mlsanityData to 7 Oct 2026

On the Effect of Anticipation on Reading Times

arXiv 2211.14301 ↗First posted 25 Nov 2022cs.CLcited by 18

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Abstract (arXiv)

Over the past two decades, numerous studies have demonstrated how less predictable (i.e., higher surprisal) words take more time to read. In general, these studies have implicitly assumed the reading process is purely responsive: Readers observe a new word and allocate time to process it as required. We argue that prior results are also compatible with a reading process that is at least partially anticipatory: Readers could make predictions about a future word and allocate time to process it based on their expectation. In this work, we operationalize this anticipation as a word's contextual entropy. We assess the effect of anticipation on reading by comparing how well surprisal and contextual entropy predict reading times on four naturalistic reading datasets: two self-paced and two eye-tracking. Experimentally, across datasets and analyses, we find substantial evidence for effects of contextual entropy over surprisal on a word's reading time (RT): in fact, entropy is sometimes better than surprisal in predicting a word's RT. Spillover effects, however, are generally not captured by entropy, but only by surprisal. Further, we hypothesize four cognitive mechanisms through which contextual entropy could impact RTs -- three of which we are able to design experiments to analyze. Overall, our results support a view of reading that is not just responsive, but also anticipatory.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 9 of its 62 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

9 of 62 references.

References · 9

  1. 2020On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behaviorcited by 115
  2. 2021Revisiting the Uniform Information Density Hypothesiscited by 63
  3. 2021Lower Perplexity is Not Always Human-Likecited by 52
  4. 2017The Natural Stories Corpuscited by 40
  5. 2022Context Limitations Make Neural Language Models More Human-Likecited by 32
  6. 2018A Neural Model of Adaptation in Readingcited by 18
  7. 2022Analyzing Wrap-Up Effects through an Information-Theoretic Lenscited by 10
  8. 2018Can Entropy Explain Successor Surprisal Effects in Reading?cited by 5
  9. 2022A Deep Learning Approach to Analyzing Continuous-Time Systemscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 5; 2026: 4.

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  1. 2023Testing the Predictions of Surprisal Theory in 11 Languagescited by 75
  2. 2024How to Compute the Probability of a Wordcited by 36
  3. 2025Large Language Models Are Human-Like Internallycited by 28
  4. 2024Generalized Measures of Anticipation and Responsivity in Online Language Processingcited by 23
  5. 2023Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"?cited by 7
  6. 2024EMTeC: A Corpus of Eye Movements on Machine-Generated Textscited by 5
  7. 2023Psychometric Predictive Power of Large Language Modelscited by 4
  8. 2025How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?cited by 3
  9. 2024Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differencescited by 1
  10. 2024Reverse-Engineering the Readercited by 1
  11. 2025Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Methodscited by 1
  12. 2025From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errorscited by 1
6 more
  1. 2026An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisalcited by 1
  2. 2024That's Optional: A Contemporary Exploration of "that" Omission in English Subordinate Clausescited by 0
  3. 2025Modeling Bottom-up Information Quality during Language Processingcited by 0
  4. 2026Glitter: Visualizing Lexical Surprisal for Readability in Administrative Textscited by 0
  5. 2026Modeling semantic association in self-paced reading with language model embeddingscited by 0
  6. 2026Do Cantonese-Adapted Language Models Better Predict Cantonese Reading? A Cross-Model Eye-Tracking Evaluationcited by 0