Modeling the effects of memory on human online sentence processing with particle filters

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What Question Answering can Learn from Trivia Nerds (2019, background): “, 2019), human incremental processing (Levy et al., 2008; Levy, 2011), and opponent modeling (He et al.”
Bridging Levels of Analysis for Probabilistic Models of Cognition (2012, background): “, 2010), as well as “garden path” effects in sentence processing (Levy et al., 2009) and the detection of changes in the distribution from which events are drawn (Brown & Steyvers, 2009).”
Rational approximations to rational models: alternative algorithms for category learning. (2010, methodology): “These results, combined with recent work that has successfully applied particle filters to a range of problems (Brown & Steyvers, 2009; Daw & Courville, 2008; Levy et al., 2009; Yi, Steyvers, & Lee, 2010), lead us to believe that particle filters have the potential to be a powerful tool for producin”
Neural language models as psycholinguistic subjects: Representations of syntactic state (2019, background): “Humans appear to show “digging-in” effects: the longer the span of time between the introduction of a local ambiguity and its resolution, the larger the garden path effect (Tabor and Hutchins, 2004; Levy et al., 2009).”