Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
Haskins Laboratories, New Haven, CT 06511, USA.
Trends Cogn Sci. 2022 Jan;26(1):25-37. doi: 10.1016/j.tics.2021.10.012. Epub 2021 Nov 19.
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了个体在统计结构学习方面的差异，将其与认知（障碍）能力的可变性联系起来。这种方法将统计学习（SL）视为一种普遍的个体能力，它是在一系列认知领域表现的基础。但是，是否有一种普遍的 SL 能力可以将个体从“差”的统计学习者中区分出来呢？为了阐明这一方法的假设，我们认为目前支持这一方法的证据是微不足道的。我们概述了另一种观点，即考虑不同认知领域中统计环境的可变性。一旦我们专注于针对现实世界感官输入统计数据进行调整的学习，就会出现对 SL 计算的另一种看法，这为 SL 研究带来了截然不同的前景。
