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. This perceptual bias has been taken as evidence that the brain’s internal representation of the body’s geometry is distorted, leading to an apparent paradox with the skillfulness of our actions. 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 representations 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. 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.