RT Journal Article SR Electronic T1 Late Bayesian inference in sensorimotor behavior JF bioRxiv FD Cold Spring Harbor Laboratory SP 130062 DO 10.1101/130062 A1 Evan Remington A1 Mehrdad Jazayeri YR 2017 UL http://biorxiv.org/content/early/2017/04/24/130062.abstract AB Sensorimotor skills rely on performing noisy sensorimotor computations on noisy sensory measurements. Bayesian models suggest that humans compensate for measurement noise and reduce behavioral variability by biasing perception toward prior expectations. Whether the same holds for noise in sensorimotor computations is not known. Testing human subjects in tasks with different levels of sensorimotor complexity, we found a similar bias-variance tradeoff associated with increased sensorimotor noise. This result was accurately captured by a model which implements Bayesian inference after – not before – sensorimotor transformation. These results indicate that humans perform “late inference” downstream of sensorimotor computations rather than, or in addition to, “early inference” in the perceptual domain. The brain thus possesses internal models of noise in both sensory measurements and sensorimotor computations.