• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail

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

Vasishth, Shravan; Chopin, Nicolas; Ryder, Robin J.; Nicenboim, Bruno (2017), Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses, CogSci 2017, Proceedings of the 39th Annual Meeting of the Cognitive Science Society, London, UK 26-29 July 2017, Cognitive Science Society

View/Open
1702.00564(1).pdf (181.5Kb)
Type
Communication / Conférence
Date
2017
Conference title
39th Annual Meeting of the Cognitive Science Society
Conference date
2017-07
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
ISBN
978-0-9911967-6-0
Metadata
Show full item record
Author(s)
Vasishth, Shravan

Chopin, Nicolas

Ryder, Robin J.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Nicenboim, Bruno
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.
Subjects / Keywords
Bayesian Hierarchical Finite Mixture Models; Psycholinguistics; Sentence Comprehension; Chinese RelativeClauses; Direct-Access Model; K-fold Cross-Validation

Related items

Showing items related by title and author.

  • Thumbnail
    On Particle Learning 
    Schäfer, Christian; Iacobucci, Alessandra; Robert, Christian P.; Mengersen, Kerrie; Chopin, Nicolas; Ryder, Robin J.; Marin, Jean-Michel (2010) Document de travail / Working paper
  • Thumbnail
    Bayesian matrix completion: prior specification and consistency 
    Rousseau, Judith; Chopin, Nicolas; Cottet, Vincent; Alquier, Pierre (2014) Document de travail / Working paper
  • Thumbnail
    Free energy Sequential Monte Carlo, application to mixture modelling 
    Chopin, Nicolas; Jacob, Pierre E. (2011) Communication / Conférence
  • Thumbnail
    Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process 
    Rousseau, Judith; Chopin, Nicolas; Liseo, Brunero (2012) Article accepté pour publication ou publié
  • Thumbnail
    Rupture organisationnelle et continuité culturelle : une étude de cas - la privatisation d'un hôpital public d'une région centrale de la Chine 
    Li, Fang (2015-10) Thèse
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo