This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

中文翻译：

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双语视觉单词识别中的非线性：广义加性建模简介

本文通过对 Dijkstra 等人的双语者词汇决策数据的重新分析，介绍了广义加法混合模型（GAMM）和分位数广义加法混合模型（QGAMM）。(2010) 和 Miwa 等人。（2014）。我们说明了回归样条如何用于测试形式上跨语言相似性的非线性效应以及控制实验试验效应。我们进一步说明了跨语言语义相似度和词频之间非线性交互的张量积平滑。最后，我们展示了 QGAMM 如何帮助阐明特定预测变量的影响是否在 RT 分布中保持不变。