Analyzing Wrap-Up Effects through an Information-Theoretic Lens

Numerous analyses of reading time (RT) data have been implemented-all in the effort to better understand the cognitive processes driving reading comprehension. However, data measured on words at the end of a sentence-or even at the end of a clause-is often omitted due to the confounding factors introduced by so-called "wrap-up effects," which manifests as a skewed distribution of RTs for these words. Consequently, the community's understanding of the cognitive processes that might be involved in these wrap-up effects is limited. In this work, we attempt to learn more about these processes by examining the relationship between wrap-up effects and information-theoretic quantities, such as word and context surprisals. We find that the distribution of information in prior contexts is often predictive of sentence-and clause-final RTs (while not of sentence-medial RTs). This lends support to several prior hypotheses about the processes involved in wrap-up effects. https://github.com/rycolab/ wrap-up-effects