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
TypeCommunication / Conférence
Conference title39th Annual Meeting of the Cognitive Science Society
Conference countryUnited Kingdom
Book titleCogSci 2017, Proceedings of the 39th Annual Meeting of the Cognitive Science Society, London, UK 26-29 July 2017
Cognitive Science Society
MetadataShow full item record
Ryder, Robin J.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
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 / KeywordsBayesian Hierarchical Finite Mixture Models; Psycholinguistics; Sentence Comprehension; Chinese RelativeClauses; Direct-Access Model; K-fold Cross-Validation
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