Learning to control the body is a fundamental process in human development. Before acquiring goal-directed skills such as walking or reaching, infants undergo developmental phases characterised by spontaneous movements with no apparent objective. These motions are believed to shape the sensorimotor system by facilitating body-environment interaction. However, how this exploration contributes to sensorimotor structuring remains an open question. A major challenge in studying spontaneous movements has been the lack of appropriate comparative data. To address this, we introduce a synthetic data-driven approach to analyse infant motion. We analysed 20 infants comprising 270 spontaneous movement units from 12 RGB-D infant recordings (22.5 ± 5.96 movement units per infant) together with 206units from 8 RGB YouTube recordings (25.75 ± 6.34 movement units per infant), and compared these empirical datasets against two synthetic datasets. Our analysis revealed that spontaneous movements, both at the infant and cluster level, engaged arm dynamics more extensively than reaching-like motions and displayed acceleration distributions skewed towards trajectories optimised for maximal dynamic excitation. Furthermore, the kinematic space explored by infants exhibited significantly higher variability. These findings demonstrate that spontaneous movements are dynamically rich, providing emergent features potentially helpful for infants to explore movement possibilities and develop coordination and control.

Attempts to implement realistic body–environment interactions during functional magnetic resonance imaging (fMRI) experiments have developed expensive, hardly reproducible, and task-specific setups. Here, we introduce MOTUM (Motion Online Tracking Under MRI), a novel system that combines real-time kinematic tracking with immersive virtual reality, enabling participants to perform naturalistic movements inside the scanner. As a proof-of-concept, we tested MOTUM during a reach-to-grasp task with and without visual feedback of one’s hand (N = 7). The system achieved high-fidelity motion tracking, induced an intense immersive experience, evoked expected sensorimotor brain activations, and maintained high fMRI data quality. Standard fMRI control metrics were below the critical threshold in 99% of volumes, indicating that participants’ arm movements had minimal impact on head motion and data quality. Direct artifactual effects of arm and hand motion were also modest and well below critical limits. Critically, MOTUM allowed us to extract rich kinematic measures and link them to brain activity on a trial-by-trial basis. Parametric modulation analyses revealed that natural variations in movement dynamics significantly influenced neural responses in parietal, frontal, and occipital regions. In sum, MOTUM is a robust method to study motor control and beyond, enabling a new class of fMRI experiments that bridge ecological realism and experimental control, pushing current neuroimaging research toward real-life neuroscience.

Spatial sensorimotor mismatch increases the excitability of the primary somatosensory cortex: Insight from an EEG-virtual reality study.

Peripersonal space (PPS) is the region of space near the body, the multisensory interface where interactions with the environment predominantly occur. This space is represented by a specialized neural system that integrates tactile and external stimuli as a function of their distance from the body. Previous studies uncovered plastic and dynamical properties of PPS representation, links between PPS encoding and higher-level cognitive functions (e.g., social cognition), as well as its alterations in neurological and psychiatric disorders. These findings have expanded the definition of PPS and have led to the development of an array of computational models of PPS, addressing the why and how of PPS encoding. Although computational models are crucial for advancing our mechanistic and functional understanding of PPS representation, no prior work has reviewed these models. Here, we address this gap by analysing computational models of PPS, and proposing a taxonomy to classify them based on their level of description, capacity to reproduce empirical findings, and ability to generate novel predictions. This effort leads us to propose that PPS may be best understood as a system that detects spatiotemporal regularities in body-environment interactions, in order to predict potential future interactions. Hence, we suggest re-defining PPS as a unified spatiotemporal field that integrates not only spatial dimensions, but also temporal ones.

Linking cellular-level phenomena to brain architecture and behavior is a holy grail for theoretical and computational neuroscience. Advances in neuroinformatics have recently allowed scientists to embed spiking neural networks of the cerebellum with realistic neuron models and multiple synaptic plasticity rules into sensorimotor controllers. By minimizing the distance (error) between the desired and the actual sensory state, and exploiting the sensory prediction, the cerebellar network acquires knowledge about the body-environment interaction and generates corrective signals. In doing so, the cerebellum implements a generalized computational algorithm, allowing it "to learn to predict the timing between correlated events" in a rich set of behavioral contexts. Plastic changes evolve trial by trial and are distributed over multiple synapses, regulating the timing of neuronal discharge and fine-tuning high-speed movements on the millisecond timescale. Thus, spiking cerebellar built-in controllers, among various computational approaches to studying cerebellar function, are helping to reveal the cellular-level substrates of network learning and signal coding, opening new frontiers for predictive computing and autonomous learning in robots.

Once contact with a pathogen has occurred, it might be too late for the immune system to react. Here, we asked whether anticipatory neural responses might sense potential infections and signal to the immune system, priming it for a response. We show that potential contact with approaching infectious avatars, entering the peripersonal space in virtual reality, are anticipated by multisensory-motor areas and activate the salience network, as measured with psychophysics, electroencephalography and functional magnetic resonance imaging. This proactive neural anticipation instigates changes in both the frequency and activation of innate lymphoid cells, mirroring responses seen in actual infections. Alterations in connectivity patterns between infection-sensing brain regions and the hypothalamus, along with modulation of neural mediators, connect these effects to the hypothalamic-pituitary-adrenal axis. Neural network modeling recapitulates this neuro-immune cross-talk. These findings suggest an integrated neuro-immune reaction in humans toward infection threats, not solely following physical contact but already after breaching the functional boundary of body-environment interaction represented by the peripersonal space.

This study investigates the effects of embodied (breathing exercises) and cognitive interventions on adolescents' flow experience and cognition patterns. Using a mixed-methods design, 303 vocational high school students were assigned to three groups: Embodied Task Group (N = 108), Cognitive Task Group (N = 100), and Mental Health Course Group (N = 95). Experiment 1 employed a 3×2 Multivariate Analysis of Covariance (MANCOVA) design to compare flow experience dimensions, while Experiment 2 used Epistemic Network Analysis (ENA) to analyze diary entries. Results showed that the Embodied Task Group outperformed the Cognitive Task Group in "Unambiguous Feedback" (ηp2 = 0.01, a small effect) and had higher "Transformation of Time" (ηp2 = 0.01, a small effect) than the Mental Health Course Group. ENA revealed that the Embodied Group developed stronger body-environment interaction patterns, shifting cognition pattern from psychological evaluations to dynamic bodily processes over time. Conversely, the Cognitive Task Group maintained event-focused cognition with weaker mind-body integration. Findings highlight breathing exercises' potential to enhance flow experience through embodied awareness and multisensory processing, offering practical implications for mental health education by promoting embodied learning tasks to foster flow experience.

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

Undulation is a form of propulsion in which waves of bending propagate along an elongated, slender body. This locomotor strategy is used by organisms that span orders of magnitude in size and represent diverse habitats and species. Despite this diversity, common neuromechanical phenomena have been observed across biologically disparate undulators, as a result of common mechanics. For example, neuromechanical phase lags (NPL), a phenomenon where waves of muscle contraction travel at different speeds than the corresponding body bends, have been observed in fish, lamprey, and lizards. Existing theoretical descriptions of this phenomenon implicate the role of physical body-environment interactions. However, systematic experimental variation of body-environment interactions and measurement of the corresponding phase lags have not been performed. Using the nematode we measured phase lags across a range of environmental interaction regimes, performing calcium imaging in body wall muscles in fluids of varying viscosity and on agar. A mechanical model demonstrates that the measured phase lags are controlled by the relative strength of elastic torques within the body and resistive forces within the medium. We further show that the phase lags correspond with a difference in the wave number of the muscle activity and curvature patterns. Hence, the environmental forces that create NPL also act as a filter that shapes and modulates the gait articulated by the nervous system. Beyond nematodes, the simplicity of our model suggests that tuning body elasticity may serve as a general means of controlling the degree of mechanical wave modulation in other undulators. Published by the American Physical Society 2025

Purpose: This study aimed to investigate the effect of smart game-based interventions on visual-spatial development in preschool children. Methods and Materials: A quasi-experimental pretest-posttest design with a control group was employed. The sample consisted of 30 preschool children selected through cluster random sampling from two preschools in Tehran, with 15 participants assigned to the experimental group and 15 to the control group. The experimental group participated in eight structured smart game sessions, while the control group received no intervention. Visual-spatial development was assessed using the 51-item Visual-Spatial Development Questionnaire (Wachs, 2014), which includes subscales for sensory and bodily awareness, spatial localization, body-environment interaction, spatial maintenance, logical visual reasoning, and representational thinking. Data analysis was conducted using multivariate analysis of covariance (MANCOVA) to examine the differences between groups while controlling for pretest scores. Findings: The results indicated significant improvements in all visual-spatial subscales among children in the experimental group compared to the control group. Sensory and bodily awareness (F = 49.81, p < 0.01), spatial localization (F = 32.58, p < 0.01), body-environment interaction (F = 269.3, p < 0.01), spatial maintenance (F = 71.72, p < 0.01), logical visual reasoning (F = 173.4, p < 0.01), and representational thinking (F = 184.9, p < 0.01) all showed significant increases. The total visual-spatial development score also significantly improved (F = 839.01, p < 0.01), confirming the effectiveness of smart games in enhancing spatial cognitive abilities. Conclusion: The findings suggest that smart game-based interventions are highly effective in fostering visual-spatial development in preschool children. The integration of interactive, structured smart play in early childhood education can significantly enhance children's cognitive abilities, particularly in spatial reasoning and problem-solving skills.

This article addresses a major source of inequality and exclusion in contemporary transport, namely the mobility injustices faced by persons with diverse forms of disability. Our focus is on one aspect, the potential for velomobility – using a range of adaptive cycles – to increase mobility and, therefore, access to education, work, recreation, social encounters, and to enjoy freedom. We pursue this topic by, first, advancing the notion of epistemic justice as the form of justice most appropriate for addressing this issue. Second, we outline some recent technical developments of adaptive cycles, suggesting that considerable progress has been made in managing a range of impairments such that technical blockages do not currently appear to be the main obstacles to the wider adoption of velomobility. We then examine socio-economic, political, environmental, and cultural constraints, suggesting these socially constructed blockages present the biggest obstacles to advancing velomobility for persons with disability who might otherwise gain the diverse therapeutic benefits.

Abstract This paper deals with a corpus study of event-based time concepts. Here we investigate their use in time reckoning practices in modern Polish culture and language. The results presented here are based on a cognitive-conceptual and linguistic analysis of the Polish National Linguistic Corpus (NKJP). These results suggest that Polish has rich inventories of lexical and phrasal expressions for event-based time intervals based on environmental and celestial indices and social norms that have not previously been described from a cognitive, anthropological, and cultural perspective. Event-based time intervals found in domains of times of day and night, are here presented. We hypothesize that even when the Polish language employs conventional metric (calendar and clock) time units, the hybrid blends of day/night cycle and cardinal directions (north, south, east, west) could reveal an emergent form of time conceptualization. This conceptual and cultural hybridization is still common among the users of the Polish language and is indicative of complexity and dynamism in body-environment interactions. This interaction is schematized in twofold conceptual constructions of event-based and metric time, blending processes that may generate more creative enactions as an alternative to the mechanical 24-hour system.

Purpose Advancements in augmented reality (AR) technology have increased the interest in improving brand equity by creating AR-enhanced branding experiences. However, despite the potential of AR branding, knowledge regarding the underlying mechanisms required for AR features to build brand equity remains limited. Thus, we considered embodied cognition theory to investigate how designing AR features enhances brand equity, particularly through the vivid experience of AR-enhanced body–environment interaction. Specifically, this study focused on both environmental and physical AR features: environmental embedding (EE) and simulated physical control (SPC). Design/methodology/approach The results of an online experiment with 297 participants in a 2 (high/low EE) × 2 (high/low SPC) between-subjects design underwent analysis of covariance and structural equation modeling to examine the relationship between AR features, vividness and brand equity. We also examined the moderating effects of prior AR experiences. Findings The results support most hypotheses that the experience of vividness is a crucial mediator linking AR features (EE and SPC) and consumer-based brand equity. The findings confirm the influential role of prior AR experience in the moderated mediation model, implying that AR-enhanced brand equity occurs primarily among technically adept AR consumers. Originality/value This study contributes to the literature by identifying AR-enhanced body–environment interactions as a novel approach for enhancing brand equity. We also revealed the antecedents of vividness in the context of AR-enhanced branding. Moreover, the findings reveal that AR effects are contingent on consumers’ prior AR experience.

This study proposes an innovative approach to vocational education for farmers in China, integrating financial literacy enhancement as a key component. Recognising the critical role of financial competence in rural economic development, we address the current disconnect between traditional educational methods and the practical needs of rural residents in the context of China’s rapidly transforming agricultural sector. Our research introduces a novel educational model grounded in embodied cognition theory, designed to bridge the gap between abstract financial concepts and the lived experiences of farmers. The proposed model emphasises experiential learning and contextualised instruction, featuring a multi-stage process that includes observation of daily agricultural and financial activities, hands-on practical training, conceptual explanation linked to farm operations, and applied learning in real agricultural contexts. This approach prioritises body-environment interactions and practical reflection, leveraging farmers’ daily experiences to enhance both vocational skills and financial literacy. Key innovations include the use of body metaphor mapping to explain complex financial concepts, the integration of financial education into broader agricultural training, and the creation of embodied learning environments that simulate real-world farming scenarios. The model aims to simultaneously improve farmers’ professional competencies and financial decision-making abilities, fostering a more holistic approach to rural vocational education. Our study employed a mixed-methods approach, combining qualitative interviews with agricultural educators and quantitative surveys of farmers to develop and refine the model. Initial pilot implementations in select rural areas of China have shown promising results in improving farmers’ financial knowledge and decision-making skills. These findings offer practical insights for educators, policymakers, and agricultural extension services, demonstrating how vocational training can be reimagined to address the multifaceted needs of rural communities in China. By integrating financial literacy into broader vocational education, this approach has the potential to enhance farmers’ overall economic resilience and contribute to sustainable rural development. The study paves the way for more effective, contextually relevant educational strategies that can empower farmers with the skills needed to thrive in an increasingly complex agricultural and financial landscape.

Inspired by animals that co-adapt their brain and body to interact with the environment, we present a tendon-driven and over-actuated (i.e.njoint,n+1 actuators) bipedal robot that (i) exploits its backdrivable mechanical properties to manage body-environment interactions without explicit control,and(ii) uses a simple 3-layer neural network to learn to walk after only 2 min of 'natural' motor babbling (i.e. an exploration strategy that is compatible with leg and task dynamics; akin to childsplay). This brain-body collaboration first learns to produce feet cyclical movements 'in air' and, without further tuning, can produce locomotion when the biped is lowered to be in slight contact with the ground. In contrast, training with 2 min of 'naïve' motor babbling (i.e. an exploration strategy that ignores leg task dynamics), does not produce consistent cyclical movements 'in air', and produces erratic movements and no locomotion when in slight contact with the ground. When further lowering the biped and making the desired leg trajectories reach 1 cm below ground (causing the desired-vs-obtained trajectories error to be unavoidable), cyclical movements based on either natural or naïve babbling presented almost equally persistent trends, and locomotion emerged with naïve babbling. Therefore, we show how continual learning of walking in unforeseen circumstances can be driven by continual physical adaptation rooted in the backdrivable properties of the plant and enhanced by exploration strategies that exploit plant dynamics. Our studies also demonstrate that the bio-inspired co-design and co-adaptations of limbs and control strategies can produce locomotion without explicit control of trajectory errors.

Life mechanics, an emerging field, focuses on the self-organizing motions manipulated by the mind within living systems. This study introduces the concept of 'active force’, generated by mind-body-environment interactions, as a fundamental driver underlying these self-organizing movements. As an example, we propose a new set of control equations to model the self-pumping swing motion by incorporating the active force into Newton's second law. With this new mechanical framework, we inversely derived the total (i.e., responsive) active force due to the body-environment interaction from the child’s swing motions with rapid standing and squatting movements. It revealed a pulse-like pattern of the total active force along the swing length, driving changes in the radial speed and swing length. This force counteracts the resistance and propels the swing, which is not attainable by the stone. Consequently, the active force serves as the foundational principle for self-organization in living systems, offering a novel mechanical approach for understanding and predicting extraordinary movements (e.g., sports and rehabilitation) regulated by the mind (e.g., nervous system) in biological systems.

The current study investigates the body-environment interaction and exploits the passive viscoelastic properties of the body to perform undulatory locomotion. The investigations are carried out using a mathematical model based on a dry frictional environment, and the results are compared with the performance obtained using a physical model. The physical robot is a wheel-based modular system with flexible joints moving on different substrates. The influence of the spatial distribution of body stiffness on speed performance is also investigated. Our results suggest that the environment affects the performance of undulatory locomotion based on the distribution of body stiffness. While stiffness may vary with the environment, we have established a qualitative constitutive law that holds across environments. Specifically, we expect the stiffness distribution to exhibit either an ascending-descending or an ascending-plateau pattern along the length of the object, from head to tail. Furthermore, undulatory locomotion showed sensitivity to contact mechanics: solid-solid or solid-viscoelastic contact produced different locomotion kinematics. Our results elucidate how terrestrial limbless animals achieve undulatory locomotion performance by exploiting the passive properties of the environment and the body. Application of the results obtained may lead to better performing long-segmented robots that exploit the suitability of passive body dynamics and the properties of the environment in which they need to move.

It is necessary for the robot to use interactions from the environment through the body in order to adaptively move through various environments. When the robot is faced with a narrow path or a space with many pillars, it should be able to use its interaction with the environment to thin its own shape, i.e., it should have a flexible body. In contrast, in the case where we want the robot to move forward powerfully on a slope or uneven terrain (small steps), it is preferable for the robot to rigidify its own body and exert a strong propulsive force in response to interactions from the environment. In this paper, we present an idea of a mobile robot that can adjust its body flexibility (stiffness) to realize such adaptive behavior, and furthermore, we demonstrate its validity through experiments. Specifically, we propose a closed-link deformable mobile robot whose stiffness can be adjusted by indirectly driving joints. We design a function that increases the stiffness of the body by controlling the joints to follow the target angle quickly, and a function that decreases the stiffness of the body by controlling the joints to follow the angle slowly. The effectiveness of a robot that can adjust its stiffness is demonstrated through experiments of traversing narrow paths and steps. We also discuss propulsion control that takes advantage of the deformable mobile robot and its applicability to uneven slopes due to the flexibility of the links.

On special issue “Passive Locomotion from Body-Environment Interactions”

The continuous stream of multisensory information between the brain and the body during body–environment interactions is crucial to maintain the updated representation of the perceived dimensions of body parts (metric body representation) and the space around the body (the peripersonal space). Such flow of multisensory signals is often limited by upper limb sensorimotor deficits after stroke. This would suggest the presence of systematic distortions of metric body representation and peripersonal space in chronic patients with persistent sensorimotor deficits. We assessed metric body representation and peripersonal space representation in 60 chronic stroke patients with unilateral upper limb motor deficits, in comparison with age-matched healthy controls. We also administered a questionnaire capturing explicit feelings towards the affected limb. These novel measures were analysed with respect to patients’ clinical profiles and brain lesions to investigate the neural and functional origin of putative deficits. Stroke patients showed distortions in metric body representation of the affected limb, characterized by an underestimation of the arm length and an alteration of the arm global shape. A descriptive lesion analysis (subtraction analysis) suggests that these distortions may be more frequently associated with lesions involving the superior corona radiata and the superior frontal gyrus. Peripersonal space representation was also altered, with reduced multisensory facilitation for stimuli presented around the affected limb. These deficits were more common in patients reporting pain during motion. Explorative lesion analyses (subtraction analysis, disconnection maps) suggest that the peripersonal space distortions would be more frequently associated with lesions involving the parietal operculum and white matter frontoparietal connections. Moreover, patients reported altered feelings towards the affected limb, which were associated with right brain damage, proprioceptive deficits and a lower cognitive profile. These results reveal implicit and explicit distortions involving metric body representation, peripersonal space representation and the perception of the affected limb in chronic stroke patients. These findings might have important clinical implications for the longitudinal monitoring and the treatments of often-neglected deficits in body perception and representation.