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Effects of Syntactic Distance and Word Order on Language Processing: An Investigation Based on a Psycholinguistic Treebank of English

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Abstract

We conducted a broad-coverage investigation of the effects of syntactic distance and word order on language processing against a dependency-annotated reading time corpus of English. A combined method of quantitative syntax and psycholinguistic analyses was adopted to yield converging evidence. It was found that (i) head-initial structures allow greater structural complexity, i.e., larger head-dependent distance, than head-final structures in both language comprehension and production; (ii) within the capacity limit of working memory, syntactic distance is a positive predictor of reading time for a word with a preceding head, whereas a negative predictor of reading time for a word with a following head; and (iii) at the sentence level, syntactic distance is a significant predictor of sentence reading time. These results suggest that (i) different word orders may enjoy different processing mechanisms in terms of cognitive difficulty and processes, which can be explained by an incremental language parser; and (ii) in addition to distance, word order should also be considered as a factor affecting language processing, which is an important extension to distance-based language processing models. Taken as a whole, our study paves the way for corpus-based integration of quantitative linguistic and psycholinguistic methods into understanding language processing and its underlying cognitive mechanisms.

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Notes

  1. Demberg and Keller (2008) claimed that integration cost is not a sufficiently broad-coverage measure for naturally-occurring texts, because it only assigns difficulty to nouns and verbs. As assumed by the DLT, only the processing of nouns and verbs induces cognitive effort, but Demberg and Keller found proof suggesting the processing difficulty of other word categories, e.g., the auxiliaries, is larger than zero. This contradiction made them to conclude “It could be interesting to extend DLT in a way that makes it possible to also assign an integration cost to those word categories”.

  2. We did not choose the Universal Dependencies parses because its linguistic validity is being debated within the dependency grammar community, due mainly to its treatment of the content word as the head of the function word (e.g., Osborne & Gerdes, 2019).

  3. Note that DDM was investigated as a property of individual languages rather than different word orders within a language (e.g., Ferrer-i-Cancho et al., 2022; Gildea & Temperley, 2010). Given the aim of the current study, however, it is necessary to control for word orders, i.e., DDir.

  4. Although the fitting to power-law functions is good overall as suggested by R2, it gives a bad fit when DD = 1, as pointed out by one reviewer. Given that short and long dependencies are best fit by exponential and power-law models respectively (e.g., Ferrer-i-Cancho et al., 2022; Lu & Liu, 2016), the bad fit when DD = 1 may suggest that the distributions of DD is exponential with more than one regime. We did a re-fitting of our data, and found that DD distribution is best fit by an exponential function when DD ≤ 2 (head-final: y = 7863.1e−0.982x, head-initial: y = 2807.9e−0.452x, R2 > 0.9), and a power-law function when DD > 2 (head-final: y = 581.2x−1.547, head-initial: y = 690.09x−1.26, R2 > 0.9). Note that under each regime, head-final dependencies have a smaller regression coefficient than head-initial dependencies, which is consistent with the conclusions above.

  5. The probability of head-final or head-initial dependencies at a given DD = the frequency of head-final or head-initial dependencies at a given DD / the frequency of total dependencies at a given DD. For example, when DD = 1, there are 4,731 dependencies, among which 2,945 are head-final and the other 1,786 are head-initial. Thus, the proportions of head-final and head-final dependencies when DD = 1 are 62.25% (= 2,946 / 4,731) and 37.75% (= 1,785 / 4,731), respectively. This method helps one obtain a quick overview of the distributions of word orders at different syntactic complexities (DDs).

  6. Note that we define DD as a word’s linear distance to its head (see “Dependency distance and dependency direction” section). By this definition, only words that have heads are assigned DD, excluding non-headed words, i.e., sentence roots.

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Acknowledgements

The authors would like to thank Timothy Osborne for improving the language of an earlier draft and the two anonymous reviewers for their insightful comments.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Institute of Quantitative Linguistics, Beijing Language and Culture University, No.15 Xueyuan Road, Beijing, CN-100083, China

    Ruochen Niu

  2. Department of Linguistics, Zhejiang University, No. 866 Yuhangtang Road, Hangzhou, CN-310058, China

    Ruochen Niu & Haitao Liu

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RN: Conceptualization, Methodology, Formal analysis and investigation, Writing—original draft preparation, Writing—review and editing. HL: Conceptualization, Writing—review and editing, Resources, Supervision.

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Correspondence to Haitao Liu.

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Niu, R., Liu, H. Effects of Syntactic Distance and Word Order on Language Processing: An Investigation Based on a Psycholinguistic Treebank of English. J Psycholinguist Res 51, 1043–1062 (2022). https://doi.org/10.1007/s10936-022-09878-4

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