Integrating statistical learning into cognitive science
Date
2020Author
Bogaerts, Louisa
Frost, Ram
Christiansen, Morten H.
Metadata
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Louisa Bogaerts, Ram Frost, Morten H. Christiansen, Integrating statistical learning into cognitive science, Journal of Memory and Language, Volume 115, 2020, 104167, ISSN 0749-596X, https://doi.org/10.1016/j.jml.2020.104167
Abstract
Over the last two decades statistical learning (SL) has evolved into a key explanatory mechanism underlying the
incidental learning of regularities across different domains of cognition, such as language, visual and auditory
perception, and memory. Yet SL has mainly been investigated as an independent research area, separated from
the primary study of the relevant cognitive domains. The aim of this special issue is to foster a bilateral integration
of SL research with cognitive science: not only should domain-relevant evidence about the complexity
of real-world input become more tightly integrated into SL research, but non-SL studies should also carefully
consider the nature and range of statistical regularities that may affect learning and processing in a given domain.
Four papers on reading in this volume demonstrate that such integration can lead to a better understanding
of reading, while also revealing the complexity and abundance of different statistical patterns present in
printed text. Moving beyond disciplinary boundaries has the promise to broaden the focus of SL research beyond
simple artificial patterns, to examine the rich and subtle intricacies of real-world cognition. A final paper on the
neurobiological underpinnings of SL and the consolidation of learned statistical regularities further illustrates
what might be gained from a better integration of SL and memory research. We conclude by discussing possible
directions for taking an integrative approach to SL forward.