Dold Hannah M H, Fründ Ingo
AG Modellierung Kognitiver Prozesse, Technische Universität Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
PLoS One. 2014 Apr 7;9(4):e91710. doi: 10.1371/journal.pone.0091710. eCollection 2014.
Statistical modeling produces compressed and often more easily interpretable descriptions of experimental data in form of model parameters. When experimental manipulations target selected parameters, it is necessary for their interpretation that other model components remain constant. For example, psychophysicists use dose rate models to describe how behavior changes as a function of a single stimulus variable. The main interest is on shifts of this function induced by experimental manipulation, assuming invariance in other aspects of the function. Combining several experimental conditions in a joint analysis that takes such invariance constraints into account can result in a complex model for which no robust standard procedures are available. We formulate a solution for the joint analysis through repeated applications of standard procedures by allowing an additional assumption. This way, experimental conditions can be analyzed separately such that all conditions are implicitly taken into account. We investigate the validity of the supplementary assumption through simulations. Furthermore, we present a natural way to check whether a joint treatment is appropriate. We illustrate the method for the specific case of the psychometric function; however the procedure applies to other models that encompass multiple experimental conditions.
统计建模以模型参数的形式生成对实验数据的压缩且通常更易于解释的描述。当实验操作针对选定参数时，为了对其进行解释，其他模型组件必须保持不变。例如，心理物理学家使用剂量率模型来描述行为如何随单个刺激变量而变化。主要关注点在于由实验操作引起的该函数的变化，假定函数的其他方面不变。在联合分析中结合多个实验条件并考虑此类不变性约束可能会导致一个复杂的模型，对此没有可用的稳健标准程序。我们通过允许一个额外的假设，通过重复应用标准程序来为联合分析制定一个解决方案。这样，可以分别分析实验条件，从而隐含地考虑所有条件。我们通过模拟研究补充假设的有效性。此外，我们提出了一种检查联合处理是否合适的自然方法。我们针对心理测量函数的具体情况说明该方法；然而该程序适用于包含多个实验条件的其他模型。
