The Effect of Surprisal on Reading Times in Information Seeking and Repeated Reading

Keren Gruteke Klein, Yoav Meiri, Omer Shubi, Yevgeni Berzak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The effect of surprisal on processing difficulty has been a central topic of investigation in psycholinguistics. Here, we use eyetracking data to examine three language processing regimes that are common in daily life but have not been addressed with respect to this question: information seeking, repeated processing, and the combination of the two. Using standard regime-agnostic surprisal estimates we find that the prediction of surprisal theory regarding the presence of a linear effect of surprisal on processing times, extends to these regimes. However, when using surprisal estimates from regime-specific contexts that match the contexts and tasks given to humans, we find that in information seeking, such estimates do not improve the predictive power of processing times compared to standard surprisals. Further, regime-specific contexts yield near zero surprisal estimates with no predictive power for processing times in repeated reading. These findings point to misalignments of task and memory representations between humans and current language models, and question the extent to which such models can be used for estimating cognitively relevant quantities. We further discuss theoretical challenges posed by these results.

Original languageEnglish
Title of host publicationCoNLL 2024 - 28th Conference on Computational Natural Language Learning, Proceedings of the Conference
EditorsLibby Barak, Malihe Alikhani
Pages219-230
Number of pages12
ISBN (Electronic)9798891761780
StatePublished - 2024
Event28th Conference on Computational Natural Language Learning, CoNLL 2024 - Miami, United States
Duration: 15 Nov 202416 Nov 2024

Publication series

NameCoNLL 2024 - 28th Conference on Computational Natural Language Learning, Proceedings of the Conference

Conference

Conference28th Conference on Computational Natural Language Learning, CoNLL 2024
Country/TerritoryUnited States
CityMiami
Period15/11/2416/11/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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