NOAM SIEGELMAN, Hebrew University of Jerusalem
Title: Statistical learning as an individual ability
Ebbinghaus Empire Series 2017-2018
Abstract: Statistical learning (SL) is typically defined as a domain-general mechanismby which cognitive systems extract regularities from sensory input, todiscover its underlying structure. As such, SL is taken to underlie a rangeof cognitive faculties, with a particularly important role in languageacquisition and use. In this talk, I take an individual-differences approachto examine SL as a theoretical construct, and its actual role in languagelearning and processing. First, I will review studies examining thecorrelations in performance across different SL tasks, and between SL tasksand linguistic outcomes. Based on the observed pattern of correlations,showing surprising degree of modality- and material-specificity, I willpresent a framework for understanding SL as a multi-faceted componentialtheoretical construct, where computations across different modalities anddomains differ in terms of the available information present to the learner.I will then proceed to empirically investigate this framework by focusing onSL performance in the visual modality. I will conclude by discussing theimplications of this approach for understanding individual-differences in SLabilities, and their relevance across cognition.
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