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Few things happen at random.  Rather, events in our environment tend to follow quasi-regular patterns.  Most organisms have evolved statistical learning mechanisms that allow them, at least in part, to detect and appropriately respond to such patterns.  For example, in sensory perception neighboring regions in the perceptual space are highly correlated; a property that in the visual domain is relevant for detecting edges in visual scenes.  In human language, picking up on the frequency with which syllables co-occur may be helpful in discovering word boundaries, just as learning about the statistics of word co-occurrences appear to be helpful in determining syntactic structure.

Because of its ubiquity across cognition, statistical learning has emerged as as an important and rapidly growing area of research within the cognitive sciences.  This symposium will bring together an interdisciplinary group of researchers from psychology, linguistics, computer science, neuroscience, and philosophy to take stock of current research in statistical learning and its future prospects.