TY  - JOUR
A1  - Vasishth, Shravan
A1  - von der Malsburg, Titus Raban
A1  - Engelmann, Felix
T1  - What eye movements can tell us about sentence comprehension
JF  - Wiley interdisciplinary reviews : Cognitive Science
N2  - Eye movement data have proven to be very useful for investigating human sentence processing. Eyetracking research has addressed a wide range of questions, such as recovery mechanisms following garden-pathing, the timing of processes driving comprehension, the role of anticipation and expectation in parsing, the role of semantic, pragmatic, and prosodic information, and so on. However, there are some limitations regarding the inferences that can be made on the basis of eye movements. One relates to the nontrivial interaction between parsing and the eye movement control system which complicates the interpretation of eye movement data. Detailed computational models that integrate parsing with eye movement control theories have the potential to unpack the complexity of eye movement data and can therefore aid in the interpretation of eye movements. Another limitation is the difficulty of capturing spatiotemporal patterns in eye movements using the traditional word-based eyetracking measures. Recent research has demonstrated the relevance of these patterns and has shown how they can be analyzed. In this review, we focus on reading, and present examples demonstrating how eye movement data reveal what events unfold when the parser runs into difficulty, and how the parsing system interacts with eye movement control. WIREs Cogn Sci 2013, 4:125134. doi: 10.1002/wcs.1209 For further resources related to this article, please visit the WIREs website.
Y1  - 2013
U6  - https://doi.org/10.1002/wcs.1209
SN  - 1939-5078
VL  - 4
IS  - 2
SP  - 125
EP  - 134
PB  - Wiley
CY  - San Fransisco
ER  - 
TY  - JOUR
A1  - Engelmann, Felix
A1  - Vasishth, Shravan
A1  - Engbert, Ralf
A1  - Kliegl, Reinhold
T1  - A framework for modeling the interaction of syntactic processing and eye movement control
JF  - Topics in cognitive science
N2  - 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.
KW  - Sentence comprehension
KW  - Eye movements
KW  - Reading
KW  - Parsing difficulty
KW  - Working memory
KW  - Surprisal
KW  - Computational modeling
Y1  - 2013
U6  - https://doi.org/10.1111/tops.12026
SN  - 1756-8757
VL  - 5
IS  - 3
SP  - 452
EP  - 474
PB  - Wiley-Blackwell
CY  - Hoboken
ER  -