Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

中文翻译：

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大脑如何学习环境结构？理解统计学习的神经认知机制的十个核心原则。


尽管越来越多的研究致力于研究人类如何编码环境模式，但对于支持统计学习的神经认知机制的本质，以及哪些因素限制或促进其在个体、物种和学习情境中的出现，仍然没有明确的共识。基于对输入模态和领域、输入结构和复杂性、注意力、神经解剖学基础、个体发育和系统发育的作用的研究回顾，提出了十项核心原则。具体来说，统计学习背后存在两套神经认知机制。首先，一套基于联想的、自动的、特定模态的学习机制是由皮质可塑性的一般原理介导的，从而改善了对遇到的刺激的处理和感知促进。其次，由前额叶皮层和相关的注意力和工作记忆网络介导的注意力依赖系统可以调节或控制学习，并且对于学习不相邻的依赖性和跨时间整合全局模式是必要的。这一理论框架有助于澄清相互矛盾的研究结果，并为未来的实证和理论努力提供基础。