title: Flexible visual statistical learning: transfer across space and time.
abstract: The environment contains considerable information that is distributed across space and time, and the visual system is remarkably sensitive to such information via the operation of visual statistical learning (VSL). Previous VSL studies have focused on establishing what kinds of statistical relationships can be learned but have not fully explored how this knowledge is then represented in the mind. These representations could faithfully reflect the details of the learning context, but they could also be generalized in various ways. This was studied by testing how VSL transfers across changes between learning and test, and the results revealed a substantial degree of generalization. Learning of statistically defined temporal sequences was expressed in static spatial configurations, and learning of statistically defined spatial configurations facilitated detection performance in temporal streams. Learning of temporal sequences even transferred to reversed temporal orders during test when accurate performance did not depend on order, per se. These types of transfer imply that VSL can result in flexible representations, which may in turn allow VSL to function in ever-changing natural environments.
authors.affiliation: Department of Psychology, Yale University, New Haven, CT 06520-8205, USA. nicholas.turk-browne@yale.edu
authors.forename: Nicholas B
authors.initials: NB
authors.lastname: Turk-Browne
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Scholl
mesh_terms: D006801:Humans; D007858:Learning; D012984:Software; D013028:Space Perception; D013223:Statistics as Topic; D013998:Time Perception; D014796:Visual Perception