论文标题
合成能力分析：认知神经影像学的经验评估和应用
Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging
论文作者
论文摘要
在实验科学中，统计功率分析通常在数据收集之前使用以确定所需的样本量。但是，当数据很难或收集昂贵时，传统的功率分析可能会昂贵。我们提出合成功率分析；在各种样本量下估算统计功率的框架，并凭经验探索了认知神经科学实验中样本量选择的合成功率分析的性能。为此，使用在观察到的认知过程中进行的隐式生成模型合成大脑成像数据。此外，我们提出了一个简单的程序来修改导致保守统计的统计检验。我们的经验结果表明，当提出的实验与先前进行的实验共享认知过程时，合成功率分析可能是试点数据收集的低成本替代方法。
In the experimental sciences, statistical power analyses are often used before data collection to determine the required sample size. However, traditional power analyses can be costly when data are difficult or expensive to collect. We propose synthetic power analyses; a framework for estimating statistical power at various sample sizes, and empirically explore the performance of synthetic power analysis for sample size selection in cognitive neuroscience experiments. To this end, brain imaging data is synthesized using an implicit generative model conditioned on observed cognitive processes. Further, we propose a simple procedure to modify the statistical tests which result in conservative statistics. Our empirical results suggest that synthetic power analysis could be a low-cost alternative to pilot data collection when the proposed experiments share cognitive processes with previously conducted experiments.
