Extensive sampling for complete models of individual brains
Extensive sampling for complete models of individual brains
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DOI：
10.1016/j.cobeha.2020.12.008
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发表时间：
2021-01-23
影响因子：
5
通讯作者：
Kay, Kendrick
中科院分区：
文献类型：
--
作者：
Naselaris, Thomas;Allen, Emily;Kay, Kendrick
在设计认知神经科学实验时，资源限制会导致个体大脑的采样变化和实验条件下的采样变化之间的根本权衡。在这里，我们认为，实验条件的广泛采样对于理解人类大脑如何处理复杂的刺激是必不可少的，任何一个大脑如何做到这一点的模型可能会推广到大多数其他大脑，并且将大量的受试者引入分析池可能会引入不必要的和不受欢迎的方差。因此，与传统观点相反，我们认为，对许多个体进行抽样提供的好处相对较少，而对有限数量的受试者进行广泛抽样对揭示一般原则更有成效。此外，强调个体大脑的深度非常适合利用现代神经科学测量技术在分辨率和信噪比方面的改进。
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.