The broad aim of this research program is to develop a deeper understanding of the representations, mechanisms, and computations that underlie people's ability to comprehend and produce language. Specifically, this research will focus on expectancy generation in sentence comprehension, with particular attention given to structural ambiguity resolution and thematic role assignment. We focus on expectancy generation because there is empirical evidence that it plays a key role in language processing, and because studying a comprehender's expectations as s/he hears or reads a sentence provides a valuable diagnostic for addressing three central questions in sentence processing: what information is available to the comprehender; when do different sources of information become available; and how do classes of information interact. Theoretically, our perspective reflects an emphasis on early information use, nonlinear interactions among knowledge sources, and the importance of both event-based semantic knowledge and statistical patterns of language usage, all of which are characteristics of constraint-based approaches and connectionist models. Our research methodology involves a combination of computer simulations, corpus analyses, and human experiments, including extensive norming procedures and on-line methodologies. Five specific areas will be studied: (1) the role of verb meaning in the generation of expectations regarding upcoming subcategorization frames; (2) the effect of verb-specific semantic distributions of arguments on subcategorization preferences; (3) developing a precise definition of plausibility by comparing six operational definitions in terms of their efficacy for predicting subjects' behavior; (4) the role of event structure and grammatical cues in generating expectations about thematic role assignment; and (5) the consequences of viewing expectations in terms of dynamics in semantic space, rather than as lists of possible lexical items.