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Himanshu Yadav
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Journal Articles
Publisher: Journals Gateway
Open Mind (2022) 6: 147–168.
Published: 15 September 2022
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Dependency length minimization is widely regarded as a cross-linguistic universal reflecting syntactic complexity in natural languages. A typical way to operationalize dependency length in corpus-based studies has been to count the number of words between syntactically related words. However, such a formulation ignores the syntactic nature of the linguistic material that intervenes a dependency. In this work, we investigate if the number of syntactic heads (rather than the number of words) that intervene a dependency better captures the syntactic complexity across languages. We demonstrate that the dependency length minimization constraint in terms of the number of words could arise as a consequence of constraints on the intervening heads and the tree properties such as node arity. The current study highlights the importance of syntactic heads as central regions of structure building during processing. The results show that when syntactically related words are nonadjacent, increased structure building in the intervening region is avoided.
Includes: Supplementary data
Journal Articles
Publisher: Journals Gateway
Open Mind (2022) 6: 1–24.
Published: 01 July 2022
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Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior, but here we show that there is systematic individual-level variation in cue weighting. Using the Lewis and Vasishth cue-based retrieval model, we estimated individual-level parameters for reading speed and cue weighting using 13 published datasets; hierarchical approximate Bayesian computation (ABC) was used to estimate the parameters. The modeling reveals a nuanced picture of cue weighting: we find support for the idea that some participants weight cues differentially, but not all participants do. Only fast readers tend to have the predicted higher weighting for structural cues, suggesting that reading proficiency (approximated here by reading speed) might be associated with cue weighting. A broader achievement of the work is to demonstrate how individual differences can be investigated in computational models of sentence processing without compromising the complexity of the model.
Includes: Supplementary data