title: Dissociable behavioural outcomes of visual statistical learning.
abstract: Statistical learning refers to the extraction of probabilistic relationships between stimuli and is increasingly used as a method to understand learning processes. However, numerous cognitive processes are sensitive to the statistical relationships between stimuli and any one measure of learning may conflate these processes; to date little research has focused on differentiating these processes. To understand how multiple processes underlie statistical learning, here we compared, within the same study, operational measures of learning from different tasks that may be differentially sensitive to these processes. In Experiment 1, participants were visually exposed to temporal regularities embedded in a stream of shapes. Their task was to periodically detect whether a shape, whose contrast was staircased to a threshold level, was present or absent. Afterwards, they completed a search task, where statistically predictable shapes were found more quickly. We used the search task to label shape pairs as "learned" or "non-learned", and then used these labels to analyse the detection task. We found a dissociation between learning on the search task and the detection task where only non-learned pairs showed learning effects in the detection task. This finding was replicated in further experiments with recognition memory (Experiment 2) and associative learning tasks (Experiment 3). Taken together, these findings are consistent with the view that statistical learning may comprise a family of processes that can produce dissociable effects on different aspects of behaviour.
authors.affiliation: Department of Psychology, University of California, Riverside, CA, USA.
authors.forename: Brett C
authors.initials: BC
authors.lastname: Bays
authors.affiliation: Department of Psychology, Princeton University, Princeton, NJ, USA.
authors.forename: Nicholas B
authors.initials: NB
authors.lastname: Turk-Browne
authors.affiliation: Department of Psychology, University of California, Riverside, CA, USA.
authors.forename: Aaron R
authors.initials: AR
authors.lastname: Seitz
keywords: Statistical learning; associative learning; multiple memory systems; perceptual thresholds; recognition memory