  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.
