This study evaluates the performance of Recurrent Neural Network (RNN) and
Transformer in replicating cross-language structural priming: a key indicator
of abstract grammatical representations in human language processing. Focusing
on Chinese-English priming, which involves two typologically distinct
languages, we examine how these models handle the robust phenomenon of
structural priming, where exposure to a particular sentence structure increases
the likelihood of selecting a similar structure subsequently. Additionally, we
utilize large language models (LLM) to measure the cross-lingual structural
priming effect. Our findings indicate that Transformer outperform RNN in
generating primed sentence structures, challenging the conventional belief that
human sentence processing primarily involves recurrent and immediate processing
and suggesting a role for cue-based retrieval mechanisms. Overall, this work
contributes to our understanding of how computational models may reflect human
cognitive processes in multilingual contexts.

通过评估循环神经网络（RNN）和 Transformer 在复制跨语言结构启动方面的表现，这项研究对人类语言处理中抽象语法表示的关键指标进行了评估。研究重点关注涉及两种类型不同的语言的中英启动，探讨这些模型如何处理结构启动这一稳定现象，即暴露于特定句子结构会增加之后选择类似结构的可能性。此外，我们还利用大型语言模型（LLM）来衡量跨语言结构启动效果。研究结果表明 Transformer 在生成启动句子结构方面的性能优于 RNN，挑战了人类句子处理主要涉及循环和即时处理的传统观念，同时表明了基于线索的检索机制的作用。总体而言，这项工作对于了解计算模型如何反映多语言环境中人类认知过程具有重要贡献。