Statistical learning in a serial reaction time task: access to separable statistical cues by individual learners

Abstract

The ability of adult learners to exploit the joint and conditional probabilities in a serial reaction time task containing both deterministic and probabilistic information was investigated. Learners used the statistical information embedded in a continuous input stream to improve their performance for certain transitions by simultaneously exploiting differences in the predictability of 2 or more underlying statistics. Analysis of individual learners revealed that although most acquired the underlying statistical structure veridically, others used an alternate strategy that was partially predictive of the sequences. The findings show that learners possess a robust learning device well suited to exploiting the relative predictability of more than I source of statistical information at the same time. This work expands on previous studies of statistical learning, as well as studies of artificial grammar learning and implicit sequence learning.