Statistical learning research: A critical review and possible new directions.

published Online First October 3, 2019Statistical 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.This article was supported by the European Research Council (ERC) Advanced Grant (project 692502-L2STAT) under the Horizon2020 research and innovation program, and by the Israel Science Foundation (Grant 217/14), awarded to Ram Frost. Blair C. Armstrong was supported by a Natural Sciences and Engineering (NSERC) Discovery Grant (2017– 06310). Morten H. Christiansen was supported in part by the Danish Council for Independent Research (FKK-Grant DFF-7013–00074)