Detailed instructions for replicating some of my paper results
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Introduces a method for converting surprisal into reading time predictions. Uses the method to show that, while surprisal can predict the existence of garden path effects in reading times, surprisal and other information-theoretic measures are unable to predict the magnitude of garden path effects in reading times. Suggests there are likely overt repair mechanisms at work in garden path processing, which are not captured by text statistics alone.
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Explores the extent to which the future surprisal effect in reading times is purely a consequence of reader uncertainty about the future. Also explores whether humans make boundedly rational predictions when they read (Simon, 1982), or whether they compute some composite of all (or a very large number) of future states. We find that future surprisal is not wholly explained by future entropy, and we find that the model that best predicts reading times does not restrict the number of predictions available to the model (contra bounded rationality).
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It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.
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Essentially shows that normed cloze probabilities provide insufficient frequency controls in psycholinguistic studies. Demonstrates how to combine cloze probabilities and corpus statistics to better predict behavioral data. This very simple approach is able to account for a number of previous subcategorization results.