Statistical learning (SL) is a cognitive mechanism that detects and extracts regularities from environmental input. SL is componential, varying across auditory/visual modalities and verbal/nonverbal domains (The verbal domain refers to communication through language, while the nonverbal domain encompasses communication without words, using cues like images and tones). The study explores the association between SL and second language (L2) grammar learning, considering its componential nature. Thirty‐four participants completed SL tasks and were trained with artificial grammar sequences over 5 days, followed by grammaticality judgment tasks. Visual‐nonverbal and auditory‐nonverbal SL mechanisms were implicated in the early stages of L2 grammar learning, while visual‐verbal SL played a role in the proficient phase. Results indicate modality and domain variation in the SL mechanism, supporting the multi‐component account. The findings contribute to understanding SL's role in L2 grammar learning and support the multi‐component account. They also have implications for L2 grammar learning.

The current study investigated the mental lexicon features of the Hakka-Mandarin dialect bilingual from two perspectives: the structural features of lexicons and the relations between lexicons. Experiment one used a semantic fluency task and complex-network analysis to observe the structural features of lexicons. Experiment two used a cross-language long-term repetition priming paradigm to explore the relations between lexicons, with three sub-experiments focusing on conceptual representation, lexical representation, and their relations, respectively. The results from experiment one showed that the dialect bilingual lexicons were small-world in nature, and the D2 (Mandarin) lexicon was better organized than the D1 (Hakka) lexicon. Experiment two found that D1 and D2 might have partially shared conceptual representations, separate lexical form representations, and partially shared lemma representations. Based on the findings, we tentatively proposed a two-layer activation model to simulate the lexicon features of dialect bilingual speakers.