In designing cognitive neuroscience experiments, resource limitations induce a fundamental trade-off between sampling variation across individual brains and sampling variation across experimental conditions. Here, we argue that extensive sampling of experimental conditions is essential for understanding how human brains process complex stimuli, that a model of how any one brain does this is likely to generalize to most other brains, and that introducing large numbers of subjects into an analysis pool is likely to introduce unnecessary and undesirable variance. Thus, contrary to conventional wisdom, we believe that sampling many individuals provides relatively few benefits and that extensive sampling of a limited number of subjects is more productive for revealing general principles. Furthermore, an emphasis on depth in individual brains is well-suited for capitalizing on the improvements in resolution and signal-to-noise ratio that are being achieved in modern neuroscientific measurement techniques.

在设计认知神经科学实验时，资源限制会在各个大脑的采样变化与实验条件的采样变化之间产生根本的折衷。在这里，我们认为，广泛的实验条件采样对于理解人脑如何处理复杂刺激至关重要，任何人的大脑如何执行此操作的模型很可能会推广到大多数其他人的大脑，并且将大量对象引入分析中池可能会引入不必要和不希望的差异。因此，与传统观点相反，我们认为对许多人进行抽样提供的好处相对较少，而对有限数量的主题进行广泛抽样对于揭示一般原理更有帮助。此外，