Biology subjects

Peviani, V. C.

Publications and source records attributed to Peviani, V. C..

3 recordsLinked to original sources

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↗

bioRxiv2024

Tool use fine-tunes arm and tool maps

There is evidence that the sensorimotor system builds fine-grained spatial maps of the limbs based on somatosensory signals. Can a hand-held tool be mapped in space with a comparable spatial resolution? Do spatial maps change following tool use? In order to address these questions, we used a spatial mapping task on healthy participants to measure the accuracy and precision of spatial estimates pertaining to several locations on their arm and on a hand-held tool. To study spatial accuracy, we first fitted linear regressions with real location as predictor and estimated location as dependent variables. Intercepts and slopes, representing constant offset and estimation error, were compared between arm and tool, and before to after tool use. We further investigated changes induced by tool use in terms of variable error associated with spatial estimates, representing their precision. We found that the spatial maps for the arm and tool were comparably accurate, suggesting that holding the tool provides enough information to the sensorimotor system to map it in space. Further, using the tool fine-tuned the users spatial maps, increasing the precision of the tool map to a greater extent than their arm map. Furthermore, this increased precision is focal to specific tool locations, i.e., the tool tip, which may work as a spatial anchor following tool use. Our results demonstrate that tool users possess dynamic maps of tool space that are comparable to body space.

neuroscience↗

bioRxiv2023

Biases in hand perception are driven by somatosensory computations, not a distorted hand model

To sense and interact with objects in the environment, we effortlessly configure our fingertips at desired locations. It is therefore reasonable to assume the underlying control mechanisms rely on accurate knowledge about the structure and spatial dimensions of our hand and fingers. This intuition, however, is challenged by years of research showing drastic biases in the perception of finger geometry.1-5 This perceptual bias has been taken as evidence that the brains internal representation of the bodys geometry is distorted,6 leading to an apparent paradox with the skillfulness of our actions.7 Here, we propose an alternative explanation of the biases in hand perception--They are the result of the Bayesian integration of noisy, but unbiased somatosensory signals about finger geometry and posture. To address this hypothesis, we combined Bayesian reverse-engineering with behavioral experimentation on joint and fingertip localization of the index finger. We modelled the Bayesian integration either in sensory or in space-based coordinates, showing that the latter model variant led to biases in finger perception despite accurate representation of finger length. Behavioral measures of joint and fingertip localization responses showed similar biases, which were well-fitted by the space-based but not the sensory-based model variant. The space-based model variant also outperformed a distorted-hand model with built-in geometric biases. In total, our results suggest that perceptual distortions of finger geometry do not reflect a distorted hand model but originate from near-optimal Bayesian inference on somatosensory signals.

neuroscience↗