We present interpretation-based processing-a theory of sentence processing that builds a syntactic and a semantic representation for a sentence and assigns an interpretation to the sentence as soon as possible. That interpretation can further participate in comprehension and in lexical processing and is vital for relating the sentence to the prior discourse. Our theory offers a unified account of the processing of literal sentences, metaphoric sentences, and sentences containing semantic illusions. It also explains how text can prime lexical access. We show that word literality is a matter of degree and that the speed and quality of comprehension depend both on how similar words are to their antecedents in the preceding text and how salient the sentence is with respect to the preceding text. Interpretation-based processing also reconciles superficially contradictory findings about the difference in processing times for metaphors and literals Ambiguity is one feature of human language that often frustrates the attempts to automatize its understanding by computers: not only can words have multiple meanings, but sometimes the meaning of a word is not taken at face value. Everyday language is often nonliteral; figurative devices such as irony, indirect request, metaphor, metonymy, or hyperbole are common and are understood easily. Metaphor is a particularly pervasive device: it is a rich source of new words (recent examples include web and couch potato) and, moreover, according to researchers such