The multi-faceted nature of visual statistical learning: Individual differences in learning conditional and distributional regularities across time and space

Emerging research has demonstrated that statistical learning is a modality-specific ability governed by domain-general principles. Yet limited research has investigated different forms of statistical learningwithinmodality. This paper explores whether there is one unified statistical learning mechanism within the visual modality, or separate task-specific abilities. To do so, we examined individual differences in spatial and nonspatial conditional and distributional statistical learning. Participants completed four visual statistical learning tasks: conditional spatial, conditional nonspatial, distributional spatial, and distributional nonspatial. Performance on all four tasks significantly correlated with each other, and performance on all tasks accounted for a large portion of the variance across tasks (57%). Interestingly, a portion of the variance of task performance (between 11% and 18%) was also accounted for by performance on each of the individual tasks. Our results suggest that visual statistical learning is the result of the interplay between a unified mechanism for extracting conditional and distributional statistical regularities across time and space, and an individual's ability to extract specific types of regularities.