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


  • Shravan Vasishth

    (University of Potsdam)

  • Nicolas Chopin


  • Robin Ryder

    (CNRS; Université Paris-Dauphine; PSL)

  • Bruno Nicenboim

    (University of Potsdam)


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.

Suggested Citation

  • Shravan Vasishth & Nicolas Chopin & Robin Ryder & Bruno Nicenboim, 2017. "Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses," Working Papers 2017-34, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2017-34

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