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  - 
TY  - JOUR
A1  - Mätzig, Paul
A1  - Vasishth, Shravan
A1  - Engelmann, Felix
A1  - Caplan, David
A1  - Burchert, Frank
T1  - A computational investigation of sources of variability in sentence comprehension difficulty in aphasia
JF  - Topics in cognitive science
N2  - We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction. The ACT-R based Lewis and Vasishth (2005) model is used to implement these three proposals. Slowed processing is implemented as slowed execution time of parse steps; intermittent deficiency as increased random noise in activation of elements in memory; and resource reduction as reduced spreading activation. As data, we considered subject vs. object relative sentences, presented in a self-paced listening modality to 56 individuals with aphasia (IWA) and 46 matched controls. The participants heard the sentences and carried out a picture verification task to decide on an interpretation of the sentence. These response accuracies are used to identify the best parameters (for each participant) that correspond to the three hypotheses mentioned above. We show that controls have more tightly clustered (less variable) parameter values than IWA; specifically, compared to controls, among IWA there are more individuals with slow parsing times, high noise, and low spreading activation. We find that (a) individual IWA show differential amounts of deficit along the three dimensions of slowed processing, intermittent deficiency, and resource reduction, (b) overall, there is evidence for all three sources of deficit playing a role, and (c) IWA have a more variable range of parameter values than controls. An important implication is that it may be meaningless to talk about sources of deficit with respect to an abstract verage IWA; the focus should be on the individual's differential degrees of deficit along different dimensions, and on understanding the causes of variability in deficit between participants.
KW  - Sentence comprehension
KW  - Aphasia
KW  - Computational modeling
KW  - Cue-based retrieval
Y1  - 2018
U6  - https://doi.org/10.1111/tops.12323
SN  - 1756-8757
SN  - 1756-8765
VL  - 10
IS  - 1
SP  - 161
EP  - 174
PB  - Wiley
CY  - Hoboken
ER  -