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A framework for modeling the interaction of syntactic processing and eye movement control

  • We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, & Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis & Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and McConnell (2009), we integrated both measures with the eye movement model EMMA (Salvucci, 2001) inside the cognitive architecture ACT-R (Anderson et al., 2004). We built a reading model that could initiate short Time Out regressions (Mitchell, Shen, Green, & Hodgson, 2008) that compensate for slow postlexical processing. This simple interaction enabled the model to predict the re-reading of words based on parsing difficulty. The model was evaluated in differentWe explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, & Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis & Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and McConnell (2009), we integrated both measures with the eye movement model EMMA (Salvucci, 2001) inside the cognitive architecture ACT-R (Anderson et al., 2004). We built a reading model that could initiate short Time Out regressions (Mitchell, Shen, Green, & Hodgson, 2008) that compensate for slow postlexical processing. This simple interaction enabled the model to predict the re-reading of words based on parsing difficulty. The model was evaluated in different configurations on the prediction of frequency effects on the Potsdam Sentence Corpus. The extension of EMMA with postlexical processing improved its predictions and reproduced re-reading rates and durations with a reasonable fit to the data. This demonstration, based on simple and independently motivated assumptions, serves as a foundational step toward a precise investigation of the interaction between high-level language processing and eye movement control.show moreshow less

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Author details:Felix EngelmannGND, Shravan VasishthORCiDGND, Ralf EngbertORCiDGND, Reinhold KlieglORCiDGND
DOI:https://doi.org/10.1111/tops.12026
ISSN:1756-8757
Title of parent work (English):Topics in cognitive science
Publisher:Wiley-Blackwell
Place of publishing:Hoboken
Publication type:Article
Language:English
Year of first publication:2013
Publication year:2013
Release date:2017/03/26
Tag:Computational modeling; Eye movements; Parsing difficulty; Reading; Sentence comprehension; Surprisal; Working memory
Volume:5
Issue:3
Number of pages:23
First page:452
Last Page:474
Organizational units:Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie
Peer review:Referiert
Institution name at the time of the publication:Humanwissenschaftliche Fakultät / Institut für Psychologie
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