A hierarchical Bayesian model for syntactic priming

TL;DR

This paper introduces a hierarchical Bayesian model that explains key properties of syntactic priming through implicit learning, unifying empirical phenomena within a single probabilistic framework.

Contribution

It presents a novel hierarchical Bayesian model that accounts for syntactic priming effects and properties, integrating empirical phenomena under a unified learning theory.

Findings

The model captures lexical boost, inverse frequency effect, and asymmetrical decay.

Priming effects can be explained by implicit learning rather than residual activation.

The model offers insights into the lexical basis of syntactic priming.

Abstract

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.