title: Statistical learning and its consequences.
abstract: Statistical learning refers to an unconscious cognitive process in which repeated patterns, or regularities, are extracted from the sensory environment. In this chapter, I describe what is currently known about statistical learning. First, I classify types of regularities that exist in the visual environment. Second, I introduce a family of experimental paradigms that have been used to study statistical learning in the laboratory. Third, I review a series of behavioral and functional neuroimaging studies that seek to uncover the underlying nature of statistical learning. Finally, I consider ways in which statistical learning may be important for perception, attention, and visual search. The goals of this chapter are thus to highlight the prevalence of regularities, to explain how they are extracted by the mind and brain, and to suggest that the resulting knowledge has widespread consequences for other aspects of cognition.
authors: Department of Psychology, Princeton University, Green Hall, Princeton, NJ 08540, USA. ntb@princeton.edu
authors: Nicholas B
authors: NB
authors: Turk-Browne
mesh_terms: D059031:Anticipation, Psychological; D001288:Attention / Q000502:physiology; D001931:Brain Mapping; D005793:Generalization, Psychological; D006801:Humans; D007858:Learning / Q000502:physiology; D008279:Magnetic Resonance Imaging; D046709:Pattern Recognition, Physiological; D012660:Semantics; D013028:Space Perception; D013223:Statistics as Topic