A hierarchical Bayesian model for syntactic priming
<|reference_start|>A hierarchical Bayesian model for syntactic priming: 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.<|reference_end|>
@article{xu2024a,
    title={A hierarchical Bayesian model for syntactic priming},
    author={Weijie Xu, Richard Futrell},
    journal={arXiv preprint arXiv:2405.15964},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15964},
    primaryClass={cs.CL}
}