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  1. A Computational Evaluation of Sentence Processing Deficits in Aphasia.Umesh Patil, Sandra Hanne, Frank Burchert, Ria De Bleser & Shravan Vasishth - 2016 - Cognitive Science 40 (1):5-50.
    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 attributes this deficit to an impairment in syntactic representations, whereas others 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, (...)
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  • Reinforcement Learning for Production‐Based Cognitive Models.Adrian Brasoveanu & Jakub Dotlačil - 2021 - Topics in Cognitive Science 13 (3):467-487.
    We investigate how Reinforcement Learning methods can be used to solve the production selection and production ordering problem in ACT‐R. We focus on four algorithms from the Q learning family, tabular Q and three versions of Deep Q Networks, as well as the ACT‐R utility learning algorithm, which provides a baseline for the Q algorithms. We compare the performance of these five algorithms in a range of lexical decision tasks framed as sequential decision problems.
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  • The Effect of Prominence and Cue Association on Retrieval Processes: A Computational Account.Felix Engelmann, Lena A. Jӓger & Shravan Vasishth - 2019 - Cognitive Science 43 (12):e12800.
    We present a comprehensive empirical evaluation of the ACT‐R–based model of sentence processing developed by Lewis and Vasishth (2005) (LV05). The predictions of the model are compared with the results of a recent meta‐analysis of published reading studies on retrieval interference in reflexive‐/reciprocal‐antecedent and subject–verb dependencies (Jäger, Engelmann, & Vasishth, 2017). The comparison shows that the model has only partial success in explaining the data; and we propose that its prediction space is restricted by oversimplifying assumptions. We then implement a (...)
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  • Exploratory and Confirmatory Analyses in Sentence Processing: A Case Study of Number Interference in German.Bruno Nicenboim, Shravan Vasishth, Felix Engelmann & Katja Suckow - 2018 - Cognitive Science 42 (S4):1075-1100.
    Given the replication crisis in cognitive science, it is important to consider what researchers need to do in order to report results that are reliable. We consider three changes in current practice that have the potential to deliver more realistic and robust claims. First, the planned experiment should be divided into two stages, an exploratory stage and a confirmatory stage. This clear separation allows the researcher to check whether any results found in the exploratory stage are robust. The second change (...)
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  • Parsing as a Cue-Based Retrieval Model.Jakub Dotlačil - 2021 - Cognitive Science 45 (8):e13020.
    This paper develops a novel psycholinguistic parser and tests it against experimental and corpus reading data. The parser builds on the recent research into memory structures, which argues that memory retrieval is content‐addressable and cue‐based. It is shown that the theory of cue‐based memory systems can be combined with transition‐based parsing to produce a parser that, when combined with the cognitive architecture ACT‐R, can model reading and predict online behavioral measures (reading times and regressions). The parser's modeling capacities are tested (...)
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  • Building an ACT‐R Reader for Eye‐Tracking Corpus Data.Jakub Dotlačil - 2018 - Topics in Cognitive Science 10 (1):144-160.
    Cognitive architectures have often been applied to data from individual experiments. In this paper, I develop an ACT-R reader that can model a much larger set of data, eye-tracking corpus data. It is shown that the resulting model has a good fit to the data for the considered low-level processes. Unlike previous related works, the model achieves the fit by estimating free parameters of ACT-R using Bayesian estimation and Markov-Chain Monte Carlo techniques, rather than by relying on the mix of (...)
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  • A Computational Investigation of Sources of Variability in Sentence Comprehension Difficulty in Aphasia.Paul Mätzig, Shravan Vasishth, Felix Engelmann, David Caplan & Frank Burchert - 2018 - Topics in Cognitive Science 10 (1):161-174.
    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 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 (...)
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