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An Expectation-Based Model of Discourse Processing

Research into sentence-level processing has consistently shown that readers do not wait until they have all of the information needed to construct the perceived meaning of the sentence. Rather they use the information at their disposal to generate predictions about likely meanings. Relevant cues can include probabilistic information about the types of structures licensed by the grammar and their relative frequencies, semantic cues, and fine-grained statistical information. This study will test the proposal that an expectation-based processing model can be extended to explain how readers construct meanings of larger works, by using self-paced reading time and visual world paradigms to test predictions against those of a priming based model, which is blind to discourse structure.

Brown faculty collaborators:

None

Other project collaborators:

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Research at Brown: Laura Kertz: An Expectation-Based Model of Discourse Processing
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