 In contrast to this poor showing on cognitive tasks, people are generally thought to be optimal or near-optimal in performing low-level motor control and perception tasks. Planning goal-directed movement, like pressing an elevator button with your finger or placing your foot on a slippery river stone, requires your motor control system to pick one among a dizzying number of possible movement strategies to achieve your goal while minimizing biomechanical costs (Trommershäuser, Maloney, &amp; Landy 2003). The loss function that our motor control system appears to use increases approximately quadratically with error for small errors but significantly less for large errors, suggesting that our motor control system is also robust to outliers (Körding &amp; Wolpert 2004). What is more, advances in machine learning have been guided by treating human performance errors for a range of perception tasks as proxies for Bayes error, yielding an observable, near-perfect normative standard. Unlike cognitive decisions, there is very little controversy concerning the overall optimality of our motor-perceptual decisions. This difference between high-level and low-level decisions is called the perception-cognition gap.