A hierarchical Bayesian model for syntactic priming
This work addresses a theoretical problem in psycholinguistics by modeling syntactic priming phenomena, but it is incremental as it builds on existing learning frameworks to explain known effects.
The authors tackled the problem of reconciling three empirical properties of syntactic priming (lexical boost, inverse frequency effect, asymmetrical decay) using a hierarchical Bayesian model (HBM), showing that the model captures these properties in simulations and suggests an implicit learning account can explain phenomena typically attributed to residual activation.
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.