The function of regressions in reading: Backward eye movements allow rereading.
ABSTRACT Standard text reading involves frequent eye movements that go against normal reading order. The function of these "regressions" is still largely unknown. The most obvious explanation is that regressions allow for the rereading of previously fixated words. Alternatively, physically returning the eyes to a word's location could cue the reader's memory for that word, effectively aiding the comprehension process via location priming (the "deictic pointer hypothesis"). In Experiment 1, regression frequency was reduced when readers knew that information was no longer available for rereading. In Experiment 2, readers listened to auditorily presented text while moving their eyes across visual placeholders on the screen. Here, rereading was impossible, but deictic pointers remained available, yet the readers did not make targeted regressions in this experiment. In Experiment 3, target words in normal sentences were changed after reading. Where the eyes later regressed to these words, participants generally remained unaware of the change, and their answers to comprehension questions indicated that the new meaning of the changed word was what determined their sentence representations. These results suggest that readers use regressions to reread words and not to cue their memory for previously read words.
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ABSTRACT: 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.Topics in Cognitive Science 05/2013; · 2.88 Impact Factor