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Adaptively recalibrating motor-sensory asynchrony is critical for animals to perceive self-produced action consequences. It is controversial whether motor- or sensory-related neural circuits recalibrate this asynchrony. By combining magnetoencephalography (MEG) and functional MRI (fMRI), we investigate the temporal changes in brain activities caused by repeated exposure to a 150-ms delay inserted between a button-press action and a subsequent flash. We found that readiness potentials significantly shift later in the motor system, especially in parietal regions (average: 219.9 ms), while visually evoked potentials significantly shift earlier in occipital regions (average: 49.7 ms) in the delay condition compared to the no-delay condition. Moreover, the shift in readiness potentials, but not in visually evoked potentials, was significantly correlated with the psychophysical measure of motor-sensory adaptation. These results suggest that although both motor and sensory processes contribute to the recalibration, the motor process plays the major role, given the magnitudes of shift and the correlation with the psychophysical measure.

In addition to mental disorders such as attention, emotion, delusions, hallucinations, and difficulties in social skills, the patients with schizophrenia exhibits significant abnormality in sensorimotor perception and control. To seek a neurobiological cause of the heterogeneous symptoms in schizophrenia, we focused on the impaired inference mechanism of the self-agency of the schizophrenia's brain where the sensory outcome generated by the self-initiated action was misattributed to the other agent's action. By developing a novel computational model of agency experience using a Bayesian decision making framework, we united the computational mechanisms of agency and motor control via internal model: a model for one to predict the sensory consequence of action. Our theory based on optimal feedback control with Kalman filtering successfully predicted a variety of schizophrenia's motor abnormalities assuming a deformed internal model. To discuss the plausibility of these model predictions, we reviewed literature that might support these predictions. We further proposed some experiments that potentially examine the proposed model of schizophrenia. Our approach in investigating a problem of mind by projecting it on the coordinates system of the embodiment effectively shed light on a central neuropathology of this disease that had been latent behind the observed behaviors.

Summary In everyday life, people control objects in the world around them to varying degrees. The processes people actively use to establish their control, while interacting with an environment containing large ambiguity, remain unknown. This study examines how people explore their control over the environment and how they detect small differences in control among objects. In the experimental task, participants moved three dots on a screen and identified one dot over which the level of control is different from that of the other two. The results support a two-step behavior mechanism underlying the sensing of control difference: People first explore their overall control in the environment, and then the results of the initial exploration are used to selectively tune the direction (i.e., either more or less) of the detected control difference, ensuring efficient and rapid detection of the type of control difference that is potentially important for further action selections., Graphical Abstract, Highlights • This study examines how people explore their control over multiple objects • The results support a two-step behavior mechanism of active sensing of control • People first sense their overall control over the environment at an early stage • The initial exploration tunes the sensitivity of detecting control difference, Neuroscience; Behavioral Neuroscience; Cognitive Neuroscience

Perceived temporal interval between voluntary action and its outcome is shorter than that between involuntary action and its outcome (i.e., intentional binding). Although the effect is robust and extensively employed as a marker of sense of agency, the nature of intentional binding in multiple actions and outcomes remains unclear. We examined intentional binding in alternated action-outcome dyads. Participants actively or passively pressed a key, followed by a tone, and they again pressed the same key immediately after the preceding tone; resulting in four keypress-tone dyads in a trial. Participants reproduced the duration of alternated keypress-tone dyads or the temporal interval between a dyad embedded in the alternations. The reproduced duration was shorter in the active than in the passive condition, suggesting the intentional binding in action-outcome alternations. In contrast, the reproduced interval between a dyad was longer in the active condition and did not correlate with the reproduced duration of the alternations. These results suggest that subjective time during actions may rely not only on a general internal clock; rather, it may also be modulated by postdictive biases that are flexibly switched based on what we recall.

Internal models are neural mechanisms that can mimic the input-output properties of controlled objects. Our studies have shown that: 1) an internal model for a novel tool is acquired in the cerebellum (Imamizu et al., 2000); 2) internal models are modularly organized in the cerebellum (Imamizu et al., 2003); 3) their outputs are sent to the premotor regions after learning (Tamada et al., 1999); and 4) the prefrontal and parietal regions contribute to the blending of the outputs (Imamizu et al., 2004). Here, we investigated changes in global neural networks resulting from the acquisition of a new internal model. Human subjects manipulated three types of rotating joystick whose cursor appeared at a position rotated 60 degrees, 110 degrees, or 160 degrees around the screen's center. In a pre-test after long-term training (5 days) for the 60 degrees and 160 degrees joysticks, brain activation was scanned during manipulation of the three joysticks. The subjects were then trained for the 110 degrees for only 25 min. In a post-test, activation was scanned using the same method as the pre-test. Comparisons of the post-test to the pre-test revealed that the volume of activation decreased in most of the regions where activation for the three rotations was observed. However, there was an increase in volume at a marginally significant level (p.08) only in the inferior-lateral cerebellum and only for the 110 degrees joystick. In the cerebral cortex, activation related to 110 degrees decreased in the prefrontal and parietal regions but increased in the premotor and supplementary motor area (SMA) regions. These results can be explained by a model in which outputs of the 60 degrees and 160 degrees internal models are blended by prefrontal and parietal regions to cope with the novel 110 degrees joystick before the 25-minute training; after the acquisition within the cerebellum of an internal model for the 110 degrees, output is directly sent to the premotor and SMA regions, and activation in these regions increases.

The purpose of this study is to identify the functional brain networks activated in relation to actual tool-use in humans. Although previous studies have identified brain activity related to tool-use gestures (Moll et al., 2000), they did not investigate the brain activity involved in such tool-use. We investigated brain activity using functional magnetic resonance imaging (fMRI) while human subjects mentally imagined using sixteen common tools and while they actually used them. Brain activity for both actual and imagined tool-use was found in the posterior part of the parietal cortex, in the supplementary motor area, and in the cerebellum. Under imagined tool-use conditions, we found brain activity in the premotor and right pars opercularis. Under actual tool-use conditions, we found it in the primary motor area, in the thalamus, and in the left pars opercularis. Our precise analysis in the cerebellum indicated that activity evoked by imagery was located significantly more lateral to that evoked by actual use. We found a relationship between activity in the tool imagery and execution conditions by comparing their t-value-weighted centroid of activation coordinates. Moreover, for half of the subjects the spatial distribution pattern for each tool was similar, suggesting that neural mechanisms contributing to skillful tool-use are modularly organized in the cerebellum.

SummarySkilled object manipulation requires knowledge, or internal models, of object dynamics relating applied force to motion [1–4], and our ability to handle myriad objects [5–6] indicates that the brain maintains multiple models [7–9]. Recent behavioral studies have shown that once learned, an internal model of an object with novel dynamics can be rapidly recruited and derecruited as the object is grasped and released [10–12]. We used event-related fMRI to investigate neural activity linked to grasping an object with recently learned dynamics in preparation for moving it after a delay. Subjects also performed two control tasks in which they either moved without the object in hand or applied isometric forces to the object. In all trials, subjects received a cue indicating which task to perform in response to a go signal delivered 5–10 s later. We examined BOLD responses during the interval between the cue and go and assessed the conjunction of the two contrasts formed by comparing the primary task to each control. The analysis revealed significant activity in the ipsilateral cerebellum and the contralateral and supplementary motor areas. We propose that these regions are involved in internal-model recruitment in preparation for movement execution.

The monkey parietal anterior intraparietal area (AIP) is part of the grasp planning and execution circuit which contains neurons that encode object features relevant for grasping, such as the width and the height. In this study we focus on the formation of AIP neurons during grasp development. We propose and implement a neural network structure and a learning mechanism that is driven by successful grasp experiences during early grasp development. The simulations show that learning leads to emergence of units that have similar response properties as the AIP visual-dominant neurons. The results may have certain implications for the function of AIP neurons and thus should stimulate new experiments that cannot only verify/falsify the model but also advance our understanding of the visuomotor learning mechanisms employed by the primate brain.