心理信息
蓝图
心理学
认知
数据科学
块(置换群论)
研究中心
认知心理学
计算机科学
认知科学
梅德林
医学
政治学
机械工程
几何学
数学
神经科学
法学
工程类
病理
作者
Ram Frost,Blair C. Armstrong,Morten H. Christiansen
出处
期刊:Psychological Bulletin
[American Psychological Association]
日期:2019-10-03
卷期号:145 (12): 1128-1153
被引量:218
摘要
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).
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