A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from ‘bad’ to ‘good’ statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

越来越多的研究调查统计结构学习中的个体差异，将它们与认知（dis）能力的可变性联系起来。这种方法将统计学习 (SL) 视为一种普遍的个人能力，它是一系列认知领域的表现的基础。但是，是否有一种通用的 SL 能力可以将个人从“坏”统计学习者分类为“好”统计学习者？在解释这种方法背后的假设时，我们建议目前支持它的证据很少。我们概述了另一种观点，该观点考虑了不同认知领域内统计环境的可变性。一旦我们专注于适应现实世界感官输入统计数据的学习，就会出现另一种 SL 计算视图，其对 SL 研究的前景完全不同。