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  1. Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study.Himanshu Yadav, Ashwini Vaidya, Vishakha Shukla & Samar Husain - 2020 - Cognitive Science 44 (4):e12822.
    Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer‐i Cancho, 2008, 2015; Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross‐linguistically (Ferrer‐i Cancho, 2014; Futrell, Mahowald, & Gibson, 2015; Liu, Xu, & Liang, 2017). This implies that on average languages avoid inefficient or complex structures for simpler structures. But (...)
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  • Locality and expectation effects in Hindi preverbal constituent ordering.Sidharth Ranjan, Rajakrishnan Rajkumar & Sumeet Agarwal - 2022 - Cognition 223 (C):104959.
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  • Predicting syntactic choice in Mandarin Chinese: a corpus-based analysis of ba sentences and SVO sentences.Haitao Liu & Yu Fang - 2021 - Cognitive Linguistics 32 (2):219-250.
    This paper investigates the effects of 10 factors on the choice between alternative ba sentences and SVO sentences in Mandarin Chinese. These factors are givenness, definiteness, animacy and pronominality of NP2s, NP2 length, VP length, verb sense, syntactic parallelism, dependency distance, and surprisal. Using corpus data and mixed-effects logistic regression modeling, we find that on the one hand, givenness, syntactic parallelism, and the log-transformed ratio of NP2 length and VP length are significant predictors of the choice between ba sentences and (...)
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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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