TY - JOUR A1 - Paape, Dario A1 - Avetisyan, Serine A1 - Lago, Sol A1 - Vasishth, Shravan T1 - Modeling misretrieval and feature substitution in agreement attraction BT - a computational evaluation JF - Cognitive science N2 - We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model. A novel methodological contribution of our study is the use of comprehension questions with open-ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model. KW - Agreement attraction KW - Eastern Armenian KW - Self-paced reading KW - Computational modeling Y1 - 2021 U6 - https://doi.org/10.1111/cogs.13019 SN - 0364-0213 SN - 1551-6709 VL - 45 IS - 8 PB - Wiley-Blackwell CY - Malden, Mass. 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 - TY - JOUR A1 - Patil, Umesh A1 - Hanne, Sandra A1 - Burchert, Frank A1 - De Bleser, Ria A1 - Vasishth, Shravan T1 - A Computational Evaluation of Sentence Processing Deficits in Aphasia JF - Cognitive science : a multidisciplinary journal of anthropology, artificial intelligence, education, linguistics, neuroscience, philosophy, psychology ; journal of the Cognitive Science Society N2 - Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non-canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account (Grodzinsky, 1995, 2000, 2006) attributes this deficit to an impairment in syntactic representations, whereas others (e.g., Caplan, Waters, Dede, Michaud, & Reddy, 2007; Haarmann, Just, & Carpenter, 1997) propose that the underlying structural representations are unimpaired, but sentence comprehension is affected by processing deficits, such as slow lexical activation, reduction in memory resources, slowed processing and/or intermittent deficiency, among others. We test the claims of two processing accounts, slowed processing and intermittent deficiency, and two versions of the Trace Deletion Hypothesis (TDH), in a computational framework for sentence processing (Lewis & Vasishth, 2005) implemented in ACT-R (Anderson, Byrne, Douglass, Lebiere, & Qin, 2004). The assumption of slowed processing is operationalized as slow procedural memory, so that each processing action is performed slower than normal, and intermittent deficiency as extra noise in the procedural memory, so that the parsing steps are more noisy than normal. We operationalize the TDH as an absence of trace information in the parse tree. To test the predictions of the models implementing these theories, we use the data from a German sentence—picture matching study reported in Hanne, Sekerina, Vasishth, Burchert, and De Bleser (2011). The data consist of offline (sentence-picture matching accuracies and response times) and online (eye fixation proportions) measures. From among the models considered, the model assuming that both slowed processing and intermittent deficiency are present emerges as the best model of sentence processing difficulty in aphasia. The modeling of individual differences suggests that, if we assume that patients have both slowed processing and intermittent deficiency, they have them in differing degrees. KW - Aphasia KW - Non-canonical sentences KW - Sentence-picture matching KW - Eye movements KW - Computational modeling KW - Cognitive architecture KW - Individual differences Y1 - 2016 U6 - https://doi.org/10.1111/cogs.12250 SN - 0364-0213 SN - 1551-6709 VL - 40 SP - 5 EP - 50 PB - Wiley-Blackwell CY - Hoboken 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 -