Computational psycholinguistics seeks to develop computational theories and implemented models of the cognitive systems that map an unfolding linguistic signal onto a mental representation of its meaning. Focusing primarily on language comprehension, this chapter begins with early theories of sentence processing, before reviewing several prominent implemented computational models. These accounts are largely informed by reading-time studies that seek to establish the strategies and constraints that determine how people resolve ambiguity. This review concludes with a more in-depth discussion of rational probabilistic accounts, for which there has been considerable consensus in recent years, and surprisal theory, which formally links these models with measures of human comprehension effort, such as reading times and brain potentials. Finally, an implemented neurobehavioral model of language comprehension is presented in greater detail, illustrating the benefit of linking computational models with several behavioral and neurophysiological indices of human comprehension, as well as the importance of looking beyond syntactic processing alone to the modeling of semantic comprehension and the role of world knowledge.