# Analyzing Wrap-Up Effects through an Information-Theoretic Lens

arXiv: https://arxiv.org/abs/2203.17213 · HTML: https://mlsanity.com/paper/2203.17213/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Revisiting the Uniform Information Density Hypothesis](https://mlsanity.com/paper/2109.11635/) (2021)
- [The Natural Stories Corpus](https://mlsanity.com/paper/1708.05763/) (2017)

## Built on by (most cited first, 10 of 10)

- [Large Language Models Are Human-Like Internally](https://mlsanity.com/paper/2502.01615/) (2025)
- [MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score](https://mlsanity.com/paper/2210.05875/) (2022)
- [On the Effect of Anticipation on Reading Times](https://mlsanity.com/paper/2211.14301/) (2022)
- [On the Proper Treatment of Tokenization in Psycholinguistics](https://mlsanity.com/paper/2410.02691/) (2024)
- [Psychometric Predictive Power of Large Language Models](https://mlsanity.com/paper/2311.07484/) (2023)
- [Measuring Information in Text Explanations](https://mlsanity.com/paper/2310.04557/) (2023)
- [Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths](https://mlsanity.com/paper/2603.23624/) (2026)
- [Probing for Reading Times](https://mlsanity.com/paper/2604.18712/) (2026)
- [Surprisal Theory is Tautological (without Rational Grounding)](https://mlsanity.com/paper/2607.21574/) (2026)
- [Distinct dynamics of conceptual and referential disruptions in human reading and large language model processing](https://mlsanity.com/paper/2608.25999/) (2026)

## Abstract (arXiv)

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.