Analyzing Wrap-Up Effects through an Information-Theoretic Lens

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Abstract

Numerous analyses of reading time (RT) data have been implemented-all in an 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 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.

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APA

Meister, C., Pimentel, T., Clark, T. H., Cotterell, R., & Levy, R. (2022). Analyzing Wrap-Up Effects through an Information-Theoretic Lens. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 2, pp. 20–28). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-short.3

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