Rational models of comprehension : Addressing the performance paradox

A fundamental goal of psycholinguistic research is to understand the architectures and mechanisms that underlie language comprehension. Such an account entails an understanding of the representation and organization of linguistic knowledge in the mind and a theory of how that knowledge is used dynamically to recover the interpretation of the utterances we encounter. While research in theoretical and computational linguistics has demonstrated the tremendous complexities of language understanding, our intuitive experience of language is rather different. For the most part people understand the utterances they encounter effortlessly and accurately. In constructing models of how people comprehend language, we are thus presented with what we dub the performance paradox: How is it that people understand language so effectively given such complexity and ambiguity? In our pursuit and evaluation of new theories, we typically consider how well a particular model is able to account for observed results from the relevant range of controlled psycholinguistic experiments (empirical adequacy), and also the ability of the model to explain why the language comprehension system has the form and function it does (explanatory adequacy). Interestingly, research over the past twenty-five years has led to tremendous variety in proposals for parsing, disambiguation, and reanalysis mechanisms, many of which have been realized as computational models. However, while it is possible to classify models – e.g., according to whether they are modular, interactive, serial, parallel, or probabilistic – consensus at any concrete level has been largely