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