When planning target-directed reaching movements, human subjects combine visual and proprioceptive feedback to form two estimates of the arm's position: one to plan the reach direction, and another to convert that direction into a motor command. These position estimates are based on the same sensory signals but rely on different combinations of visual and proprioceptive input, suggesting that the brain weights sensory inputs differently depending on the computation being performed. Here we show that the relative weighting of vision and proprioception depends both on the sensory modality of the target and on the information content of the visual feedback, and that these factors affect the two stages of planning independently. The observed diversity of weightings demonstrates the flexibility of sensory integration and suggests a unifying principle by which the brain chooses sensory inputs so as to minimize errors arising from the transformation of sensory signals between coordinate frames.

When planning goal-directed reaches, subjects must estimate the position of the arm by integrating visual and proprioceptive signals from the sensory periphery. These integrated position estimates are required at two stages of motor planning: first to determine the desired movement vector, and second to transform the movement vector into a joint-based motor command. We quantified the contributions of each sensory modality to the position estimate formed at each planning stage. Subjects made reaches in a virtual reality environment in which vision and proprioception were dissociated by shifting the location of visual feedback. The relative weighting of vision and proprioception at each stage was then determined using computational models of feedforward motor control. We found that the position estimate used for movement vector planning relies mostly on visual input, whereas the estimate used to compute the joint-based motor command relies more on proprioceptive signals. This suggests that when estimating the position of the arm, the brain selects different combinations of sensory input based on the computation in which the resulting estimate will be used.

Most voluntary actions rely on neural circuits that map sensory cues onto appropriate motor responses. One might expect that for everyday movements, like reaching, this mapping would remain stable over time, at least in the absence of error feedback. Here we describe a simple and novel psychophysical phenomenon in which recent experience shapes the statistical properties of reaching, independent of any movement errors. Specifically, when recent movements are made to targets near a particular location, subsequent movements to that location become less variable, but at the cost of increased bias for reaches to other targets. This process exhibits the variance-bias tradeoff that is a hallmark of Bayesian estimation. We provide evidence that this process reflects a fast, trial-by-trial learning of the prior distribution of targets. We also show that these results may reflect an emergent property of associative learning in neural circuits. We demonstrate that adding Hebbian (associative) learning to a model network for reach planning lead to a continuous modification of network connections that biases network dynamics toward activity patterns associated with recent inputs. This learning process quantitatively captures the key results of our experimental data in human subjects, including the effect that recent experience has on the variance-bias tradeoff. This network also provides a good approximation to a normative Bayesian estimator. These observations illustrate how associative learning can incorporate recent experience into ongoing computations in a statistically principled way.