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Athletes must transfer their performance when changing equipment due to innovative developments in sports technology. This kind of transfer has received only moderate attention. The aim of this study was to examine whether a mechanical change in sports equipment disturbs an athlete's performance and affects biomechanical and neurophysiological parameters. Therefore, an experiment was conducted in which 36 participants in three groups pedaled at 70 rounds per minute on a cycling ergometer with a circular and a noncircular (NC) chainring. The dependent variables were the total variability of the cadence, torque effectiveness, and muscle cocontraction (electromyographic cocontraction) of four antagonistic acting muscle pairs. Data were recorded during an acquisition phase, a transfer phase, and a retention phase. The results revealed that practice on a circular chainring induces a positive transfer on the NC chainring for total variability without a proactive interference effect. Torque effectiveness did not change within or between groups during the acquisition, transfer, and retention phases. Torque effectiveness and electromyographic cocontraction were not affected when the chainrings were altered from Day 1 to Day 2. During the retention phase, electromyographic cocontraction was higher when using the NC chainring, but the difference was small in absolute terms. The results regarding transfer and proactive interference seem to be strongly dependent on the movement task and the change in sports equipment. Transfer from the circular to NC chainring indicates refined neuromuscular control and improved movement coordination. [ABSTRACT FROM AUTHOR]

AbstractThe Post-Movement Beta Rebound (PMBR) is the increase in beta-band power after voluntary movement ends, but its specific role in cognitive processing is unclear. Current theory links PMBR with updates to internal models, mental frameworks that help anticipate and react to sensory feedback. However, research has not explored how reactivating a preexisting action plan, another source for internal model updates, might affect PMBR intensity. To address this gap, we recruited 20 participants (mean age 18.55 ± 0.51; 12 females) for an experiment involving isolated (single-step) or sequential (two-step) motor tasks based on predetermined cues. We compared PMBR after single-step movements with PMBR after the first movement in two-step tasks to assess the influence of a subsequent action on the PMBR power associated with the first action. The results show a significant increase in PMBR magnitude after the first movement in sequential tasks compared to the second action and the isolated movements. Notably, this increase is more pronounced for right-hand movements, suggesting lateralized brain activity in the left hemisphere. These findings indicate that PMBR is influenced not only by external stimuli but also by internal cognitive processes such as working memory. This insight enhances our understanding of PMBR's role in motor control, emphasizing the integration of both external and internal information. [ABSTRACT FROM AUTHOR]

The impact of ethanol on the fetus is a significant concern as an estimated 2–5% of live births may be affected by prenatal alcohol exposure. This exposure can lead to various functional and structural abnormalities in the cerebral cortex, basal ganglia, diencephalon, and cerebellum, resulting in region-specific symptoms. The deficits relate to the motor and cognitive domains, affecting, in particular, general intelligence, attention, executive functions, language, memory, visual perception, and social skills—collectively called the fetal alcohol spectrum disorder (FASD). Recent studies suggest that damage to the developing cerebellum (in form of alcohol exposure) can impair the cortical targets of the cerebello-thalamo-cortical tract. This malfunction in the cerebello-cerebral loop optimization may be due to disruptions in the formation of the foundational elements of the internal model within the developing cerebellum. Alcohol exposure targets multiple nodes in the reciprocal loops between the cerebellum and cerebral cortex. Here, we examine the possibility that prenatal alcohol exposure damages the developing cerebellum and disrupts the connectivity within the cerebello-cerebral neuronal circuits, exacerbating FASD-related cortical dysfunctions. We propose that malfunctions between cerebellar internal model (critically involved in predictions) and cerebral regions contribute to the deficits observed in FASD. Given the major role of the cerebellum in motor, cognitive, and affective functions, we suggest that therapies should target these malfunctions to mitigate the burden of FASD. We discuss the concept of therapies oriented towards malfunctioning cerebello-cerebral loops (TOMCCLs), emphasizing anti-inflammatory strategies and treatments aimed at modulating cerebellar myelination to restore optimal and predictive cerebello-cerebral functions. [ABSTRACT FROM AUTHOR]

The good regulator theorem and the internal model principle are sometimes cited as mathematical proofs that an agent needs an internal model of the world in order to have an optimal policy. However, these principles rely on a definition of “internal model” that is far too permissive, applying even to cases of systems that do not use an internal model. As a result, these principles do not provide evidence (let alone a proof) that internal models are necessary. The paper also diagnoses what is missing in the GRT and IMP definitions of internal model, which is that models need to make predictions that represent variables in the target system (and these representations need to be usable by an agent so as to guide behavior). [ABSTRACT FROM AUTHOR]

Unlike ballistic movements such as reaching, little is known about how aging affects tracking performance. The current study initially aimed at confirming the differences in tracking performance between younger and older people. The experiment included 14 young and 10 older subjects who were instructed to perform a circular tracking task on the fronto-parallel and sagittal planes with a visible target in the first half interval (called the feedback (FB) interval) and an invisible target disappearing and reappearing in the remaining interval (called the feedforward (FF) interval). The results demonstrated that the aging effect was sufficient to deteriorate tracking performance, regardless of the environment. However, the older group’s performance was not significantly influenced by the depth movement, whereas the younger group’s performance significantly deteriorated during the FB interval. Finally, the differences in errors between the FF and FB intervals for each movement plane were investigated, demonstrating the vulnerability of the internal model used for each plane to the visibility of the target. The younger group demonstrated a significant difference between the fronto-parallel and sagittal planes; however, the older group exhibited no significant difference, indicating that the younger group acquired an internal model sensitive to visual information. We confirmed aging-induced tracking performance degradation and highlighted the impact of aging on tracking performance, resulting in an increased understanding of cerebellar motor control rather than conventional ballistic movements.

Recent work showed that individuals with cerebellar degeneration could leverage intact reinforcement learning (RL) to alter their movement. However, there was marked inter-individual variability in learning, and the factors underlying it were unclear. Cerebellum-dependent sensory prediction may contribute to RL in motor contexts by enhancing body state estimates, which are necessary to solve the credit-assignment problem. The objective of this study was to test the relationship between the predictive component of state estimation and RL in individuals with cerebellar degeneration. Individuals with cerebellar degeneration and neurotypical control participants completed two tasks: an RL task that required them to alter the angle of reaching movements and a state estimation task that tested the somatosensory perception of active and passive movement. The state estimation task permitted the calculation of the active benefit shown by each participant, which is thought to reflect the cerebellum-dependent predictive component of state estimation. We found that the cerebellar and control groups showed similar magnitudes of learning with reinforcement and active benefit on average, but there was substantial variability across individuals. Using multiple regression, we assessed potential predictors of RL. Our analysis included active benefit, somatosensory acuity, clinical ataxia severity, movement variability, movement speed, and age. We found a significant relationship in which greater active benefit predicted better learning with reinforcement in the cerebellar, but not the control group. No other variables showed significant relationships with learning. Overall, our results support the hypothesis that the integrity of sensory prediction is a strong predictor of RL after cerebellar damage. [ABSTRACT FROM AUTHOR]

Lateral predictive coding is a recurrent neural network that creates energy-efficient internal representations by exploiting statistical regularity in sensory inputs. Here, we analytically investigate the trade-off between information robustness and energy in a linear model of lateral predictive coding and numerically minimize a free energy quantity. We observed several phase transitions in the synaptic weight matrix, particularly a continuous transition that breaks reciprocity and permutation symmetry and builds cyclic dominance and a discontinuous transition with the associated sudden emergence of tight balance between excitatory and inhibitory interactions. The optimal network follows an ideal gas law over an extended temperature range and saturates the efficiency upper bound of energy use. These results provide theoretical insights into the emergence and evolution of complex internal models in predictive processing systems. [ABSTRACT FROM AUTHOR]

Animals must distinguish the sensory consequences of self-generated movements (reafference) from those of other-generated movements (exafference). Only self-generated movements entail the production of motor copies (i.e., corollary discharges), which are compared with reafference in the cerebellum to compute predictive or internal models of movement. Internal models emerge gradually over the first three postnatal weeks in rats through a process that is not yet fully understood. Previously, we demonstrated in postnatal day (P) 8 and P12 rats that precerebellar nuclei convey corollary discharge and reafference to the cerebellum during active (REM) sleep when pups produce limb twitches. Here, recording from a deep cerebellar nucleus (interpositus, IP) in P12 rats of both sexes, we compared reafferent and exafferent responses with twitches and limb stimulations, respectively. As expected, most IP units showed robust responses to twitches. However, in contrast with other sensory structures throughout the brain, relatively few IP units showed exafferent responses. Upon finding that exafferent responses occurred in pups under urethane anesthesia, we hypothesized that urethane inhibits cerebellar cortical cells, thereby disinhibiting exafferent responses in IP. In support of this hypothesis, ablating cortical tissue dorsal to IP mimicked the effects of urethane on exafference. Finally, the results suggest that twitch-related corollary discharge and reafference are conveyed simultaneously and in parallel to cerebellar cortex and IP. Based on these results, we propose that twitches provide opportunities for the nascent cerebellum to integrate somatotopically organized corollary discharge and reafference, thereby enabling the development of closed-loop circuits and, subsequently, internal models. [ABSTRACT FROM AUTHOR]

In the future, a situation in which operators switch between different machine classes in teleoperated hydraulic excavator is envisioned. In such a case, because the classes have different dynamic characteristics, the operator is expected to acquire an internal model for each class and switch models each time the operator switches between machines. However, in the case of teleoperated hydraulic excavator, the operator cannot obtain information such as the size and dynamic characteristics of the machine to be switched to; thus, the operator may not be able to switch internal models properly, which may affect the operation efficiency. Therefore, this study proposes a method in which images and videos are used to present the dynamic characteristics of the next machine to be operated during machine changeover in teleoperated hydraulic excavator. To verify the effectiveness of the proposed method, a simulator that imitates teleoperated hydraulic excavator was built and tested on test subjects. The swing operation time significantly increased when the machine was switched without presentation, compared with the case without switching. Meanwhile, the proposed method did not increase the swing operation time associated with machine switching, suggesting its effectiveness. The video presentation method was more effective than the image presentation method for suppressing the increase in swing operation time, indicating that the operator can immediately switch to an appropriate internal model with the presentation of the dynamic characteristics of the machine in advance using video. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A symmetric gait pattern in humans reflects near-identical movement in bilateral limbs during walking. However, little is known about how gait symmetry changes on different inclines. This study aimed to address this knowledge gap using the central pattern generator and internal model hypotheses. Eighteen healthy young adults underwent five 2-minute walking trials (inclines of +15%, +8%, 0%, −8%, and −15%). Dependent variables included step time, step length, step width, maximum heel clearance, time to peaks of maximum heel clearance, their corresponding coefficients of variation (CV), and respective symmetry indices (SI). Significant differences were observed in SI of step length (p = .022), step length variability (p

This paper develops a discrete-time repetitive control (RC) system with a fractional-delay internal model. Unlike the conventional RC, the time delay for constructing the internal model is not necessarily an integer, implying that the time delay is allowed to be fractional. In this work, a fractional delay-based repetitive control system is presented. Firstly, the fractional time delay is calculated based on the information of the reference or disturbance along with the associated sampling period. Secondly, the fractional time delay is divided into two parts, i.e., the large integer and small fractional delays. Finally, the fractional part is realized by a stable and causal infinite impulse response (IIR) filter, whose coefficients are calculated using the Thiran formula. The controller structure and stability analysis are developed for three different RCs, namely a fractional-delay general RC (FD-GRC), a fractional delay-odd harmonics RC (FD-OHRC), and a fractional delay-(6 l ±1) RC (FD-(6 l ±1)RC). Experiments and comparison studies of the integer delay-based RCs are conducted to verify the effectiveness and improved tracking accuracy of the proposed method. • The fractional delay-based internal models are formulated for three different RCs. • The proposed delay-based RCs are referred as FD-GRC, FD-OHRC, and FD- (6 l ± 1) RC. • Controller architectures and stability analyses of the proposed RCs are developed. • The fractional delay part in the controller is implemented by an IIR filter. • The IIR filter coefficients are obtained by using Thiran formula. [ABSTRACT FROM AUTHOR]

Internal models are essential for the production of accurate movements. The accuracy of saccadic eye movements is thought to be mediated by an internal model of oculomotor mechanics encoded in the cerebellum. The cerebellum may also be part of a feedback loop that predicts the displacement of the eyes and compares it to the desired displacement in real time to ensure that saccades land on target. To investigate the role of the cerebellum in these two aspects of saccade production, we delivered saccade-triggered light pulses to channelrhodopsin-2-expressing Purkinje cells in the oculomotor vermis (OMV) of two male macaque monkeys. Light pulses delivered during the acceleration phase of ipsiversive saccades slowed the deceleration phase. The long latency of these effects and their scaling with light pulse duration are consistent with an integration of neural signals at or downstreamof the stimulation site. In contrast, light pulses delivered during contraversive saccades reduced saccade velocity at short latency and were followed by a compensatory reacceleration which caused gaze to land on or near the target. We conclude that the contribution of the OMV to saccade production depends on saccade direction; the ipsilateral OMV is part of a forward model that predicts eye displacement, whereas the contralateral OMV is part of an inverse model that creates the force required to move the eyes with optimal peak velocity for the intended displacement. [ABSTRACT FROM AUTHOR]

In predictive processing frameworks, 'global model failures' refer to instances where predictions about the world are strongly violated, resulting in large prediction errors. Neuromodulatory systems have long been implicated in learning processes and in mediating prediction error-driven learning. Recent findings support the idea that the locus coeruleus (LC) – a brain-wide neuromodulatory system – signals several types of prediction error. The LC is proposed to integrate prediction errors to compute global model failures, a computation which can explain the diverse roles of the LC in learning, behavior, and arousal. Combining an understanding of LC global model failure signaling with readouts of LC activity may reveal how predictive processing differs across individuals. Predictive processing models posit that brains constantly attempt to predict their sensory inputs. Prediction errors signal when these predictions are incorrect and are thought to be instructive signals that drive corrective plasticity. Recent findings support the idea that the locus coeruleus (LC) – a brain-wide neuromodulatory system – signals several types of prediction error. I discuss how these findings support models proposing that the LC signals global model failures: instances where predictions about the world are strongly violated. Focusing on the cortex, I explore the utility of this signal in learning rate control, how the LC circuit may compute the signal, and how this view may aid our understanding of neurodivergence. [ABSTRACT FROM AUTHOR]

The interplay between computational neuroscience and humanoid robotics, both historically and looking forward, is presented, with a particular focus on two significant projects: the ERATO Kawato Dynamic Brain and the ICORP Computational Brain. Discussion revolves around pivotal brain regions, namely the cerebellum, basal ganglia, and cerebrum, and the associated computational models. [ABSTRACT FROM AUTHOR]

This research introduces two controllers for non-self-regulating systems with dominant time delays. These controllers are designed using sliding mode control principles with built-in predictive structures. Previous studies have already demonstrated the effectiveness of these controllers in non-self-regulating systems with non-dominant delays. However, in this study, we investigate the extension of these controllers to systems with time-dominated time-delay processes. To thoroughly evaluate the performance of both approaches, we conduct extensive tests involving set-point changes and perturbations. The results indicate that the proposed methods outperform conventional techniques in both scenarios. The evaluation metrics used include integral squared error (ISE), total control variation (TVU), settling time, and maximum overshoot. In general, the new dynamic sliding mode controllers exhibit superior capabilities in handling systems with significant delays compared to standard controllers.

The impact of ethanol on the fetus is a significant concern as an estimated 2–5% of live births may be affected by prenatal alcohol exposure. This exposure can lead to various functional and structural abnormalities in the cerebral cortex, basal ganglia, diencephalon, and cerebellum, resulting in region-specific symptoms. The deficits relate to the motor and cognitive domains, affecting, in particular, general intelligence, attention, executive functions, language, memory, visual perception, and social skills—collectively called the fetal alcohol spectrum disorder (FASD). Recent studies suggest that damage to the developing cerebellum (in form of alcohol exposure) can impair the cortical targets of the cerebello-thalamo-cortical tract. This malfunction in the cerebello-cerebral loop optimization may be due to disruptions in the formation of the foundational elements of the internal model within the developing cerebellum. Alcohol exposure targets multiple nodes in the reciprocal loops between the cerebellum and cerebral cortex. Here, we examine the possibility that prenatal alcohol exposure damages the developing cerebellum and disrupts the connectivity within the cerebello-cerebral neuronal circuits, exacerbating FASD-related cortical dysfunctions. We propose that malfunctions between cerebellar internal model (critically involved in predictions) and cerebral regions contribute to the deficits observed in FASD. Given the major role of the cerebellum in motor, cognitive, and affective functions, we suggest that therapies should target these malfunctions to mitigate the burden of FASD. We discuss the concept of therapies oriented towards malfunctioning cerebello-cerebral loops (TOMCCLs), emphasizing anti-inflammatory strategies and treatments aimed at modulating cerebellar myelination to restore optimal and predictive cerebello-cerebral functions.

Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of robot environments, as they heavily rely on the accuracy of physical models. In this paper, we introduce an innovative approach to robot arm motion control, inspired by the cognitive mechanism of inner rehearsal observed in humans. The core concept revolves around the robot's ability to predict or evaluate the outcomes of motion commands before execution. This approach enhances the learning efficiency of models and reduces the mechanical wear on robots caused by excessive physical executions. We conduct experiments using the Baxter robot in simulation and the humanoid robot PKU-HR6.0 II in a real environment to demonstrate the effectiveness and efficiency of our proposed approach for robot arm reaching across different platforms. The internal models converge quickly and the average error distance between the target and the end-effector on the two platforms is reduced by 80% and 38%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive observer for an active leader and designed a distributed observer‐based control law for the formation control of networked uncertain offshore vessels under the switching topologies. The design of the observer, only depending on the output of the leader system, is featured with the ability of accurately estimating the output of the leader system with an unknown input over the time‐varying communication network, satisfying the periodic switching condition. Then based on the output‐based observer, the formation problem of multiple offshore vessels is converted into the stabilization problem of a multi‐input multi‐output augmented system by further combining the internal model technique and performing a coordinate and input transformation. The original problem is finally solved through constructing a stabilizer for the time‐varying uncertain augmented system, and the efficiency of our design is validated by the formation control of a group of offshore vessels. [ABSTRACT FROM AUTHOR]

This article presents a systematic method for designing discrete-time low-order sliding mode repetitive controller for a class of uncertain linear systems. A linear system considered in this class is perturbed by band-limited periodic disturbances and parametric uncertainties. The digital controller design method we propose combines two control strategies, namely the repetitive control and the sliding mode control techniques. The proposed controller is required to be low order and is intended to reject the periodic disturbances with specific dominant frequencies and to establish robustness against plant parametric uncertainties. Moreover, the proposed method yields a repetitive controller with only a small number of delay terms and can perform fast transient responses. We show through mathematical analyses that the linear system applying the low-order sliding mode repetitive controller satisfies sliding mode stability and robustness criteria. In addition, we demonstrate via a numerical example that a servomotor controlled by the low-order sliding mode repetitive controller can track a frequency-shifted triangular signal with a short transient period and minor errors. Other examples are also presented and show that the low-order sliding mode repetitive controller exhibits better performance than other relevant control methods. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed resource allocation problem of a second-order multi-agent system with external disturbances. The global network resource equality constraint is considered. A distributed algorithm with an exponential convergence rate is proposed based on an internal model for a known bounded disturbance. By using the active disturbance approach to design a reduced-order extended state observer, the proposed algorithm is modified to reject an unknown external disturbance. Moreover, exponential convergence is achieved through the Lyapunov theory when the disturbance is constant. When the differential of the disturbance is bounded, by adjusting the control parameter of the observer, the proposed algorithm converges to an arbitrarily small neighbourhood of the optimal solution. Finally, our simulation results demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The right posterolateral portions of the cerebellum (crus-I/II) are involved in language processing. However, their functional role in language remains unknown. The cerebellum is hypothesized to acquire an internal model that is a functional copy of mental representations in the cerebrum and to contribute to cognitive function. In this research, based on the cerebellar internal model hypothesis, we conducted task-based and resting-state functional magnetic resonance imaging (fMRI) experiments to investigate the role of the cerebellum in the syntactic and semantic aspects of comprehension of sentences. In a syntactic task, participants read sentences with center-embedded hierarchical structures. The hierarchical level-dependent activity was found in the right crus-I as well as Broca's area (p < 0.05, voxel-based small volume correction (SVC)). In a semantic task, the participants read three types of sentences for investigation of sentence-level, phrase-level, and word-level semantic processing. The semantic level-dependent activity was found in the right crus-II as well as in the left anterior temporal lobe and the left angular gyrus (p < 0.05, voxel-based SVC). Moreover, the right crus-I/II showed significant activity when the cognitive load was high. Resting-state fMRI demonstrated intrinsic functional connectivity between the right crus-I/II and language-related regions in the left cerebrum (p < 0.05, voxel-based SVC). These findings suggest that the right crus-I and crus-II are involved, respectively, in the syntactic and semantic aspects of sentence processing. The cerebellum assists processing of language in the cerebrum when the cognitive load is high. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Animals interact with their environment through mechanically active, mobile sensors. The efficient use of these sensory organs implies the ability to track their position; otherwise, perceptual stability or prehension would be profoundly impeded. The nervous system may keep track of the position of a sensorimotor organ via two complementary feedback mechanisms--peripheral reafference (external, sensory feedback) and efference copy (internal feedback). Yet, the potential contributions of these mechanisms remain largely unexplored. By training male rats to place one of their vibrissae within a predetermined angular range without contact, a task that depends on knowledge of vibrissa position relative to their face, we found that peripheral reafference is not required. The presence of motor cortex is not required either, except in the absence of peripheral reafference to maintain motor stability. Finally, the red nucleus, which receives descending inputs from motor cortex and cerebellum and projects to facial motoneurons, is critically involved in the execution of the vibrissa positioning task. All told, our results point toward the existence of an internal model that requires either peripheral reafference or motor cortex to optimally drive voluntary motion. [ABSTRACT FROM AUTHOR]

This paper addresses the adaptive inverse optimal output regulation problem for a class of uncertain nonlinear systems driven by an exosystem. The unknown parameters, internal disturbances, and unmeasured states are contained in the nonlinear system. Firstly, the output regulation problem is decomposed into a feedforward control design problem which can be solved by the internal model based on the output regulation theory, and an adaptive inverse optimal stabilization problem. Then an auxiliary system is designed, and a new state observer related to the auxiliary system is given. By combining adaptive control technology and inverse optimal control method, a novel adaptive output feedback inverse optimal controller is developed to make the output of the system track the reference signal fast. With this control strategy, all the signals of the closed-loop system are uniformly ultimately bounded (UUB), and the newly well-defined cost functional which is connected with the auxiliary system and the controller can be minimized. Finally, a simulation case is put forward to verify the feasibility of the newly raised controller and the state observer.

Delusions of control in schizophrenia are characterized by the striking feeling that one's actions are controlled by external forces. We here tested qualitative predictions inspired by Bayesian causal inference models, which suggest that such misattributions of agency should lead to decreased intentional binding. Intentional binding refers to the phenomenon that subjects perceive a compression of time between their intentional actions and consequent sensory events. We demonstrate that patients with delusions of control perceived less self-agency in our intentional binding task. This effect was accompanied by significant reductions of intentional binding as compared to healthy controls and patients without delusions. Furthermore, the strength of delusions of control tightly correlated with decreases in intentional binding. Our study validated a critical prediction of Bayesian accounts of intentional binding, namely that a pathological reduction of the prior likelihood of a causal relation between one's actions and consequent sensory events--here captured by delusions of control--should lead to lesser intentional binding. Moreover, our study highlights the import of an intact perception of temporal contiguity between actions and their effects for the sense of agency. [ABSTRACT FROM AUTHOR]

The introduction of (fully) automated vehicles has generated a re-interest in motion sickness, given that passengers suffer much more from motion sickness compared to car drivers. A suggested solution is to improve the anticipation of passive self-motion via cues that alert passengers of changes in the upcoming motion trajectory. We already know that auditory or visual cues can mitigate motion sickness. In this study, we used anticipatory vibrotactile cues that do not interfere with the (audio)visual tasks passengers may want to perform. We wanted to investigate (1) whether anticipatory vibrotactile cues mitigate motion sickness, and (2) whether the timing of the cue is of influence. We therefore exposed participants to four sessions on a linear sled with displacements unpredictable in motion onset. In three sessions, an anticipatory cue was presented 0.33, 1, or 3 s prior to the onset of forward motion. Using a new pre-registered measure, we quantified the reduction in motion sickness across multiple sickness scores in these sessions relative to a control session. Under the chosen experimental conditions, our results did not show a significant mitigation of motion sickness by the anticipatory vibrotactile cues, irrespective of their timing. Participants yet indicated that the cues were helpful. Considering that motion sickness is influenced by the unpredictability of displacements, vibrotactile cues may mitigate sickness when motions have more (unpredictable) variability than those studied here. [ABSTRACT FROM AUTHOR]

The problem of compensation of external disturbing influences for MIMO system with input delay is important and relevant. A solution to this problem is proposed in the problems of dynamic objects control and in a number of others. The proposed method is based on the principle of an internal model and requires the identification of perturbation parameters. At the first stage, a scheme for extracting a disturbance is presented which is represented as a sinusoidal signal with an unknown frequency, amplitude, and phase. At the second stage, the problem of identifying the frequencies of a sinusoidal and multisinusoidal signal is solved. In the last stage, an algorithm for stabilizing the state of the object to zero is developed using feedback. A new scheme for compensating external disturbances for a MIMO system with an input delay is proposed. A new algorithm for identifying the frequencies of a multisinusoidal signal is proposed. The analysis of the possibilities of the proposed estimation method using computer simulation in the MATLAB Simulink environment is carried out. The developed method can be effectively applied to a wide class of applied tasks related to the control of robots and robotic manipulators for various purposes.

The main goal of insurance company management is to increase shareholdersâ value and implement a strategy that promotes sustainable growth of the company. Well-known possible measures intended to achieve that goal are as follows: share price, economic value, market capitalisation, gross premiums earned and solvency ratio. These measures include efficient capital management as capital expenses could be a major cost position depending on risk appetite and the extent of capital needed to support it. This research focuses on non-life insurers for reserve risk modelling. In the current study, a more accurate risk quantification model has been developed than the standard model provided by the EU regulator under the Solvency II framework. The proposed model provides capital cost gains as well. A case study based on non-life real data set with underwriting in the Baltic countries is discussed with inclusion of pandemic trends that had an impact on economies and customer behaviours. The study considers different non-life reserve distributions for each insurance business line, risk aggregation and the way of choosing the most appropriate type of copula model for non-life reserve risk. Adequate capital is calculated by applying value at risk at 99.5%, which is mandatory in the EU market. The study considers which selected tests have to be implemented in order to choose the most appropriate copula model for reserve risk.

Objectives: Multiple step saccades (MSSs) are an atypical form of saccade that consists of a series of small-amplitude saccades. It has been argued that the mechanism for generating MSS is due to the automatic saccadic plan. This argument was based on the observation that trials with MSS had shorter saccadic latency than trials without MSS in the reactive saccades. However, the validity of this argument has never been verified by other saccadic tasks. Alternatively, we and other researchers have speculated that the function of MSS is the same as that of the corrective saccade (CS), i.e., to correct saccadic errors. Thus, we propose that the function of the MSS is also to rectify saccadic errors and generated by forward internal models. The objective of the present study is to examine whether the automatic theory is universally applicable for the generation of MSSs in various saccadic tasks and to seek other possible mechanisms, such as error correction by forward internal models. Methods: Fifty young healthy subjects (YHSs) and fifty elderly healthy subjects (EHSs) were recruited in the present study. The task paradigms were prosaccade (PS), anti-saccade (AS) and memory-guided saccade (MGS) tasks. Results: Saccadic latency in trials with MSS was shorter than without MSS in the PS task but similar in the AS and MGS tasks. The intersaccadic intervals (ISI) were similar among the three tasks in both YHSs and EHSs. Conclusion: Our results indicate that the automatic theory is not a universal mechanism. Instead, the forward internal model for saccadic error correction might be an important mechanism. [ABSTRACT FROM AUTHOR]

During human walking the whole body center-of-mass (COM) trajectory may be a control objective, a goal the central nervous system uses to plan and regulate movement. Our previous observation, that after practice walking in a novel laterally directed force field people adapt a COM trajectory similar to their normal trajectory, supports this idea. However, our prior work only presented data demonstrating changes in COM trajectory in response to a single force field. To evaluate whether this phenomena is robust, in the present study we present new data demonstrating that people adapt their COM trajectory in a similar manner when the direction of the external force field is changed resulting in drastically different lower limb joint dynamics. Specifically, we applied a continuous, left-directed force field (in the previous experiment the force field was applied to the right) to the COM as participants performed repeated trials of a discrete walking task. We again hypothesized that with practice walking in the force field people would adapt a COM trajectory that was similar to their baseline performance and exhibit aftereffects, deviation of their COM trajectory in the opposite direction of force field, when the field was unexpectedly removed. These hypotheses were supported and suggest that participants formed an internal model to control their COM trajectory. Collectively these findings demonstrate that people adapt their gait patterns to anticipate consistent aspects of the external environment. These findings suggest that this response is robust to force fields applied in multiple directions that may require substantially different neural control. [ABSTRACT FROM AUTHOR]

Robot arm motion control is a fundamental aspect of robot capabilities, with arm reaching ability serving as the foundation for complex arm manipulation tasks. However, traditional inverse kinematics-based methods for robot arm reaching struggle to cope with the increasing complexity and diversity of robot environments, as they heavily rely on the accuracy of physical models. In this paper, we introduce an innovative approach to robot arm motion control, inspired by the cognitive mechanism of inner rehearsal observed in humans. The core concept revolves around the robot’s ability to predict or evaluate the outcomes of motion commands before execution. This approach enhances the learning efficiency of models and reduces the mechanical wear on robots caused by excessive physical executions. We conduct experiments using the Baxter robot in simulation and the humanoid robot PKU-HR6.0 II in a real environment to demonstrate the effectiveness and efficiency of our proposed approach for robot arm reaching across different platforms. The internal models converge quickly and the average error distance between the target and the end-effector on the two platforms is reduced by 80% and 38%, respectively.

The brain's computations for active and passive self-motion estimation can be unified with a single model that optimally combines vestibular and visual signals with sensory predictions based on efference copies. It is unknown whether this theoretical framework also applies to the integration of artificial motor signals, such as those that occur when driving a car, or whether self-motion estimation in this situation relies on sole feedback control. Here, we examined if training humans to control a self-motion platform leads to the construction of an accurate internal model of the mapping between the steering movement and the vestibular reafference. Participants (n = 15) sat on a linear motion platform and actively controlled the platform's velocity using a steering wheel to translate their body to a memorized visual target (motion condition). We compared their steering behavior to that of participants (n = 15) who remained stationary and instead aligned a nonvisible line with the target (stationary condition). To probe learning, the gain between the steering wheel angle and the platform or line velocity changed abruptly twice during the experiment. These gain changes were virtually undetectable in the displacement error in the motion condition, whereas clear deviations were observed in the stationary condition, showing that participants in the motion condition made within-trial changes to their steering behavior. We conclude that vestibular feedback allows not only the online control of steering but also a rapid adaptation to the gain changes to update the brain's internal model of the mapping between the steering movement and the vestibular reafference. [ABSTRACT FROM AUTHOR]

This paper presents a novel design of discrete-time dual internal model-based repetitive control systems. The design strategy is accomplished by combining general and high-order modified repetitive control schemes for simultaneous tracking repetitive tasks and rejection of uncertain periodic disturbances. The proposed controller is constructed from two different discrete-time internal models, rendering a dual internal model-based repetitive controller (DIMRC). The first internal model is intended to track repetitive commands with a fixed fundamental frequency. Meanwhile, the second internal model is coupled to compensate for an exogenous periodic disturbance with an uncertain frequency. The controller structure, stability conditions, and convergence analysis are discussed in this paper. The performance of the proposed controller is validated through simulation studies showing accurate tracking and excellent disturbance rejection simultaneously. [ABSTRACT FROM AUTHOR]

Summary: In practical engineering, load torque disturbance and parameter uncertainties are two important factors, which may deteriorate the tracking accuracy of permanent magnet synchronous motor (PMSM) servo system. Therefore, its global position tracking control problem is a challenging task when the load torque disturbance is time‐varying and the motor parameters are unknown. In this article, an internal model controller based on global robust output regulation (GROR) theory is proposed to achieve this control objective. In particular, we first formulate the global position tracking control problem as a GROR problem. Then, the GROR problem is converted into a global robust stabilization problem of an augmented system by constructing an appropriate internal model. Finally, we can stabilize the augmented system by a global stabilization controller instead of local stabilization controller used in the recent work, which guarantees global position tracking and disturbance rejection of PMSM servo system. The excellent position tracking performance of our design is demonstrated by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this article, an internal‐model‐based event‐triggered control is proposed for the rendezvous problem of linear uncertain multi‐agent systems. Our approach is not only robust to parametric uncertainty and external disturbances, but also capable of preserving the connectivity of the position‐dependent time‐varying communication network. The event‐triggered mechanism is in a piecewise constant form and thus can be directly implemented in the digital platform. Technically, different from traditional internal model design, a class of distributed dynamic compensators are designed based on sampled relative position and velocity signals to convert the rendezvous problem into a stabilization with connectivity problem of a special augmented system, which is then solved by a piecewise constant stabilizer. Finally, it is proved that the rendezvous problem of the original system is solvable by stabilizing such augmented system and excluding Zeno behavior. [ABSTRACT FROM AUTHOR]

In targeted movements of the hand, descending activation patterns must not only generate muscle activation but also adjust spinal reflexes from stabilizing the initial to stabilizing the final postural state. We estimate descending activation patterns that change minimally while generating a targeted movement within a given movement time based on a model of the biomechanics, the muscle dynamics, and the stretch reflex. The estimated descending activation patterns predict human movement trajectories quite well. Their temporal structure varies across workspace and with movement speed, from monotonic profiles for slow movements to nonmonotonic profiles for fast movements. Descending activation patterns at different speeds thus do not result from a mere rescaling of invariant templates but reflect varying needs to compensate for interaction torques and muscle dynamics. The virtual attractor trajectories, on which active muscle torques are zero, lie within reachable workspace and are largely invariant when represented in end-effector coordinates. Their temporal structure along movement direction changes from linear ramps to "N-shaped" profiles with increasing movement speed. [ABSTRACT FROM AUTHOR]

Model-based reinforcement learning enables an agent to learn in variable environments and tasks by optimizing its actions based on the predicted states and outcomes. This mechanism has also been considered in the brain. However, exactly how the brain selects an appropriate model for confronting environments has remained unclear. Here, we investigated the model selection algorithm in the human brain during a reinforcement learning task. One primary theory of model selection in the brain is based on sensory prediction errors. Here, we compared this theory with an alternative possibility of internal model selection with reward prediction errors. To compare these two theories, we devised a switching experiment from a first-order Markov decision process to a second-order Markov decision process that provides either reward- or sensory prediction error regarding environmental change. We tested two representative computational models driven by different prediction errors. One is the sensory prediction-error-driven Bayesian algorithm, which has been discussed as a representative internal model selection algorithm in the animal reinforcement learning task. The other is the reward-prediction-error-driven policy gradient algorithm. We compared the simulation results of these two computational models with human reinforcement learning behaviors. The model fitting result supports that the policy gradient algorithm is preferable to the Bayesian algorithm. This suggests that the human brain employs the reward prediction error to select an appropriate internal model in the reinforcement learning task. [ABSTRACT FROM AUTHOR]