Biology subjects

Miller, L. E.

Publications and source records attributed to Miller, L. E..

At least 19 recordsLinked to original sources

bioRxiv2026

High-density surface EMG grid enables non-invasive characterization of intrinsic hand muscles activity

Understanding the neuromuscular properties that allow dexterous manipulation of objects remains a major challenge in neurorehabilitation, largely due to the difficulty of characterizing intrinsic hand muscle activity. These muscles are small, densely packed, and anatomically complex, making selective recordings with intramuscular electromyography (EMG) technically demanding and impractical for comprehensive studies. In this work, we present a custom, high-density (HD) surface EMG grid designed to non-invasively capture activity from intrinsic hand muscles from both dorsal and palmar surfaces. We evaluated the quality and spatial selectivity of the recordings by directly comparing them with intramuscular EMG signals obtained from the dorsal and palmar interossei. Surface EMG signals corresponded closely to the intramuscular recordings, with high correlation values for all subjects and tasks. Double differential spatial filtering significantly improved selectivity, although some residual volume conduction remained. The dorsal grid primarily captured dorsal interossei activity, while the palmar grid was more sensitive to lumbrical activation. The palmar interossei recordings were spatially more varied, with the second palmar interosseous predominantly detected on the dorsal grid and the third and fourth on the palmar grid. Together, these results demonstrate that non-invasive HD surface EMG will allow more complete measurement of intrinsic muscle activity, to provide a better understanding of the complex relation between the intrinsic and extrinsic hand muscles during dexterous movements. This basic information will allow refinement of biomechanical hand models and prosthetic devices, and the development of biomimetic brain computer interfaces aimed at restoring natural hand function after neurological injury.

bioengineering↗

bioRxiv2026

Regulated apoptosis is a conserved mechanism pausing female reproduction and establishes the sterile worker caste in the eusocial wasp, Polistes

Female reproduction is an energetically expensive process, so species evolve to balance survival with reproductive output. Many female organisms can temporarily pause their reproduction, including egg development, in response to physiological stress. The cellular mechanisms initiating and maintaining a stress-induced pause in oogenesis have been most extensively studied in Drosophila melanogaster. While the molecular control of paused oogenesis in response to starvation have been well characterized in flies, it remains unknown if these mechanisms are shared by other species with regulated pauses in oogenesis. Eusocial insects are characterized by a reproductive division of labor, with colonies of reproductive queens and sterile female workers. The social paper wasp, Polistes, has a dynamic dominance-based hierarchy for queen status. Worker Polistes are kept sterile by a combination of social and nutritional stressors. Here, we establish Polistes as a model to explore adult female reproductive plasticity. Through immunohistochemistry we have directly compared the Drosophila and Polistes ovarian structure and identified critical regions of the ovary in wasps that undergo regulated cell elimination during reproductive pause in flies. By comparing tissue structure, cell organization and rates of cell death between Polistes queens and workers we identified apoptosis as a key regulator maintaining worker sterility. Critically, this mechanism appears to be partially conserved with that in Drosophila. Finally, we find that changes in the timing and location of cell death in Polistes workers implicate oocyte identity and oocyte growth as additional potential regulators of temporary disruption of oogenesis. Summary StatementEstablishing the social paper wasp, Polistes, as a new model for female adult reproductive plasticity via temporary pausing of oogenesis in the sterile female workers.

developmental biology↗

bioRxiv2026

Spatial localisation of touch on a robotic limb in the absence of direct haptic feedback

Artificial limbs typically lack somatosensory receptors, limiting users access to haptic feedback about movement outcomes. However, indirect tactile signals generated during object interaction are transmitted to the body-limb interface and may serve as a haptic feedback source. This study investigates whether such indirect tactile signals convey distinct information that can support localisation of touch on an artificial body part. Using an additional robotic thumb (The Third Thumb, Dani Clode Design), we measured tactile signals at the interface between the Third Thumb and the hand following stimulation from four vibration motors positioned along the Thumb. We then assessed whether participants could discriminate these signals and integrate them with different Thumb postures to spatially localise touch. Measurements showed that each stimulation site produced distinct tactile signatures at the interface. Behaviourally, participants localised touch at above-chance levels. Representational similarity analyses further revealed that performance was best explained by the spatial positions of stimuli, rather than by limb-based coordinates or indirect signal patterns alone, indicating flexible mapping of indirect touch signals onto the Third Thumb posture. Together, these findings demonstrate that indirect tactile signals transmitted through an artificial limb can be discriminated and flexibly remapped to support spatial localisation of touch.

neuroscience↗

bioRxiv2026

Muscle-driven hand simulations emphasize the critical role of the extensor mechanism

Biomechanical simulations of complex hand motions remain scarce, due to challenges that span computation and data acquisition. Using a computer vision-based motion capture approach, a 23-degree of freedom musculoskeletal model, and direct collocation optimization, we performed muscle-driven simulations to track hand kinematics from 7 participants performing American Sign Language gestures. While proximal joints were tracked accurately, interphalangeal joint tracking was significantly worse, with a consistent flexion bias. Modifications to finger extensor muscle paths that incorporated the dual-inserting nature of the extensors improved accuracy, suggesting better representation of extensor force distribution across distal joints may be necessary for accurate hand simulations.

bioengineering↗

bioRxiv2026

A framework for quantifying the mechanics of dexterous grasp

AO_SCPLOWBSTRACTC_SCPLOWA hallmark of primate behavior is the exceptional ability to dexterously grasp and manipulate objects, yet the investigation of the neural mechanisms that support manual dexterity has been hindered by technical challenges. Optical hand tracking is complicated by frequent occlusions, and contact forces are hard to measure with sufficient precision. Furthermore, while monitoring the kinematics during reaching phase, and the contact forces during object manipulation phase is difficult, joint torques are impossible to measure directly. While challenging, the ability to estimate joint torques in the complex primate hand could provide an invaluable continuous mechanical description spanning both phases. With this in mind, we have developed an experimental apparatus and data processing pipeline for quantifying these variables describing prehension. The apparatus presented objects of various sizes and orientations throughout the workspace, evoking different grasping strategies. Object surfaces were instrumented with thousands of pressure-sensitive elements, enabling high-resolution measurement of distributed contact forces. Simultaneously, eight high-speed cameras were used to reconstruct hand and arm movements with markerless tracking, triangulating 3D landmarks, and mapping them onto a musculoskeletal model, enabling estimation of time-varying joint angles. This posture quantification allowed contact forces to be automatically assigned to specific hand segments, in close agreement with manual human annotations. We used the reconstructed movements and contact forces with the musculoskeletal model of the hand to compute inverse dynamics, yielding joint torques throughout behavior, unifying the hand kinematics and grasp forces into a single physical description. Throughout the processing, we identified individual neurons in the motor cortex of monkeys that were related to grasp force, kinematics, and torques. Together, this framework enabled a comprehensive and precise physical characterization of primate manual behavior, providing a foundation for investigating the neural mechanisms of manual dexterity.

neuroscience↗

bioRxiv2026

Sensorimotor training lightens the perceived weight of body augmentation devices

Wearable haptic and robotic devices must transmit useful forces and tactile information without remaining perceptually intrusive. Here we show that perceived weight provides a behavioral marker of this transition toward transparency. Participants judged the weight of their hand with or without a finger-extending exoskeleton before and after object-manipulation training with either the exoskeleton or a structurally matched control device. Psychometric modelling decomposed perceived effector weight into biological-hand and artificial-device components. Attachment produced baseline attenuation of both hand and device weight, whereas training selectively increased attenuation of the finger-extending exoskeleton. Thus, active use made the augmenting device contribute less to perceived effector weight while leaving perceived hand weight stable. These findings suggest that wearable devices become perceptually lighter when their mechanical consequences are integrated into sensorimotor control. Perceived weight offers a compact metric for evaluating transparency, embodiment, and usability in wearable haptic and robotic interfaces. SummarySensorimotor training makes wearable finger-extending exoskeletons feel lighter.

neuroscience↗

bioRxiv2026

Flexible integration of corollary discharge and sensory feedback signals in somatosensory cortex

Motor control depends on the continuous integration of motor and sensory signals to maintain accurate estimates of body state, yet neural evidence for this integration remains elusive. Here, we investigated the interaction of motor corollary discharge and proprioceptive feedback signals in area 2 of monkey somatosensory cortex during voluntary and externally-perturbed reaching tasks. Though single neurons had mixed responses to corollary discharge and sensory feedback, we disentangled these signals at the population level to discover they occupy approximately orthogonal subspaces. Integrating information across these subspaces enabled accurate body state estimation prior to feedback arrival during voluntary movements. Moreover, the orthogonal population geometry of corollary discharge and sensory feedback enabled cancellation of movement-related signals to improve the decoding of external perturbations. Together, these results identified orthogonality as a population-level coding strategy for flexible integration of motor and sensory signals to support multiple distinct computations.

neuroscience↗

bioRxiv2026

Overlap in neural representations of coordinated wrist and finger movements in human motor cortex

Dexterous hand function underlies many essential human activities, from tool use to expression through gestures. Coordinated digit movements are enabled by the intricate musculature of the hand and forearm, which also imposes mechanical coupling between the digits and wrist, constraining their independent control. It remains unclear whether motor cortex inherits these constraints in its activity or encodes digit and wrist independently. To address this problem, we asked individuals with intracortical microelectrode arrays implanted in motor cortex to attempt flexion and extension of individual digits, either in isolation or in combination with attempted wrist movements. We could accurately decode which digit was moving based on cortical recordings, and channels selective for digit identity were arranged somatotopically across the recording arrays. Nevertheless, the activity during flexion or extension overlapped between digits, and movement direction of a given digit could be reliably inferred by a decoder trained on movements of other digits. This directional signal was largely invariant to the digits initial posture. The population axis describing digit movement direction was aligned with the axes associated with wrist flexion-extension or pronation-supination. This alignment persisted during simultaneous wrist and digit movements, which complicated efforts to control them individually. However, by decoding wrist and digit motion from activity orthogonal to the shared direction axis, a participant was able to achieve continuous control of virtual hand movements with improved speed and reduced unintended movements. Together, the results identify both a code for digit identity and a low-dimensional flexion-extension signal which is shared across the digits and wrist. This arrangement is consistent with muscle-like biomechanical constraints on motor cortical activity, which must be accounted for to improve coordinated BCI control.

neuroscience↗

bioRxiv2026

Consistent EMG encoding during reach and grasp by neurons in motor cortex

Identifying what the activity of neurons in the primary motor cortex (M1) represents is essential for understanding motor computation. Yet the relation of M1 to muscles and movement remains unresolved, in part because the kinematic variables M1 appears to encode differ across the limb: hand velocity during reaching, but joint position during grasping. This discrepancy has been taken to imply fundamentally distinct control strategies for proximal and distal segments. Yet, here we show that a single, muscle-based control principle accounts well for both observations. We recorded neural activity, electromyographic (EMG) signals, and movement kinematics from macaque monkeys performing planar reaching, wrist movement, or free-form grasping tasks. Across behaviors, EMG-based encoding models explained M1 firing rates as accurately as the best-performing kinematic model: velocity for reaching, position for grasping, and either variable for wrist movements. Impulse response analysis revealed that these task-dependent kinematic relationships arise from the differences in mechanical impedance of each limb segment: the inertial and intersegmental coupling which dominate the dynamics of the proximal arm are far less important in the hand. These findings indicate that M1 output has a relatively simple, consistent relation to muscle activation and that the apparent divergence in kinematic encoding is a consequence of limb mechanics rather than distinct cortical strategies.

neuroscience↗

bioRxiv2026

Distinct neural modes carry information about grasp force and phase in the sensorimotor cortex

Humans perform a variety of complex hand movements to manipulate objects, requiring precise control of changing forces. Understanding the role of sensorimotor cortex and the cortical dynamics underlying these actions is crucial for developing interventions that restore dexterous hand function after injury or disease. In this study, two individuals with tetraplegia resulting from cervical spinal cord injury attempted a series of isometric grasps. Neural activity was recorded from the motor and somatosensory cortices using intracortical microelectrode arrays while participants attempted to exert a static force or to ramp force up and down. Despite their inability to execute movement, and with limited afferent input, the spiking activity in motor and somatosensory cortex was modulated with the task. Within the neural response we identified independent neural modes - distinct patterns of population-level neural activity - that were informative about both the timing and magnitude of the force. Moreover, distinct neural modes were observed during static and dynamic grasping conditions, suggesting independent control schemes for maintaining and changing forces. These modes were related to phases of the task, including the onset, offset, holding periods, as well as phases of increasing and decreasing force. These results will inform the design of intracortical brain-computer interface (iBCI) systems that can leverage these naturally occurring patterns of grasp and force control to restore dexterous hand function. Significance StatementRestoring dexterous hand function after injury remains a major challenge, partly due to an incomplete understanding of the cortical dynamics underlying grasping and force control. In this study, we investigated neural activity within the motor and somatosensory cortices of individuals with tetraplegia attempting to perform grasps to different target forces with varying temporal profiles. We identified distinct neural modes modulated during specific phases of grasp that encode force information throughout the task. These findings suggest that brain-computer interfaces could leverage these native neural modes to restore grasping and force modulation.

neuroscience↗

bioRxiv2025

Distinct prior expectations shape tactile and proprioceptive localization

When a mosquito lands on your finger, swatting it away requires your brain to calculate its location in the external space, which depends on the bodys 3D posture. Two competing hypotheses explain how the brain solves this challenge: the integration hypothesis, where tactile signals are transformed into spatial coordinates by integrating touch and posture information; and the cueing hypothesis, where touch merely cues a location on the body whose position is specified via proprioception. Adjudicating between these hypotheses is nearly impossible without modeling the latent factors underlying somatosensory spatial perception. We fill this gap in the present study. We first formalized each hypothesis from a Bayesian perspective: If touch merely triggers proprioceptive localization (cueing hypothesis), tactile and proprioceptive localization should rely on the same Bayesian computations, with identical prior expectations about the mosquitos spatial location; If they involve distinct Bayesian computational processes (integration hypothesis), distinct prior expectations may shape tactile and proprioceptive localization. To test these predictions, we had nineteen participants localize either proprioceptive or tactile targets on their fingertips. We then fit their data with several Bayesian models of each hypothesis. Models allowing different prior distributions between modalities provided the best fit for most participants, with 17 out of 19 participants showing significantly different prior distributions across modalities. These provide strong computational evidence that tactile and proprioceptive localization rely on distinct computational mechanisms, a conclusion that has important implications for how we understand these everyday behaviors and their neural mechanisms. Author SummaryWhen a mosquito lands on your finger and you swat it away, your brain must solve a challenging problem: determining where the mosquito is in space based on where it touched your skin and where your finger is positioned. Scientists have debated how the brain accomplishes this. One hypothesis proposes that the brain transforms touch signals by combining them with information about body posture--an integration process. An alternative hypothesis suggests that touch simply signals which body part was contacted, and the brain then locates that body part with only proprioception--essentially treating touch as only a cue. While these hypotheses make different predictions, distinguishing between them using behavior alone has proven difficult because the underlying computations remain hidden. We addressed this by having participants locate either touches on their fingertips or the fingertips themselves, then used Bayesian computational modeling to reveal the spatial expectations guiding these judgments. Our models showed that tactile and proprioceptive localization rely on distinct spatial expectations, with 17 of 19 participants showing significantly different patterns. These findings provide computational evidence that localizing touch involves transformations beyond simply locating the body, supporting the integration hypothesis and challenging the idea that touch merely cues body location.

neuroscience↗

bioRxiv2025

Dynamics of sensorimotor plasticity during exoskeletal finger augmentation

How does the brain integrate artificial body extensions into its somatosensory representations? Prior work shows that the body and technology are represented simultaneously, but little is known about how these representations evolve dynamically across different sensorimotor interaction phases. To fill this gap, we investigated the dynamics of somatosensory plasticity using a custom-built wearable finger-extension device that elongated users fingers by 10 cm, spatially augmenting their reachable workspace. Across four time points, before, during (pre-and post-use), and after device wear, participants completed a high-density proprioceptive mapping task to measure representations of the biological and artificial fingers. We observed three distinct phases of plasticity. First, wearing the finger-extension device led to a contraction of the perceived length of the biological finger. Second, after active use, the represented lengths of the biological and artificial fingers stretched significantly, an effect absent when participants trained with a non-augmenting control device. Third, a post-removal aftereffect on the biological finger representation was observed. These results demonstrate that wearable finger extensions that provide spatial augmentation are rapidly integrated into body representations, with dynamic proprioceptive adjustments shaped by structural and functional properties of the device. This work advances our understanding of how the sensorimotor system accommodates artificial extensions and highlights the potential for body-augmenting technologies to be intuitively integrated within the sensorimotor system.

neuroscience↗

bioRxiv2025

The spatial coding of touch is defined in intrinsic, limb-specific coordinates: an EEG study

The brain localizes touch in space by integrating tactile and proprioceptive signals, a process known as tactile remapping. While it is often assumed that the remapped touch is encoded in an extrinsic, limb-independent reference frame, an alternative view proposes that touch may instead be represented within an intrinsic, limb-specific coordinate system. To test these hypotheses, we used electroencephalography (EEG) and a novel tactile stimulation paradigm in which participants received touch on their hands positioned at various locations relative to the body. Previous findings suggest that neural activity in primate sensorimotor and parietal regions monotonically encodes limb position. We therefore analyzed amplitude gradients in somatosensory evoked potentials (SEPs) to test predictions from each coding scheme. If touch is coded extrinsically, neural gradients should reflect changes of the external stimulus location, regardless of the limb. If coded intrinsically, gradients should be tied to the position of each limb and mirror each other between hands. Both univariate and multivariate EEG analyses found no evidence for extrinsic coding. Instead, we observed neural signatures of limb-specific, intrinsic spatial codes, the earliest emerging about 160 ms after touch in centro-parietal regions, later shifting to fronto-temporal and parieto-occipital areas. Furthermore, a population-based neural network model of tactile remapping successfully reproduced the observed gradient patterns. These results show that the human brain localizes touch using an intrinsic, limb-specific spatial code, challenging the dominant assumption of extrinsic encoding in tactile remapping.

neuroscience↗

bioRxiv2025

A Generalist Intracortical Motor Decoder

Mapping the relationship between neural activity and motor behavior is a central aim of sensori-motor neuroscience and neurotechnology. While most progress to this end has relied on restricting complexity, the advent of foundation models instead proposes integrating a breadth of data as an alternate avenue for broadly advancing downstream modeling. We quantify this premise for motor decoding from intracortical microelectrode data, pretraining an autoregressive Transformer on 2000 hours of neural population spiking activity paired with diverse motor covariates from over 30 monkeys and humans. The resulting model is broadly useful, benefiting decoding on 8 downstream decoding tasks and generalizing to a variety of neural distribution shifts. However, we also highlight that scaling autoregressive Transformers seems unlikely to resolve limitations stemming from sensor variability and output stereotypy in neural datasets. Code: https://github.com/joel99/ndt3

neuroscience↗

bioRxiv2024

Changes in cortical grasp-related activity before and after object contact

The transition from hand opening to grasping a coffee cup happens smoothly, yet the neural systems underlying this behavior undergo a significant state change at the onset of grasp. At this moment, the motor system begins to control contact forces and the somatosensory system receives a barrage of cutaneous signals that convey information about contact forces and the objects local features (e.g., edges, texture, and curvature). These cutaneous signals supplement the ongoing flow of proprioceptive input that encodes hand posture as well as muscle forces. How object identity is represented in sensorimotor cortex across this transition remains unknown. In the present study, we sought to quantify the object-specific neural signals in individual neurons of the primary motor cortex (M1) and Brodmann areas 3a, 3b/1, and 2 of the somatosensory cortex in macaque monkeys. Before contact, object-specific information was carried mainly by M1 and proprioceptive area 3a, but this information did not generalize between the periods before and after contact. This observation is consistent with the abruptly changing force signals at contact affecting the assumed postural representation of the object, rather than each modality maintaining an invariant identity. After contact, despite a general decrease in firing rates, information about object identity increased and was encoded with high efficiency across sensorimotor cortex. Cutaneous areas 3b and 1, largely uninformative before contact, became highly informative once objects were grasped. Area 2, which receives both cutaneous and musculotendinous inputs, conveyed little object-specific information before contact, when it too became strongly informative, consistent with its integrative role. Thus, M1 and 3a serve as the main carriers of object information before contact, while cutaneous and integrative somatosensory circuits dominate after contact. This shift highlights the profound change in the coding of object identify in the sensorimotor cortex during contact.

neuroscience↗

bioRxiv2024

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices. https://snel-repo.github.io/falcon/

neuroscience↗

bioRxiv2024

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface

ObjectiveCreating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue. ApproachWe devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to. Main resultsWe found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network (RNN) decoder with 10-12 clusters. SignificanceThis study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.

neuroscience↗

bioRxiv2024

Bayesian inference in arm posture perception

To configure our limbs in space the brain must compute their position based on sensory information provided by mechanoreceptors in the skin, muscles, and joints. Because this information is corrupted by noise, the brain is thought to process it probabilistically, and integrate it with prior belief about arm posture, following Bayes rule. Here, we combined computational modeling with behavioral experimentation to test this hypothesis. The model conceives the perception of arm posture as the combination of a probabilistic kinematic chain composed by the shoulder, elbow, and wrist angles, compromised with additive Gaussian noise, with a Gaussian prior about these joint angles. We tested whether the model explains errors in a VR-based posture-matching task better than a model that assumes a uniform prior. Human participants (N=20) were required to align their unseen right arm to a target posture, presented as a visual configuration of the arm in the horizontal plane. Results show idiosyncratic biases in how participants matched their unseen arm to the target posture. We used maximum likelihood estimation to fit the Bayesian model to these observations and retrieve key parameters including the prior means and its variance-covariance structure. The Bayesian model including a Gaussian prior explained the response biases and variance much better than a model with a uniform prior. The prior varied across participants, consistent with the idiosyncrasies in arm posture perception, and in alignment with previous behavioral research. Our work clarifies the biases in arm posture perception within a new perspective on the nature of proprioceptive computations. New & NoteworthyWe modeled the perception of arm posture as a Bayesian computation. A VR posture-matching task was used to empirically test this Bayesian model. The Bayesian model including a non-uniform postural prior well explained individual participants biases in arm posture perception.

neuroscience↗