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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 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.
What theories best characterise the parsing processes triggered upon encountering ambiguity, and what effects do these processes have on eye movement patterns in reading? The present eye-tracking study, which investigated processing of attachment ambiguities of an adjunct in Spanish, suggests that readers sometimes underspecify attachment to save memory resources, consistent with the good-enough account of parsing. Our results confirm a surprising prediction of the good-enough account: high-capacity readers commit to an attachment decision more often than low-capacity participants, leading to more errors and a greater need to reanalyse in garden-path sentences. These results emerged only when we separated functionally different types of regressive eye movements using a scanpath analysis; conventional eye-tracking measures alone would have led to different conclusions. The scanpath analysis also showed that rereading was the dominant strategy for recovering from garden-pathing. Our results may also have broader implications for models of reading processes: reanalysis effects in eye movements occurred late, which suggests that the coupling of oculo-motor control and the parser may not be as tight as assumed in current computational models of eye movement control in reading.