Why can we learn to feel ownership over some non-human body parts, but not others? One potentially critical yet underexplored factor is the functionality of the given body part. This study addressed the contribution of body function to embodiment by examining how participants embodied a dynamic virtual arm with altered function. To understand the full extent of how flexible embodiment might be, participants included children as well as adults. Participants used a virtual arm to feed animals within an immersive environment. Reach functionality was systematically manipulated by adjusting the arm's final reaching length from a normal condition to be slightly reduced; slightly increased; or markedly increased. Only extreme functionality alterations significantly reduced subjective ratings of limb ownership, particularly in adults. Motor adaptations, for example in arm velocity, largely reflected a smooth integration of the virtual arm's perceived capabilities with the participants' own physical limitations. Children responded to the altered embodiment with more cautious, less refined movement strategies than adults. Across ages, exposure to functionally enhanced virtual arms led to increased subjective estimates of reaching affordances, highlighting significant plasticity within this domain. Collectively, these results demonstrate that embodiment is largely flexible with respect to body function, with some constraining effects on the sense of body ownership and the properties of sensorimotor control. Moreover, for children function constrains motor control more than ownership. These findings reveal flexibility regarding body function, and age-dependent changes in how it shapes the boundaries of embodiment.

Understanding how changes in virtual body functionality influence embodiment remains challenging, particularly in children. Prior work has focused mainly on visual body manipulation, while functional alterations are less systematically studied. We present a child-centered virtual reality system and experimental framework that enables controlled manipulation of virtual arm functionality for embodiment research. Using a linearized Go-Go technique, the system supports functionally reduced, normal, and enhanced virtual arms while normalizing reach across users with different physical arm lengths. An engaging animal-feeding task was designed to be intuitive for children aged 5-10, robust to tracking noise, and suitable for quantitative analysis. The interaction flow balances playful engagement with experimental control, enabling reliable use by non-technical researchers. Studies with children and adults demonstrate how the platform supports measurement of reaching behavior, perceived reachability, and embodiment, providing a validated tool for investigating functional body plasticity across developmental and comparative contexts.

Some of the most dramatic examples of neuroplasticity in the human brain follow congenital sensory deprivation, yet the plasticity mechanisms producing this large-scale cortical remapping remain poorly understood. Congenital malformation of the upper-limb provides a unique temporal dissociation of developmental plasticity mechanisms: While sensory deprivation from the absent hand is triggered before birth, compensatory motor behaviours develop gradually throughout childhood. Using paediatric neuroimaging and semi-ecological behavioural analysis in children (5-7 years old) and adults (>25 years old) with unilateral upper-limb congenital limb difference, we studied deprivation- and use-dependent plasticity in the deprived primary somatosensory cortex and beyond. We reveal that global remapping, encompassing the entire sensory homunculus, is established early and maintained in adulthood. Modelling indicates that deprivation-driven homeostatic plasticity can account for this global remapping. Hebbian-based compensatory learning further contributes to the magnitude of inter-individual differences observed at both childhood and adulthood. Our findings emphasise the early establishment and stability of cortical maps, despite extensive daily-life behavioural adaptation.

A recent review of studies using Immersive Virtual Reality (IVR) with children found that IVR contributed to positive learning outcomes [7], however a systematic understanding of how each feature of IVR contributes to its success as a learning experience is still limited. This paper outlines the design of a multisensory embodied IVR experience, with the aim to explore how the inclusion of multisensory and interactive features in IVR contribute to the pedagogical success of the experience.

Adults are known to identify their own body through a combination of multisensory cues and top-down expectations regarding its form, while children may possess a more flexible body representation. Here we use virtual reality to test how children and adults use form cues to feel ownership over a virtual hand with novel, varying degrees of corporeality and how a sense of ownership of the hand and movement fluency with it may be trained. In Experiment 1, children (N = 40, 6-8 years) and adults (N = 45) experienced four virtual hand forms (Hand, hand with a missing Thumb, crab-like Claw, Cross). Participants had to catch slowly moving virtual feathers while the virtual hand form moved in and out of synchrony with their own hand movements. In Experiment 2, we gave each child (N = 10, 6-9 years) and adult (N = 11) repeated experience with the Claw. Across studies we found that sensations of ownership over the virtual hand were facilitated by human-like forms, movement synchrony, and short-term training. For children only, we also found that human-like forms maintained a strong facilitatory influence even when movement was asynchronous. Further, for children only, training improved movement fluency and increased the sense that the virtual form was a 'tool' rather than a hand. We suggest that children's top-down expectations regarding their body do not always interact with their multisensory inputs; their experiences are sharpened with training more than adults; and repeated short virtual experiences do not blur children's perceived distinction between the real and virtual self. SUMMARY: Both children and adults are sensitive to the corporeality of virtual hand forms, showing enhanced ownership for human-like forms. While adults rely on concurrent movement synchrony and form, children treat them independently-maintaining some ownership for human-like forms even when the movement was asynchronous. Short-term training with a non-human virtual form (crab-like claw) increased ownership and improved movement fluency particularly for children. Compared to adults, children's embodiment of moving virtual hands reflects distinct processes-showing greater flexibility, independent cue use, and functional relevance.

This study investigates how interaction methods and spatial distance affect user performance and experience in virtual reality (VR) tasks. We compared the Go-Go technique with ray-based Laser selection across eight target distances (0.67× to 4× arm length) in a memory task to identify distance ranges that minimize motor and cognitive demands and support working memory. Results with 41 participants revealed a trade-off: Go-Go enhanced embodiment, while Laser yielded higher efficiency and lower workload. This trade-off was distance-dependent: Go-Go performed best at 1.2×-1.4×, and Laser at 1.4×-2.2×. Our results offer practical design guidelines for adaptive, distance-aware VR systems that align performance and subjective experience.

Previous research on body appreciation across the lifespan has produced conflicting results that it increases with age, decreases with age, or is generally stable with an increase in women over 50-years-old. Furthermore, most of the research has been conducted in White, Western populations. Cross-cultural research suggests that both Chinese and African women experience similar sociocultural pressures as White Western women, and that appearance ideals are shifting to resemble a more Western ideal. We cross-sectionally and cross-culturally examined body appreciation across the lifespan, recruiting White Western women (UK, USA, Canada, and Australia), Black Nigerian women, and Chinese women. 1186 women aged 18-80 completed measures of body appreciation, internalisation of thin and athletic ideals, and perceived sociocultural pressure. Body appreciation did not vary with age in women from any country. Nigerian women reported the highest body appreciation, and Western women the lowest. Higher thin/athletic ideal internalisation, and higher perceived sociocultural pressure were significantly associated with lower body appreciation in all countries and age-groups. Overall, our findings indicate that although levels of body appreciation differ drastically between ethnicities and cultures, it is generally stable across age, and shows cross-culturally robust relationships between sociocultural internalisation and pressure.

The advancement of motor augmentation and the broader domain of human-machine interaction rely on a seamless integration with users’ physical and cognitive capabilities. These considerations may markedly fluctuate among individuals on the basis of their age, form, and abilities. There is a need to develop a standard for considering these diversity needs and preferences to guide technological development, and large-scale testing can provide us with evidence for such considerations. Public engagement events provide an important opportunity to build a bidirectional discourse with potential users for the codevelopment of inclusive and accessible technologies. We exhibited the Third Thumb, a hand augmentation device, at a public engagement event and tested participants from the general public, who are often not involved in such early technological development of wearable robotic technology. We focused on wearability (fit and control), ability to successfully operate the device, and ability levels across diversity factors relevant for physical technologies (gender, handedness, and age). Our inclusive design was successful in 99.3% of our diverse sample of 596 individuals tested (age range from 3 to 96 years). Ninety-eight percent of participants were further able to successfully manipulate objects using the extra thumb during the first minute of use, with no significant influences of gender, handedness, or affinity for hobbies involving the hands. Performance was generally poorer among younger children (aged ≤11 years). Although older and younger adults performed the task comparably, we identified age costs with the older adults. Our findings offer tangible demonstration of the initial usability of the Third Thumb for a broad demographic.

The present study explored the effects of visuomotor synchrony in virtual reality during the embodiment of a full human avatar in children (aged 5-6 years) and adults. Participants viewed their virtual bodies from a first-person perspective while they moved the body during self-generated and structured movement. Embodiment was measured via questions and psychophysiological responses (skin conductance) to a virtual body-threat and during both movement conditions. Both children and adults had increased feelings of ownership and agency over a virtual body during synchronous visuomotor feedback (compared to asynchronous visuomotor feedback). Children had greater ownership compared to adults during synchronous movement but did not differ from adults on agency. There were no differences in SCRs (frequency or magnitude) between children and adults, between conditions (i.e., baseline or movement conditions) or visuomotor feedback. Collectively, the study highlights the importance of visuomotor synchrony for children's ratings of embodiment for a virtual avatar from at least 5 years old, and suggests adults and children are comparable in terms of psychophysiological arousal when moving (or receiving a threat to) a virtual body. This has important implications for our understanding of the development of embodied cognition and highlights the considerable promise of exploring visuomotor VR experiences in children.

Some of the most dramatic examples of neuroplasticity in the human brain follow congenital sensory deprivation, though we have limited understanding of the plasticity mechanisms driving such large-scale remapping. Hand loss due to congenital limb differences (CLD) offers a unique temporal dissociation of developmental neuroplasticity mechanisms: While sensory deprivation is congenital, compensatory motor behaviours develop progressively across childhood. Using paediatric neuroimaging and semi-ecological behavioural analysis in children (5-7 years old) and adults (>25 years old) with unilateral upper-limb CLD, we studied deprivation- and use-dependent plasticity in the deprived primary somatosensory cortex and beyond. We reveal that global remapping, encompassing the entire sensory homunculus, is established early and maintained in adulthood. We demonstrate that deprivation-driven homeostatic plasticity can drive this global remapping, with Hebbian-based compensatory learning further contributing to inter-individual differences both in childhood and adulthood. Our findings emphasise the early establishment and stability of cortical maps, despite extensive daily-life behavioural adaptation. ### Competing Interest Statement The authors have declared no competing interest.

Abstract Virtual reality (VR) is an emerging, immersive, multisensory technology with the potential to become a widely used tool for children of all ages. Although the majority of official guidelines typically recommend its use for individuals over 10–13 years old, younger children have started to adopt this technology. Given its highly experiential nature and the limited research available, further investigation is needed to assess both the positive and negative impacts of VR on children of all ages. In this chapter, we examine existing knowledge on its use across various settings and discuss its promising aspects (e.g., expanding educational opportunities, pain relief in clinical environments) as well as potential concerns (e.g., ethical issues related to data collection and the possibility of exploitation). As VR is still a nascent technology, we can draw insights from current research and the effects of screen media on children. The power of VR could significantly influence children’s daily lives and families, but it is essential to understand how this novel technology may affect child development differently from adults, considering various ages and developmental stages.

'Embodied cognition' suggests that our bodily experiences broadly shape our cognitive capabilities. We study how embodied experience affects the abstract physical problem-solving styles people use in a virtual task where embodiment does not affect action capabilities. We compare how groups with different embodied experience - 25 children and 35 adults with congenital limb differences versus 45 children and 40 adults born with two hands - perform this task, and find that while there is no difference in overall competence, the groups use different cognitive styles to find solutions. People born with limb differences think more before acting but take fewer attempts to reach solutions. Conversely, development affects the particular actions children use, as well as their persistence with their current strategy. Our findings suggest that while development alters action choices and persistence, differences in embodied experience drive changes in the acquisition of cognitive styles for balancing acting with thinking.

Full text Figures and data Side by side Abstract Editor's evaluation eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Motor variability is a fundamental feature of developing systems allowing motor exploration and learning. In human infants, leg movements involve a small number of basic coordination patterns called locomotor primitives, but whether and when motor variability could emerge from these primitives remains unknown. Here we longitudinally followed 18 infants on 2–3 time points between birth (~4 days old) and walking onset (~14 months old) and recorded the activity of their leg muscles during locomotor or rhythmic movements. Using unsupervised machine learning, we show that the structure of trial-to-trial variability changes during early development. In the neonatal period, infants own a minimal number of motor primitives but generate a maximal motor variability across trials thanks to variable activations of these primitives. A few months later, toddlers generate significantly less variability despite the existence of more primitives due to more regularity within their activation. These results suggest that human neonates initiate motor exploration as soon as birth by variably activating a few basic locomotor primitives that later fraction and become more consistently activated by the motor system. Editor's evaluation This important work on locomotor development takes a longitudinal approach to show that the number of basic locomotor 'primitives' in infant stepping increases from newborn to walking onset, while the variability in their activation decreases. It presents convincing data from the modelling of EMG and kinematic data, which should be of interest to physiologists and psychologists interested in motor skills and development. https://doi.org/10.7554/eLife.87463.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest Human babies start to walk on their own when they are about one year old, but before that, they can move their legs to produce movements called 'stepping', where they take steps when held over a surface; and kicking, where they kick in the air when lying on their backs. These two behaviors are known as 'locomotor precursors' and can be observed from birth. Previous studies suggest that infants produce these movements by activating a small number of motor primitives, different modules in the nervous system – each activating a combination of muscles to produce a movement. However, babies and toddlers exhibit a lot of variability when they move, which is a hallmark of typical development that furthers exploring and learning. So far, it has been unclear whether such differences arise as soon as babies are born and if so, how a small number of motor primitives could result in this variability. Hinnekens et al. hypothesized that the great variety of movements in infants can be generated from a small set of motor primitives, when several cycles of flexing and extending the legs are considered. To test their hypothesis, the researchers first needed to establish how and when infants generate this variability of movement. To do so, they used electromyography to record the leg muscle activity of 18 babies during either movement resulting in a body displacement (locomotor movement) or rhythmic movement. These measurements were taken at either two or three timepoints between birth and the onset of walking. Next, the scientists used a state-of-the-art machine learning approach to model the neural basis underlying these recordings, which showed that newborns generate a lot of movement variability, but they do so by activating a small number of motor primitives, which they can combine in different ways. Hinnekens et al. also show that as babies get older, the number of motor primitives increases while the variety of movements decreases due to a more steady activation of each motor primitive. Cerebral plasticity is maximal during the first year of life, and infants can regularly learn new motor skills, each leading to the ability to perform more movements. Motor variability is believed to play an important role in this learning process and is known to be decreased in atypical development. As such, examining motor variability may be a promising tool to identify neurodevelopmental delays at younger ages. Introduction Variability arises at several levels of the motor system during early locomotor development. Firstly, as soon as birth, infants are able to perform a wide range of behaviors involving flexion and extension cycles of the lower limbs, such as stepping, kicking, swimming, or crawling (Forma et al., 2019; McGraw, 1941; McGraw, 1939; Sylos-Labini et al., 2020; Thelen et al., 1983b; Thelen and Fisher, 1982).Secondly, a given behavior can be realized with numerous coordination modes. For example, neonatal stepping can involve alternated steps, parallel steps, serial steps, or single steps (Siekerman et al., 2015). Similarly, toddlers can follow curved paths when walking or generate a variety of coordination patterns on the fly when cruising over varying distances (Ossmy and Adolph, 2020). Thirdly, a given coordination mode can be realized by different combinations of muscles. For example, infants demonstrate a high variability of muscle activations throughout their first year of life when stepping or kicking, even when producing only alternated leg movements (Sylos-Labini et al., 2020; Teulier et al., 2012). This multilevel variability can arise in numerous environmental contexts and is associated with the development of multiple components, like the growth of musculoskeletal structures, the myelination of neural circuits, or the motivational goal to move, leading infants to learn new skills with their own developmental time scale (Adolph et al., 2018). The third type of variability, corresponding to the ability of the human body to produce a given movement in various ways, is permitted by the existence of numerous nerves and muscles that can control a given joint, which is often referred to as motor control redundancy (Bernstein, 1967). In adulthood, the central nervous system (CNS) seems to simplify the coordination of these numerous degrees of freedom (DOFs) via a small number of encoded primitives, also called motor modules or muscle synergies (Bizzi et al., 1991; d'Avella et al., 2003; Tresch et al., 1999). A primitive is a neural structure that is stored within the CNS at a spinal level and that autonomously produces a coordinated pattern of behavior (i.e. involving several muscles) when recruited from higher centers (Bizzi et al., 2008). In adult organisms, primitives seem to be encoded within the spinal cord and the brainstem (Bizzi et al., 1991; Hart and Giszter, 2010; Mussa-Ivaldi et al., 1994; Roh et al., 2011) and activated by the motor cortex (Drew et al., 2008; Overduin et al., 2015; Overduin et al., 2012) as well as regulated by sensory feedback (Cheung et al., 2005). In humans, the physical location of such primitives remains unknown, but computational modeling from electromyographic (EMG) data also suggests the existence of a modular command (Berger et al., 2013; Ivanenko et al., 2004). Two types of modules are described: a spatial module is a group of muscles that are activated together with relative weights, while a temporal module is a waveform that describes the activation of a spatial module across time (Delis et al., 2014). In adults walking, the EMG activity of numerous muscles of the lower limb can be efficiently reproduced by 4–5 spatial and temporal modules (Dominici et al., 2011; Hinnekens et al., 2020; Lacquaniti et al., 2012; Neptune et al., 2009; Figure 1). Figure 1 Download asset Open asset Theory of modularity and modular organization of adult walking. Left: the theory of modularity postulates that individual muscle activations result from the combination of basic spinal structures called locomotor primitives, which are of two types: spatial (blue) and temporal (orange) modules. According to the space-by-time model that is used here, the brain activates those modules through a supraspinal input (green) that specifies which amplitude of activation has to be allocated to each possible pair of spatial and temporal modules. In humans, non-negative matrix factorization (NNMF) is used to identify the underlying motor primitives and their activation coefficients from electromyographic (EMG) data. Right: illustration of NNMF applied to five right steps of walking in a human adult. EMG patterns can be decomposed into four spatial modules (blue) and four temporal modules (orange). Muscles from both sides can be allocated to a same spatial module to form bilateral modules. Within each spatial module, weightings are plotted for muscles m1 to m10 in the following order: rectus femoris, tibialis anterior, biceps femoris, soleus, and gluteus medius (right muscles in dark blue followed by left muscles in light blue). Activation coefficients (green) represent the level of activation of each possible pair of spatial and temporal modules during five steps. Two features are typical of adults' modular organization: the stability of activation (activations coefficients remain stable during the five steps) and the selectivity of activation (one spatial module is always activated with only one temporal module and vice versa). The development of this fine-tuned modular organization has been investigated from birth on, and several neonatal behaviors have been found to already rely on a low-dimensional modular organization. In particular, stepping and kicking are two neonatal behaviors that can involve alternate flexion and extension cycles of the lower limb, stepping being elicited by a pediatrician when the infant is held in an erected position while kicking is a natural behavior performed in supine position. Those behaviors were found to each involve modules that present similarities with mature modules, making them both distinct locomotor precursors (Dominici et al., 2011; Sylos-Labini et al., 2020). For example, neonatal stepping is based on two modules while walking in toddlers is based on four, suggesting that the motor repertoire of newborns is restricted (Dominici et al., 2011). This is coherent with the fact that innate behaviors are described as stereotyped (Jeng et al., 2002; Spencer and Thelen, 2000; Thelen et al., 1981), with strong coupling among joints (Fetters et al., 2004; Jeng et al., 2002; Thelen et al., 1981) and among agonist/antagonist muscles (Teulier et al., 2012), while typically developing infants will develop the ability to dissociate their degrees of freedom toward a richer repertoire (Fetters et al., 2004). However, the existence of a low-dimensional modular organization in newborns was established on single-step or averaged data (Dominici et al., 2011; Sylos-Labini et al., 2020), while the muscular activity that underlies neonatal movements is known to be highly variable, even for a given coordination mode like, for example, across alternated leg movements (Teulier et al., 2012). This intra-individual variability is believed to be a key feature of typical motor development allowing learning (Dhawale et al., 2017; Hadders-Algra, 2018). According to animal studies, variability could even be centrally regulated for the purpose of motor exploration (Kao et al., 2008; Mandelblat-Cerf et al., 2009). If modularity and variability seem antagonistic at first glance, the question of whether those two features are compatible or incompatible needs to be addressed to better understand motor development. On one hand, a low-dimensional modular system inherently limits motor exploration (Cohn et al., 2018; Valero-Cuevas, 2009). In this vein, the high variability of EMG data during the first year of life opened discussion about the existence itself of motor primitives (Teulier et al., 2012). On the other hand, data from animals suggest that such variability could be generated within a modular system during development. A variable output was indeed observed after applying different stimuli to the neonatal spinal cord of rodents (Kiehn and Kjaerulff, 1996; Klein et al., 2010), which is also believed to store motor primitives (Blumberg et al., 2013; Dominici et al., 2011). In young songbirds, a specialized cortical area has even been found to be responsible for inserting variability into the temporal structure of vocalization to facilitate learning in early development, resulting in a highly variable output that becomes structured when inhibiting the area (Aronov et al., 2011; Kao et al., 2008). As those data suggest that is possible to produce variability by modulating the activation of basic inputs, such organization might shape the development of the motor system in human infants. In human infants, investigations of the motor system are more limited and EMG recordings are the closest signals to the neural output that can be recorded while moving. Yet, if modularity and variability do coexist within the neural command, we should be able to separate the contribution of motor primitives from the variability of EMG signals and observe their cross-evolution during development. To test this prediction, we longitudinally followed 18 human infants and recorded the EMG activity of 10 lower-limb muscles on 2–3 time points between birth and walking onset, during stepping, kicking, or walking (Figure 2). Using a state-of-the-art unsupervised machine learning approach, we were able to model the underlying command by decomposing the EMG data of numerous muscles into step-invariant basic muscle patterns (which represent the motor primitives at each age) and into step-variable activation coefficients (which theoretically represent the variable descending command that modulates the activation of the motor primitives, at least in adults) (d'Avella et al., 2003; Delis et al., 2014; Figure 1). We describe the evolution of both motor variability and motor modularity from birth to independent walking and provide evidence that the human motor system could theoretically initiate its exploration by variably activating a few temporary basic structured patterns. Figure 2 Download asset Open asset Development of basic electromyographic (EMG) and kinematic parameters. (A–E) Example of EMG data for each age and behavior in one infant. A set of five cycles of flexion and extension is presented for each age and behavior. High-pass-filtered data are shown for two muscles (extension phases appear on a gray background). The 10 muscles are then pictured as completely preprocessed (i.e. filtered and normalized in amplitude and time, blue envelope). The black line is the averaged signal across the five pictured cycles. The scale of 1 s is displayed at the bottom of each figure. RF, rectus femoris; TA, tibialis anterior; BF, biceps femoris; So, soleus; GM, gluteus medius. (F–I) Evolution of several features starting from birth to walking onset for stepping or kicking. Individual data are shown in dotted line with the same color code as in Figure 3. Each point was computed as a mean score for each individual (see section 'Number of cycles included in the analysis'). The black bold line represents the averaged values across individuals. The black point (or trait in F) represents the adult landmark. (F) Cycle duration. (G). Kinematic variability (standard deviation of cycle duration divided by averaged cycle duration). (H) Proportion of flexion and extension phases. Figure 2—source data 1 Individual data regarding basic electromyographic (EMG) and kinematic parameters (corresponding to Figure 2F–H). https://cdn.elifesciences.org/articles/87463/elife-87463-fig2-data1-v1.xlsx Download elife-87463-fig2-data1-v1.xlsx Results Eighteen infants were tested longitudinally on 2–3 time points between birth (~4 d) and walking onset (~14 mo). The time points were either around birth, around 3 mo, or around walking onset (individual characteristics and precise time points are reported in Table 2). Around birth and 3 months old, we observed the stepping behavior and/or the kicking behavior, while at walking onset we only recorded independent walking. In each behavior and at each age, infant movements were recorded using surface EMG on 10 bilateral lower-limb muscles and two 2D video cameras. Based on the resulting films, trained coders selected alternated cycles of flexion and extension of the lower limbs, which allowed us to study the same movement regardless of the behavior that could be produced by the infant at each age and focus only on the generation of trial-to-trial variability for this given movement. Data from a given baby were considered analyzable when we had recorded clean surface EMG signals of the 10 lower-limb muscles during at least five alternated cycles of flexion and extension, both at birth and 3 months old and through the same behavior (stepping or kicking). Those cycles were not necessarily consecutive, but to be selected a given cycle had to be at least preceded by an extension and succeeded by a flexion. In total, 586 cycles of flexion and extension were included into the analysis. When more than five cycles were available, we proceeded by analyzing random combinations of five cycles among the available ones and averaging the results afterward, so that the variability would always be calculated on a same number of cycles. For each behavior, we computed the variability of the motor output (index of EMG variability [IEV]) and used non-negative matrix factorization (NNMF) to identify the underlying motor primitives and their activation parameters. We computed a goodness-of-fit criterion to establish whether the cycle-to-cycle variability of five cycles of flexion and extension of the lower limbs could be produced through various combinations of those motor primitives. We compared this goodness-of-fit criterion across ages and computed other indexes in order to characterize (1) how variably were those motor primitives activated and (2) how selective were those primitives (i.e. if they controlled numerous muscles at a time or a few muscles). Table 1 summarizes the role of each of the main variables. Details are available in the 'Materials and methods' section. Table 1 Summary of the role of the main variables of the study. Short nameRoleIndex of EMG variability (IEV)Represents the cycle-to-cycle variability of EMG data across five alternated cycles of flexion and extension of the lower limb.Variance accounted for (VAF)Represents the goodness of fit of the model of modularity for a given number of modules. When the VAF for a fixed number of four spatial and temporal modules is computed, it quantifies how well experimental data can be modeled as originating from four modules.Number of modulesRepresents the smallest number of invariant spatial and temporal elements in which the EMG signals can be factorized (chosen as the smallest number allowing to reach a VAF > 0.75).Index of recruitment variability (IRV)Represents the extent to which spatial and temporal modules are steadily (lower value) or variably activated across cycles to produce the EMG outputs (higher value).Index of recruitment selectivity (IRS)Represents the extent to which spatial modules can be activated with different temporal modules (lower values) or exclusively activated with a given temporal module (higher value).Selectivity of muscular activations index (SMAI)Represents the extent to which spatial modules each control numerous muscles at a time (lower value) or a few muscles at a time (higher value).Selectivity of temporal activations index (STAI)Represents the extent to which temporal modules each control muscles during a long time (lower value) or during a shorter peak of time (higher value). EMG, electromyography. Kinematic parameters and EMG signals reveal maximal motor variability during the neonatal period We started by characterizing basic kinematic and EMG parameters at each age and in each behavior. Wilcoxon tests were performed among kinematic parameters (cycle duration and its variability, proportion of extension/flexion phases) to assess basic differences. The cycle duration was different across behaviors with a decrease from stepping at birth to stepping at 3 mo (p=0.01) and to walking in toddlers (p<0.001) as well as a decrease from kicking at birth to kicking at 3 mo (p=0.003) and to walking in toddlers (p=0.009, Figure 2F). The proportion of phases within a cycle was slightly different across ages (Figure 2H, Supplementary file 1a). The kinematic variability was assessed by the variability of cycle duration (Figure 2G). This variability significantly decreased for stepping and kicking from 3 mo to walking onset in toddlers (p<0.001). Between birth and 3 months old, this variability seems to have begun to decrease (p=0.021 for kicking, and trend of p=0.083 for stepping). The variability of EMG data was assessed by the IEV ( Figure 3D). This index significantly decreased from 3 mo to walking onset in both stepping and kicking (p=0.003 and p=0.001 respectively). However, it significantly decreased between birth and 3 months old for stepping (p=0.005) and not for kicking in which the evolution seems to be different across individuals (p=0.519, Figure 3D). Figure 3 Download asset Open asset Decrease in variability between birth and walking onset associated with modifications of the underlying set of motor primitives. (A) Computational elements contributing to the electromyography (EMG) and their trial-to-trial variability (from top do down: activation coefficients, spatial and temporal modules, and muscle outputs). (B–D). Graphs (B–D) show how changes within the upper levels can explain the resulting motor variability during infant locomotor development. Individual data are represented as dotted lines. Each point was computed as a mean score for each individual (see section 'Number of cycles included in the analysis'). The black bold line represents the averaged values across individuals. The black point indicates the adult landmark, and the gray diamonds indicate individual values from 20 adults (Supplementary file 1c). (B) Variability of module activations, assessed by the index of recruitment variability (IRV). IRV represents the variability of the input that specifies which amplitude of activation has to be allocated to each possible pair of spatial and temporal modules. This index decreases from birth to walking onset considering stepping or kicking as neonatal behavior. (C) Number of spatial and temporal modules, which increases from birth to walking onset considering stepping or kicking as neonatal behavior (D) Index of EMG variability (IEV, same as in Figure 2I). This index decreases from birth to walking onset, considering stepping or kicking as neonatal behavior. (E) Figure legend. Each individual is represented by a color throughout the article. To take into account the variability of walking onset in our representations, colors of each individual are sorted according to their age of walking onset. Figure 3—source data 1 Individual data regarding electromyographic (EMG) output and modeling of the modular organization from birth to walking onset (corresponding to Figure 3B–D). https://cdn.elifesciences.org/articles/87463/elife-87463-fig3-data1-v1.xlsx Download elife-87463-fig3-data1-v1.xlsx Figure 4 with 2 supplements see all Download asset Open asset Modular organization at each age in a representative individual. At each age, electromyographic (EMG) patterns can be decomposed into spatial modules and temporal modules (orange). Within each spatial module, weightings are plotted for muscles m1 to m10 in the following order: rectus femoris, tibialis anterior, biceps femoris, soleus, and gluteus medius (right muscles in dark colors followed by left muscles in light colors). Activation coefficients (at the crossing between each spatial and temporal modules) represent the level of activation of each possible pair of spatial and temporal modules during five steps. (A-B) At birth (red, top left) and 3 mo (purple, top right), EMG activity of stepping can be decomposed into four spatial and four temporal modules. (C) At walking onset (blue, bottom), EMG activity needs to be decomposed into seven spatial and seven temporal modules to get the same quality of modeling than at birth and 3 mo with less modules. Activation coefficients are highly variable at birth and 3 mo and less variable in toddlerhood, with some pairs that are nearly never activated across the five cycles. Note that toddler activations are still more variable than in adults (Figure 1). The number of motor primitives increases from birth to walking onset while variability decreases A modular decomposition was applied to each dataset (for a given behavior, at a given age and for a given subject) thanks to NNMF. We found that several aspects of this decomposition were different depending on the age regarding both dimensionality (i.e. number of primitives) and variability of activations (Figures 3 and 4). To study dimensionality, we considered two approaches based on the variance accounted for (VAF) that is the index that indicates the quality of the modelling. The first approach identified the number of motor primitives (i.e. spatial and temporal modules) that are needed to reach a predetermined VAF threshold. This threshold was established to 0.75 according to Hinnekens et al., 2020. This approach allowed us to determine the number of modules of each individual, which showed that the number of modules was higher at walking onset than at birth and 3 mo (the number of modules was on averaged 4.3 ± 0.7 for stepping and 4.6 ± 0.6 for kicking at birth, 5.2 ± 0.6 for stepping, and 4.5 ± 0.6 for kicking at 3 months old, and 7 ± 0.6 for walking at walking onset, Figure 3C). The second approach set the number of modules to four as in standard adult walking and relied on the analysis of the resulting VAF. This approach assessed dimensionality of the underlying modular system just like the first one but directly tested the hypothesis that four spatial and temporal modules are sufficient to adequately represent the given EMG signals across cycles. By relying on real numbers instead of integers, this second approach is useful because it is more suited to perform statistical analyses. It confirmed that a low-dimensional model fitted better at birth than at walking onset (Supplementary file 1b). We observed a significant VAF decrease between stepping at birth and walking (p=0.002) and between stepping at 3 mo and walking (p<0.001), with the same effects for kicking (p<0.001). Between birth and 3 months old, the VAF value significantly decreased in stepping (p=0.019) but not in kicking (p=0.850) for which the evolution was different across individuals (Figure 3C). To sum up, the modular organization was more complex in toddlers than in infants, with a decrease in the VAF with age, indicating that more and more modules were needed to equivalently reconstruct the EMG patterns, as illustrated by Figures 3B and 4. Motor primitives are recruited with maximal variability and low selectivity during the neonatal period After having analyzed the dimensionality of the signals, we wanted to explain how the IEV (EMG variability) could be higher in infants while their dimensionality was lower. Thus, we focused on the variability of activations of motor primitives. The index of recruitment variability (IRV), which represents the extent to which spatial and temporal modules are variably activated across steps, significantly decreased in toddlers in comparison to infants (Figure 3A), indicating that module recruitment was less and less variable starting from either stepping or kicking from birth to walking (respectively p=0.002 and p<0.001) and from 3 mo to walking (p<0.001). Here again, the value significantly decreased between birth and 3 months old for stepping (p=0.001) but not for kicking (p=0.424). To check that the effects were not due to differences in the number of modules, we performed the same computations on values obtained by systematically extracting four spatial and temporal modules and found the same effects (Supplementary file 1a). This shows that, even with the same number of modules, toddlers, almost like adults recruit modules in a more systematic way across cycles than infants (see Figure 3—source data 1 for individual data). Finally, we repeated the analysis while allowing the modules to vary for each cycle, similarly to what was done in Cheung et al., 2020a, and still found the same effect on the IRV (see Figure 4—figure supplements 1 and 2). The index of recruitment selectivity (IRS, which represents the extent to which a spatial module is activated with a single temporal module and vice versa), tended to increase with age, ranging from 0.395 on average in newborn stepping or kicking to 0.44 in toddlers walking (Supplementary file 1b). This index was always far below the adult value at every age, which is on average 0.62 (Supplementary file 1c), suggesting a low selectivity in the recruitment of spatial and temporal modules during development. Indeed, a spatial module could be activated along with several temporal modules and vice versa depending on the cycle (Figure 4). We also repeated this computation after having extracted four spatial and temporal modules from each dataset and found the same results. Motor primitives evolve between birth and walking onset toward gathering less muscles at a time In order to identify whether motor primitives would have been preserved across ages, we applied the best m

It has long been known that there are topographic maps of the body in primary sensory and motor cortices. While these maps have greater representation of sensitive body parts, the fact that we do not feel these distortions in everyday sensory experience indicates that there are also higher-level corrective processes involved in tactile perception. Beyond perceptions on the body, one’s own body is perceived as distinct from external objects, and this perception gives rise to a feeling of ownership over the body—that my body is mine or belongs to me. This arises from both bottom-up and top-down sensory signals. In the rubber-hand illusion, stroking on a fake hand induces the participant to feel that it is their own. Therefore, the sight of a body, and the synchrony of visual and tactile signals on it, are important cues to body ownership. Other forms of multisensory synchrony, including movement and interoceptive signals, also contribute. Prior expectations of the body’s posture and form constrain the extent to which these sensory signals produce feelings of ownership. Since body ownership arises from a multiplicity of signals, it is subject to significant individual differences. There is also plasticity in body representation. This is demonstrated by neural reorganization in individuals with congenital limb loss and by developmental effects. While very young infants are sensitive to the multisensory signals that drive body ownership (e.g., visuotactile synchrony), it takes substantial experience for the tactile sensations of the body to be flexibly coded in appropriate reference frames; likewise, children up to 10 years old tend to embody an appropriately oriented hand more than adults. Understanding own-body representation has important applications, including for tool use, prosthetic design, and virtual reality.

Knowledge of one's own body size is a crucial facet of body representation, both for acting on the environment and perhaps also for constraining body ownership. However, representations of body size may be somewhat plastic, particularly to allow for physical growth in childhood. Here we report a developmental investigation into the role of hand size in body representation (the sense of body ownership, perception of hand position, and perception of own-hand size). Using the rubber hand illusion paradigm, this study used different fake hand sizes (60%, 80%, 100%, 120% or 140% of typical size) in three age groups (6- to 7-year-olds, 12- to 13-year-olds, and adults; N = 229). We found no evidence that hand size constrains ownership or position: participants embodied hands which were both larger and smaller than their own, and indeed judged their own hands to have changed size following the illusion. Children and adolescents embodied the fake hands more than adults, with a greater tendency to feel their own hand had changed size. Adolescents were particularly sensitive to multisensory information. In sum, we found substantial plasticity in the representation of own-body size, with partial support for the hypothesis that children have looser representations than adults.

There are vast potential applications for children's entertainment and education with modern virtual reality (VR) experiences, yet we know very little about how the movement or form of such a virtual body can influence children's feelings of control (agency) or the sensation that they own the virtual body (ownership). In two experiments, we gave a total of 197 children aged 4-14 years a virtual hand which moved synchronously or asynchronously with their own movements and had them interact with a VR environment. We found that movement synchrony influenced feelings of control and ownership at all ages. In Experiment 1 only, participants additionally felt haptic feedback either congruently, delayed or not at all – this did not influence feelings of control or ownership. In Experiment 2 only, participants used either a virtual hand or non-human virtual block. Participants embodied both forms to some degree, provided visuomotor signals were synchronous (as indicated by ownership, agency, and location ratings). Yet, only the hand in the synchronous movement condition was described as feeling like part of the body, rather than like a tool (e.g., a mouse or controller). Collectively, these findings highlight the overall dominance of visuomotor synchrony for children's own-body representation; that children can embody non-human forms to some degree; and that embodiment is also somewhat constrained by prior expectations of body form.

In adults, illusory embodiment of a virtual avatar can be induced using synchronous visuomotor cues. Further, embodying different-sized avatars influences adults’ perception of their environment’s size. This study (N=92) investigated whether children are also susceptible to such embodiment and size illusions. Adults and 5-year-olds viewed a first-person perspective of different-sized avatars, moving either synchronously or asynchronously with themselves. Participants rated their feelings of embodiment over the avatar, as well as estimating the sizes of their environment and body. Unlike adults, children embodied the avatar regardless of visuomotor synchrony. Both adults and children embodied different-sized avatars, affecting their perception of the size of their environment. These findings have important implications for our understanding of children’s bodily awareness and size perception.

Adults’ body representation is constrained by multisensory information and knowledge of the body such as its possible postures. This study (N = 180) tested for similar constraints in children. Using the rubber hand illusion with adults and 6- to 7-year-olds, we measured proprioceptive drift (an index of hand localisation) and ratings of felt hand ownership. The fake hand was either congruent or incongruent with the participant’s own. Across ages, congruency of posture and visual-tactile congruency yielded greater drift towards the fake hand. Ownership ratings were higher with congruent visual-tactile information, but unaffected by posture. Posture constrains body representation similarly in children and adults, suggesting that children have sensitive, robust mechanisms for maintaining a sense of bodily self.

Children’s and adults’ body representation is constrained by bottom-up multisensory information and by top-down knowledge on possible postures. Using the rubber hand illusion paradigm, this study (N = 229) investigates whether different fake hand sizes (60%, 80%, 100%, 120% or 140% of typical hand size) constrain embodiment in three age groups (6- to 7-year-olds, 12- to 13-year-olds, and adults). Embodiment was measured by questionnaire, proprioceptive drift, and affordance judgements. In line with previous work, we found robust effects of age and synchrony, with higher responses at younger ages and under conditions of visual-tactile synchrony. There were no significant effects of hand size on proprioceptive drift or self-rated hand ownership; nor did participants verbally report that their hand had changed size. Participants of all ages therefore embodied a differently-sized fake hand, without being explicitly aware of the size change. However, manual judgments of own-hand size were significantly influenced by the size of the previously seen fake hand. Therefore, participants did implicitly incorporate a size change into their body schema. In sum, embodiment of differently-sized hands reveals substantial plasticity in body representation, modulated strongly by multisensory information and age. Further, the embodiment of a differently-sized hand specifically affects action-oriented representations of the body.

Background: Deep brain stimulation (DBS) of the pedunculopontine nucleus (PPN) has been investigated for the treatment of levodopa-refractory gait dysfunction in parkinsonian disorders, with equivocal results so far. Objectives: To summarize the clinical outcomes of PPN-DBS-treated patients at our centre and elicit any patterns that may guide future research. Materials and Methods: Pre- and post-operative objective overall motor and gait subsection scores as well as patient-reported outcomes were recorded for 6 PPN-DBS-treated patients, 3 with Parkinson’s disease (PD), and 3 with progressive supranuclear palsy (PSP). Electrodes were implanted unilaterally in the first 3 patients and bilaterally in the latter 3, using an MRI-guided MRI-verified technique. Stimulation was initiated at 20–30 Hz and optimized in an iterative manner. Results: Unilaterally treated patients did not demonstrate significant improvements in gait questionnaires, UPDRS-III or PSPRS scores or their respective gait subsections. This contrasted with at least an initial response in bilaterally treated patients. Diurnal cycling of stimulation in a PD patient with habituation to the initial benefit reproduced substantial improvements in freezing of gait (FOG) 3 years post-operatively. Among the PSP patients, 1 with a parkinsonian subtype had a sustained improvement in FOG while another with Richardson syndrome (PSP-RS) did not benefit. Conclusions: PPN-DBS remains an investigational treatment for levodopa-refractory FOG. This series corroborates some previously reported findings: bilateral stimulation may be more effective than unilateral stimulation; the response in PSP patients may depend on the disease subtype; and diurnal cycling of stimulation to overcome habituation merits further investigation.