Implicit Statistical Learning: A Tale of Two Literatures
Implicit Statistical Learning: A Tale of Two Literatures
复制标题
DOI：
10.1111/tops.12332
复制
发表时间：
2019-07-01
影响因子：
3
通讯作者：
Christiansen, Morten H.
中科院分区：
文献类型：
--
作者：
Christiansen, Morten H.
内隐学习和统计学习是解决心理学和认知科学中一个长期存在的问题的两种当代方法，这个问题是生物体如何从环境中的模式规则中提取出来的。虽然这两种方法都关注学习者利用分布特性发现输入模式的能力，但相关研究大多发表在不同的文献中，令人惊讶的是，它们之间的交叉授粉很少。这导致了对学习所涉及的计算的明显相反的观点，使基于组块的学习与概率学习对立起来。在这篇论文中，我追溯了这两种学习方法近一个世纪的历史渊源，并在“内隐统计学习”的标题下为它们的整合而争辩。基于记忆文献的基本见解，我勾画了一个基于统计的组块框架，旨在为理解内隐统计学习提供一个统一的基础。
Implicit learning and statistical learning are two contemporary approaches to the long-standing question in psychology and cognitive science of how organisms pick up on patterned regularities in their environment. Although both approaches focus on the learner's ability to use distributional properties to discover patterns in the input, the relevant research has largely been published in separate literatures and with surprisingly little cross-pollination between them. This has resulted in apparently opposing perspectives on the computations involved in learning, pitting chunk-based learning against probabilistic learning. In this paper, I trace the nearly century-long historical pedigree of the two approaches to learning and argue for their integration under the heading of "implicit statistical learning." Building on basic insights from the memory literature, I sketch a framework for statistically based chunking that aims to provide a unified basis for understanding implicit statistical learning.