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  1. 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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  • Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing.Richard Futrell, Edward Gibson & Roger P. Levy - 2020 - Cognitive Science 44 (3):e12814.
    A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation‐based and memory‐based. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. Our model, lossy‐context surprisal, holds that the processing difficulty at a word in context is proportional to (...)
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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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