This study explored the interaction between learning conditions, linguistic complexity, and first language (L1) syntactic transfer in semiartificial grammar learning by conceptually replicating and extending Tagarelli et al. (2016). We changed the L1 background, elicited production data during debriefing, and added a binary mixed-effects logistic regression analysis to compare variability at learner and item levels with group-level variation on exposure condition, linguistic complexity, and their interaction. Our results replicated those of the original study regarding the comparative efficacy of explicit instruction; however, we also found a condition × complexity interaction absent in the original study. Debriefing sentence-production data suggest that the changed L1-L2 typological distance may have leveled off the advantage of explicit instruction in the learning of the complex V2-VF structure. Finally, our mixed-effects modeling analysis revealed that variability at learner and item levels accounted for a larger proportion of the variance of the outcomes than all the predictors combined.

中文翻译：

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半人工语言学习中的学习条件、语言复杂性和第一次语言转移

本研究通过概念上复制和扩展 Tagarelli 等人的研究，探讨了半人工语法学习中学习条件、语言复杂性和第一语言 (L1) 句法迁移之间的相互作用。（2016 年）。我们更改了 L1 背景，在汇报过程中获取生产数据，并添加了二元混合效应逻辑回归分析，以比较学习者和项目级别的可变性与暴露条件、语言复杂性及其交互的组级差异。我们的结果复制了关于明确教学的比较效果的原始研究。然而，我们还发现原始研究中没有条件×复杂性相互作用。汇报造句数据表明，改变的 L1-L2 类型距离可能已经使显式教学在学习复杂的 V2-VF 结构中的优势趋于平稳。最后，我们的混合效应建模分析表明，学习者和项目级别的可变性占结果方差的比例大于所有预测变量的总和。