Insensitivity of the Human Sentence-Processing System to Hierarchical Structure

By Stefan L. Frank, Rens Bod. 2011

226 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Developmental and Educational Psychology

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Citations in the corpus

Rapid Expectation Adaptation during Syntactic Comprehension (2013, unknown): “utterance [29–34].”
A Systematic Assessment of Syntactic Generalization in Neural Language Models (2020, background): “…of words w 1 , . . . , w N in a held-out test corpus C : Models with improved perplexity have also been shown to better match various human behavioral measures, such as gaze duration during reading (Frank and Bod, 2011; Fossum and Levy, 2012; Goodkind and Bicknell, 2018; Wilcox et al., 2020).”
The effect of word predictability on reading time is logarithmic (2013, unknown): “Previous work (Demberg & Keller, 2008; Frank & Bod, 2011; Kennedy, Pynte, Murray, & Paul, in press) has reported predictability effects in this corpus, but did not examine curve shape.”
Neural language models as psycholinguistic subjects: Representations of syntactic state (2019, background): “…linking RNN performance to human language processing (Elman, 1990; Christiansen and Chater, 1999; MacDonald and Christiansen, 2002) and grammaticality judgments (Lau et al., 2017), and RNN surprisals are a strong predictor of human reading times (Frank and Bod, 2011; Good-kind and Bicknell, 2018).”
What do we mean by prediction in language comprehension? (2015, background): “…& Rayner, 1981; Rayner, Binder, Ashby, & Pollatsek, 2001; Rayner & Well, 1996; see also Boston, Hale, Kliegl, Patil, & Vasishth, 2008; Demberg & Keller, 2008; Demberg, Keller, & Koller, 2013; Frank & Bod, 2011; McDonald & Shillcock, 2003; Smith & Levy, 2013; see Staub, 2015 for a recent review).”
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency (2018, background): “While we are not interested in directly modeling human processing difﬁculty here, we note that there is a long tradition linking RNN performance to human language processing (El-man, 1990; Christiansen and Chater, 1999; Mac-Donald and Christiansen, 2002; Frank and Bod, 2011).”
The neural architecture of language: Integrative modeling converges on predictive processing (2020, background): “…make informed guesses about how the linguistic signal may unfold, and much behavioral and neural evidence now suggests that readers and listeners indeed engage in such predictive behavior (Altmann & Kamide, 1999; Frank & Bod, 2011; Kuperberg & Jaeger, 2016; Shain et al., 2020; Smith & Levy, 2013).”