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Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses

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1702.00564(1).pdf (181.5Kb)
Date
2017
Dewey
Analyse
Sujet
Bayesian Hierarchical Finite Mixture Models; Psycholinguistics; Sentence Comprehension; Chinese RelativeClauses; Direct-Access Model; K-fold Cross-Validation
Conference name
39th Annual Meeting of the Cognitive Science Society
Conference date
07-2017
Conference city
Londres
Conference country
United Kingdom
Book title
CogSci 2017, Proceedings of the 39th Annual Meeting of the Cognitive Science Society, London, UK 26-29 July 2017
Publisher
Cognitive Science Society
Year
2017
ISBN
978-0-9911967-6-0
URI
https://basepub.dauphine.fr/handle/123456789/17985
Collections
  • CEREMADE : Publications
Metadata
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Author
Vasishth, Shravan
Chopin, Nicolas
Ryder, Robin J.
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Nicenboim, Bruno
Type
Communication / Conférence
Abstract (EN)
We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the retrieval time (the distance-based account). An alternative theory, direct-access, assumes that retrieval times are a mixture of two distributions: one distribution represents successful retrievals (these are independent of dependency distance) and the other represents an initial failure to retrieve the correct dependent, followed by a reanalysis that leads to successful retrieval. We implement both models as Bayesian hierarchical models and show that the direct-access model explains Chinese relative clause reading time data better than the distance account.

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