolic trajectories; (2) unveil how prior information can be used to disambiguate the sources of visual information within a Bayesian encoding-decoding framework; (3) show that such predictions might be robust against complex dynamic environments; and (4) indicate future lines of research to scrutinize the role of prior knowledge calibrating visual information and prediction for action control.

Objective: We are still lacking a pathophysiological mechanism for functional disorders explaining the emergence and manifestation of characteristic, severely impairing bodily symptoms like chest pain or dizziness. A recent hypothesis based on the predictive coding theory of brain function suggests that in functional disorders, internal expectations do not match the actual sensory body states, leading to perceptual dysregulation and symptom perception. To test this hypothesis, we investigated the account of internal expectations and sensory input on gaze stabilization, a physiologically relevant parameter of gaze shifts, in functional dizziness.Methods: We assessed gaze stabilization in eight functional dizziness patients and 11 healthy controls during two distinct epochs of large gaze shifts: during a counter-rotation epoch (CR epoch), where the brain can use internal models, motor planning, and resulting internal expectations to achieve internally driven gaze stabilization; and during an oscillation epoch (OSC epoch), where, due to terminated motor planning, no movement expectations are present, and gaze is stabilized by sensory input alone.Results: Gaze stabilization differed between functional patients and healthy controls only when internal movement expectations were involved [F(1,17) = 14.63, p = 0.001, and partial η2 = 0.463]: functional dizziness patients showed reduced gaze stabilization during the CR (p = 0.036) but not OSC epoch (p = 0.26).Conclusion: While sensory-driven gaze stabilization is intact, there are marked, well-measurable deficits in internally-driven gaze stabilization in functional dizziness pointing at internal expectations that do not match actual body states. This experimental evidence supports the perceptual dysregulation hypothesis of functional disorders and is an important step toward understanding the underlying pathophysiology.

Objective: We are still lacking a pathophysiological mechanism for functional disorders explaining the emergence and manifestation of characteristic, severely impairing bodily symptoms like chest pain or dizziness. A recent hypothesis based on the predictive coding theory of brain function suggests that in functional disorders, internal expectations do not match the actual sensory body states, leading to perceptual dysregulation and symptom perception. To test this hypothesis, we investigated the account of internal expectations and sensory input on gaze stabilization, a physiologically relevant parameter of gaze shifts, in functional dizziness. Methods: We assessed gaze stabilization in eight functional dizziness patients and 11 healthy controls during two distinct epochs of large gaze shifts: during a counter-rotation epoch (CR epoch), where the brain can use internal models, motor planning, and resulting internal expectations to achieve internally driven gaze stabilization; and during an oscillation epoch (OSC epoch), where, due to terminated motor planning, no movement expectations are present, and gaze is stabilized by sensory input alone. Results: Gaze stabilization differed between functional patients and healthy controls only when internal movement expectations were involved [ F (1,17) = 14.63, p = 0.001, and partial η2 = 0.463]: functional dizziness patients showed reduced gaze stabilization during the CR (p = 0.036) but not OSC epoch (p = 0.26). Conclusion: While sensory-driven gaze stabilization is intact, there are marked, well-measurable deficits in internally-driven gaze stabilization in functional dizziness pointing at internal expectations that do not match actual body states. This experimental evidence supports the perceptual dysregulation hypothesis of functional disorders and is an important step toward understanding the underlying pathophysiology. [ABSTRACT FROM AUTHOR]

It has been proposed that the cerebellum contributes to social cognition. Based on the view that cerebellar internal models create predictions on motions and actions, we hypothesize that the posterior cerebellum supports identifying temporal sequences of persons' actions as well as detecting inconsistent actions that violate the implied trait. Participants were required to memorize the temporal order of a set of sentences that implied a personality trait. Importantly, the sentence sets were designed in such a way that the first half of each set involved actions that were consistent with the same trait, while the other half was either consistent or inconsistent with that trait. As expected, we found robust posterior cerebellar activation when memorizing the order of the actions, irrespective of trait consistency, but more crucially also for actions implying an inconsistent trait in comparison to consistent trait actions. We also found that the medial prefrontal cortex and posterior cerebellum were associated with confidence level in retrieving the sequences. This study supports the hypothesis that the posterior cerebellum identifies and predicts the low-level temporal order of actions and demonstrates for the first time that this area is also involved in the high-level prediction of trait implications of those actions. [ABSTRACT FROM AUTHOR]

The objective of this paper is to characterize the rich interplay between automatic and cognitive control processes that we propose is the hallmark of skill, in contrast to habit, and what accounts for its flexibility. We argue that this interplay isn't entirely hierarchical and static, but rather heterarchical and dynamic. We further argue that it crucially depends on the acquisition of detailed and well-structured action representations and internal models, as well as the concomitant development of metacontrol processes that can be used to shape and balance it. [ABSTRACT FROM AUTHOR]

• Cerebellar generated internal models are disrupted in autism spectrum disorders. • Cerebellar-mediated prediction and adaptation is impaired in autism. • Cerebellar contribution to E/I imbalance in autism. • Cerebellar plasticity is disrupted in autism. • Cerebellar-guided implicit learning is impaired in autism. Autism spectrum disorders (ASD) are highly prevalent neurodevelopmental disorders; however, the neurobiological mechanisms underlying disordered behavior in ASD remain poorly understood. Notably, individuals with ASD have demonstrated difficulties generating implicitly derived behavioral predictions and adaptations. Although many brain regions are involved in these processes, the cerebellum contributes an outsized role to these behavioral functions. Consistent with this prominent role, cerebellar dysfunction has been increasingly implicated in ASD. In this review, we will utilize the foundational, theoretical contributions of the late neuroscientist Masao Ito to establish an internal model framework for the cerebellar contribution to ASD-relevant behavioral predictions and adaptations. Additionally, we will also explore and then apply his key experimental contributions towards an improved, mechanistic understanding of the contribution of cerebellar dysfunction to ASD. [ABSTRACT FROM AUTHOR]

According to predictive processing theories, vision is facilitated by predictions derived from our internal models of what the world should look like. However, the contents of these models and how they vary across people remains unclear. Here, we use drawing as a behavioral readout of the contents of the internal models in individual participants. Participants were first asked to draw typical versions of scene categories, as descriptors of their internal models. These drawings were converted into standardized 3d renders, which we used as stimuli in subsequent scene categorization experiments. Across two experiments, participants' scene categorization was more accurate for renders tailored to their own drawings compared to renders based on others' drawings or copies of scene photographs, suggesting that scene perception is determined by a match with idiosyncratic internal models. Using a deep neural network to computationally evaluate similarities between scene renders, we further demonstrate that graded similarity to the render based on participants' own typical drawings (and thus to their internal model) predicts categorization performance across a range of candidate scenes. Together, our results showcase the potential of a new method for understanding individual differences – starting from participants' personal expectations about the structure of real-world scenes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

What happens in the brain when we learn? Ever since the foundational work of Cajal, the field has made numerous discoveries as to how experience could change the structure and function of individual synapses. However, more recent advances have highlighted the need for understanding learning in terms of complex interactions between populations of neurons and synapses. How should one think about learning at such a macroscopic level? Here, we develop a conceptual framework to bridge the gap between the different scales at which learning operates, from synapses to neurons to behavior. Using this framework, we explore the principles that guide sensorimotor learning across these scales, and set the stage for future experimental and theoretical work in the field. Experimental work on the neural basis of learning has largely focused on single neurons and synapses, yet behavior depends on coordinated interactions between large populations of neurons and synapses. A state space framework has been developed to study dynamics of multidimensional systems, but has not yet been widely adopted to study signatures of learning in neural activity and synaptic weights at a population level. Recent studies have successfully used the state space approach to link behavior to the geometry and structure of neural dynamics. We propose a broader application of the state space framework for understanding learning in terms of coordinated changes across populations of synapses and neurons. The state space framework provides an account of the various timescales of learning, and enables an understanding of the computational principles of learning at a macroscopic level. [ABSTRACT FROM AUTHOR]

Multiple theories regarding motor learning and postural control development aim to explain how the central nervous system (CNS) acquires, adjusts, and learns postural behaviors. However, few theories of postural motor development and learning propose possible neurophysiologic correlates to support their assumptions. Evidence from behavioral and computational models support the cerebellum's role in supervising motor learning through the production of forward internal models, corrected by sensory prediction errors. Optimal Feedback Control Theory (OFCT) states that the CNS learns new behaviors by minimizing the cost of multi-joint movements that attain a task goal. By synthesizing principles of the OFCT, postural sway characteristics, and cerebellar anatomy and its internal models, we propose an integrated learning model in which cerebellar supervision of postural control is governed by movement cost functions. [ABSTRACT FROM AUTHOR]

The 2008–2009 Global Financial Crisis (GFC) has swayed regulators to set forth the Solvency II agreement for determining Solvency Capital Requirement (SCR) for insurance companies. In this paper, we apply novel internal models to investigate whether the latest version of the Solvency II standard model demands sufficient capital charges, both in normal and stressed times, for the different risk categories included in bond and stock portfolios. Because the GFC has shown that extreme events on the tail of probability distributions can occur quite often, our empirical findings indicate that the magnitude of the equity risk using the GJR–EVT–Copula method requires insurers to keep more SCR for stock portfolios than the Solvency II standard model. In the case of a bond portfolio, we conclude that the Solvency II standard model requires approximately the same SCR as our internal model for the higher quality and longer maturity bonds, whereas the standard model overestimates SCR for the lower quality and shorter maturity bonds. At the same time, the standard model underestimates interest-rate risk and overestimates spread risk. Overall, the discrepancies in the estimated SCRs between the Solvency II standard technique and our internal models increase as the level of the risks rise for both stock and bond markets. Our empirical results are in line with other competing internal modeling techniques regarding stock market investment and bond portfolios with the higher quality and longer maturity bonds, while for the lower quality and shorter maturity bonds, the results contradict other modeling procedures. The obtained empirical results are interesting in terms of theory and practical applications and have important implication for compliance with the Solvency II capital requirements. Likewise, it can be of interest to insurance regulators, policymakers, actuaries, and researchers within the field of insurance and risk management. [ABSTRACT FROM AUTHOR]