During language comprehension, words that are less expected tend to take more effort. This phenomenon has been described by the hypothesis that cognitive cost scales in surprisal (negative log probability; Hale, 2001; Levy, 2008), with a core justification being that surprisal quantifies the amount by which a rational comprehender's beliefs about meaning change upon encountering a word. However, this focus on next-word prediction may be too narrow. In this work we advocate measuring processing cost directly with the size of the change in beliefs about meaning, a reframing which implies a novel class of potential situations where surprisal may systematically overestimate cost. We identify typographical errors as a test case, and implement estimators of surprisal and belief-update in a noisy-channel model of comprehension as inference about intended strings. In a self-paced reading time study, we present evidence that human reading time behaves as predicted by belief-update size, rather than surprisal.