Statistical learning (SL) is involved in a wide range of basic and higher-order cognitive functions and is taken to be an important building block of virtually all current theories of information processing. In the last 2 decades, a large and continuously growing research community has therefore focused on the ability to extract embedded patterns of regularity in time and space. This work has mostly focused on transitional probabilities, in vision, audition, by newborns, children, adults, in normal developing and clinical populations. Here we appraise this research approach and we critically assess what it has achieved, what it has not, and why it is so. We then center on present SL research to examine whether it has adopted novel perspectives. These discussions lead us to outline possible blueprints for a novel research agenda. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

---

统计学习研究：严格的审查和可能的新方向。

统计学习（SL）涉及广泛的基本和高阶认知功能，并被视为实际上是当前所有信息处理理论的重要组成部分。因此，在过去的20年中，一个庞大且不断发展的研究社区致力于提取时间和空间规律的嵌入模式。这项工作主要集中在正常发育和临床人群中新生儿，儿童，成人在视力，听觉上的过渡概率。在这里，我们对这种研究方法进行评估，并严格评估其取得的成就，尚未取得的成就以及取得成功的原因。然后，我们以当前的SL研究为中心，以检查它是否采用了新颖的观点。这些讨论使我们概述了新颖研究议程的可能蓝图。