Statistical learning abilities and their relation to language: Insights from individual differences
Noam Siegelman, Haskins Laboratories
Tuesday, Jan 19 2020, 16:00-17:00 GMT
Zoom Details: [Please Request]
Research on Statistical Learning (SL) had a profound impact on the study of language, highlighting aspects of the linguistic input that can be learned from experience. As a result, many researchers now hold the view that SL plays a key role in language acquisition and processing. But how can this theorized link be experimentally demonstrated? In the first part of the talk, I will review work demonstrating how the study of individual differences can reveal the underlying computations shared between SL and language. At the same time, I will highlight some challenges that this line of work faces due to the complex and multi-componential nature of SL. In light of these challenges, in the second part of the talk I will propose an alternative approach for studying the role of SL in language, which is based on an analysis of actual linguistic behavior given the statistical regularities embedded in the input. I will present studies applying this approach in the domain of reading acquisition, examining individual differences in children’s sensitivity to different types of regularities in their writing system and the relations between this variability and their emerging reading skills. I will conclude by discussing the implications of this work to SL theory, proposing a view that ties SL skills to success or failure in finding the balance between the impact of multiple regularities that co-exist in the sensory input in light of their systematicity.