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We examine the ability of several models of computation and storage to explain reading time data. Specifically, wedemonstrate on both the Dundee and the MIT reading time corpora, that fragment grammars, a model that optimizes the tradeoff between computation and storage, is able to better explain people’s reaction times than two baseline models which exclusively favor either storage or computation. Additionally, we make a contribution by extending an existing incremental parser to handle more general grammars and scale well to larger rule and data sets. 1

Evaluating Models of Computation and Storage in Human Sentence Processing