Lecture by Ram Frost "Statistical learning as an individual ability"
Ram Frost is from Department of Psychology, The Hebrew University, Jerusalem, Israel
Statistical learning (SL), the ability to extract the distributional properties of sensory input in time and space, is taken to be the main mechanism by which cognitive systems discover the underlying regularities of their environments. SL plays a key role in the segmentation, discrimination, and categorization of visual and auditory input, shaping the basic representations for a wide range of sensory, motor, and cognitive abilities. As such, SL has become a major theoretical construct in cognitive science. In recent years researchers show increased interest in individual abilities in SL. What determines individuals’ efficacy in detecting regularities in sensory input? What does it predict? Is it stable across modalities? Scientists explore these questions by trying to understand the source of variance in performance in a visual SL task through a novel methodology. The theoretical implications for a mechanistic explanation of SL was discussed.