Judgements about double-embedded relative clauses differ between languages

Frank, Stefan L.; Ernst, Patty

doi:10.1007/s00426-018-1014-7

Cited by 19 publications

(34 citation statements)

References 29 publications

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“…In addition, various corpus analyses (Futrell et al, 2015; Liu, 2008; Rajakrishnan et al, 2016; Temperley, 2007) also provide evidence for the dependency minimization hypothesis, which is based on the assumption of limited working memory capacity. Our results lend support to a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining processing complexity in various languages (Boston et al, 2008; Demberg & Keller, 2008; Frank & Ernst, 2019; Husain et al, 2014, 2015; Levy, 2013; Levy et al, 2013; Levy & Keller, 2013; Staub, 2010; Vasishth & Drenhaus, 2011). Similar importance to linguistic exposure and its effect on processing is given by the connectionist models of processing (Christiansen & Chater, 1999; Christiansen & MacDonald, 2009; Engelmann & Vasishth, 2009; Frank et al, 2016; MacDonald & Christiansen, 2002).…”

Section: Discussionsupporting

confidence: 78%

“…In particular, it has been claimed that head‐final languages (e.g., German, Hindi, Japanese) allow for longer head‐dependent distance. Previous work on predictive processing (Husain et al, 2014; Konieczny, 2000; Levy & Keller, 2013; Miyamoto & Nakamura, 2003; Nakatani & Gibson, 2010; Vasishth & Lewis, 2006) as well as the forgetting effect (Frank & Ernst, 2019; Frank et al, 2016; Husain & Bhatia, 2018; Vasishth et al, 2010) shows that head‐final languages allow for longer distance in dependencies involving verbal heads (but see Safavi, Husain, & Vasishth, 2016; Vasishth & Drenhaus, 2011). For example, with regard to the forgetting effect, it has been shown that contrary to the results in an SVO language like English, in SOV languages like German, Dutch, and Hindi, increased HD (in the form of embedding) does not lead to forgetting of the upcoming verbs.…”

Section: Discussionmentioning

confidence: 99%

“…In this case, we look at the number of intervening heads between a given head and dependent rather than simple linear distance. This takes into account experimental results that suggest adaptability as the explanation for maintaining a greater number of heads in memory (Frank & Ernst, 2019; Frank et al, 2016; Husain & Bhatia, 2018; Vasishth et al, 2010). Again, since the experimental work has explored verbal dependencies, we would be interested in probing such dependencies in this study.…”

Section: Predictionsmentioning

confidence: 99%

“…The source of this grammaticality illusion has been attributed to working memory load (Frank & Ernst, 2019; Gibson & Thomas, 1999; Vasishth et al, 2010; also see Christiansen & MacDonald, 2009). In a doubly center‐embedded structure, the HD between the nouns and the verbs is long and this requires multiple verbs to be maintained for a long time.…”

Section: Introductionmentioning

confidence: 99%

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Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

et al. 2020

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Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008Hawkins, 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 show that the comprehension system can adapt to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject-Verb-Object (SVO) or Subject-Object-Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the direction of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results Correspondence should be sent to Himanshu Yadav is now affiliated with University of Potsdam, Germany. Vishakha Shukla is now affiliated with Shroff Charity Eye Hospital, Delhi, India. show that most languages tend to avoid greater structural depth. Together, these results show evidence for "limited adaptability" to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Predictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

et al. 2020

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“…German materials are shown in the sentences (4); these are word‐for‐word translations of the English materials in (3). Vasishth et al () probed the structural forgetting effect using reading times for material after the end of the verb phrases, and Frank and Ernst () have replicated the effect using acceptability judgments (but see Bader, ; Häussler & Bader, , for complicating evidence).…”

Section: Structural Forgettingmentioning

confidence: 99%

Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing

2020

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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 the surprisal of the word given a lossy memory representation of the context—that is, a memory representation that does not contain complete information about previous words. We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language‐dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory‐based theories, can be derived from lossy‐context surprisal as a special case of a novel, more general principle called information locality.

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Sentence processing in Turkish: A review and future directions

Deniz

2025

Lingua

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Judgements about double-embedded relative clauses differ between languages

Cited by 19 publications

References 29 publications

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

Word Order Typology Interacts With Linguistic Complexity: A Cross‐Linguistic Corpus Study

Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing

Sentence processing in Turkish: A review and future directions

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