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

Shravan Vasishth (), Nicolas Chopin, Robin Ryder () and Bruno Nicenboim ()
Additional contact information
Shravan Vasishth: University of Potsdam
Robin Ryder: CNRS; Université Paris-Dauphine; PSL
Bruno Nicenboim: University of Potsdam

No 2017-34, Working Papers from Center for Research in Economics and Statistics

Abstract: 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.

Keywords: Bayesian Hierarchical Finite Mixture Models; Psycholinguistics; Sentence Comprehension; Chinese Relative Clauses; Direct-Access Model; K-fold Cross-Validation (search for similar items in EconPapers)
Pages: 6 pages
Date: 2017-05-01
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://crest.science/RePEc/wpstorage/2017-34.pdf CREST working paper version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:crs:wpaper:2017-34

Access Statistics for this paper

More papers in Working Papers from Center for Research in Economics and Statistics Contact information at EDIRC.
Bibliographic data for series maintained by Secretariat General () and Murielle Jules Maintainer-Email : murielle.jules@ensae.Fr.