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<p>Surprisal, the statistical likelihood of a word or syntactic structure given its preceding context, is an important metric for understanding the predictive/integrative process in reading. Although individual words and syntactic structures vary in surprisal within any text, regardless of its overall difficulty, it remains unclear whether text difficulty modulates how surprisal influences reading. We hypothesized that higher text difficulty increases readers’ reliance on contextual information during reading, leading to stronger surprisal effects. To test this hypothesis, we examined how text difficulty modulates the strength of word and syntactic surprisal effects and whether this modulation generalizes across computational language models with different architectures. We compared reading times for two sets of texts that differed in difficulty but were matched on average surprisal. Results showed larger word and syntactic surprisal effects in more difficult texts, consistently across models. These results suggest that the predictive/integrative processes are shaped by global text properties.</p>

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Text difficulty modulates the surprisal effect in self-paced reading

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Lin Chen,
  • Gaisha Oralova,
  • Xiaoping Fang,
  • Shannon Clark,
  • Daniela Teodorescu,
  • Maxwell Helfrich,
  • Alona Fyshe,
  • Carrie Demmans Epp,
  • Charles Perfetti

摘要

Surprisal, the statistical likelihood of a word or syntactic structure given its preceding context, is an important metric for understanding the predictive/integrative process in reading. Although individual words and syntactic structures vary in surprisal within any text, regardless of its overall difficulty, it remains unclear whether text difficulty modulates how surprisal influences reading. We hypothesized that higher text difficulty increases readers’ reliance on contextual information during reading, leading to stronger surprisal effects. To test this hypothesis, we examined how text difficulty modulates the strength of word and syntactic surprisal effects and whether this modulation generalizes across computational language models with different architectures. We compared reading times for two sets of texts that differed in difficulty but were matched on average surprisal. Results showed larger word and syntactic surprisal effects in more difficult texts, consistently across models. These results suggest that the predictive/integrative processes are shaped by global text properties.