Bayesian inference in arm posture perception

In this collection, you can find preprocessed data from 20 human participants linked to the paper 'Bayesian inference in arm posture perception' (preprint on BioRxiv: https://doi.org/10.1101/2024.07.05.602180). For each participant, the dataset reports the target posture and the responses measured in an arm proprioceptive matching task. Target and response postures are reported for each arm joint angle (shoulder, elbow and wrist) in degrees (1D horizontal plane).

Abstract:
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. According to the Bayesian brain hypothesis, the brain forms a belief – here an estimate about the configuration of the arm in space – by combining sensory information with prior beliefs about the default arm posture, following Bayes’ rule. Such computations boost precision in the perception of arm posture but go at the expense of a bias. To test this hypothesis, we combined computational modeling with behavioral experimentation on arm posture perception. 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 this Bayesian model explains errors in a posture-matching task better than a model that assumes a uniform prior. In the task, implemented in VR, 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.