The cave of shadows: Addressing the human factor with generalized additive mixed models
The cave of shadows: Addressing the human factor with generalized additive mixed models
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DOI：
10.1016/j.jml.2016.11.006
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发表时间：
2017-06-01
影响因子：
4.3
通讯作者：
Bates, Douglas
中科院分区：
文献类型：
--
作者：
Baayen, Harald;Vasishth, Shravan;Bates, Douglas
广义可加混合模型是广义线性混合模型的推广，它使得处理实验数据中的时间自相关结构成为可能。这种自相关结构很可能是学习、疲劳或实验中注意力起伏的结果(“人的因素”)。与大麦的分子或情节不同，心理语言学实验中的受试者是聪明的生物，他们的生存依赖于对环境的不断适应，包括实验的环境。三个数据集表明，人为因素可能与感兴趣的预测因素交互作用，包括阶乘和度量。我们还表明，特别是在广义加性模型的框架内，在非线性世界中，将考虑所有可能的偶然性的最大限度地拟合复杂模型作为建模策略是不可取的。讨论了用于验证性和探索性数据分析的替代建模策略。(C)2016 Elsevier Inc.保留所有权利。
Generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data. This autocorrelational structure is likely to be a consequence of learning, fatigue, or the ebb and flow of attention within an experiment (the 'human factor'). Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival on constant adaptation to their environment, including the environment of an experiment. Three data sets illustrate that the human factor may interact with predictors of interest, both factorial and metric. We also show that, especially within the framework of the generalized additive model, in the nonlinear world, fitting maximally complex models that take every possible contingency into account is illadvised as a modeling strategy. Alternative modeling strategies are discussed for both confirmatory and exploratory data analysis. (C) 2016 Elsevier Inc. All rights reserved.