  The effect of syntactic priming exhibits three well-documented empirical
properties: the lexical boost, the inverse frequency effect, and the
asymmetrical decay. We aim to show how these three empirical phenomena can be
reconciled in a general learning framework, the hierarchical Bayesian model
(HBM). The model represents syntactic knowledge in a hierarchical structure of
syntactic statistics, where a lower level represents the verb-specific biases
of syntactic decisions, and a higher level represents the abstract bias as an
aggregation of verb-specific biases. This knowledge is updated in response to
experience by Bayesian inference. In simulations, we show that the HBM captures
the above-mentioned properties of syntactic priming. The results indicate that
some properties of priming which are usually explained by a residual activation
account can also be explained by an implicit learning account. We also discuss
the model's implications for the lexical basis of syntactic priming.
