Individual differences in distributional statistical learning: better frequency “discriminators” are better “estimators”

People can easily extract and encode statistical information from their environment. However, research has primarily focused on conditional statistical learning (i.e., the ability to learn joint and conditional relationships between stimuli) and has largely neglected distributional statistical learning (i.e., the ability to learn the frequency and variability of distributions). For example, learning that “E” is more common in the English alphabet than “Z.” In this article, we investigate how distributional learning can be measured by exploring the relationship between, and psychometric properties of, four different measures of distributional learning—from the ability to discriminate relative frequencies to the ability to estimate frequencies. We identified moderate relationships between four distributional learning measures and these tasks accounted for a substantial portion of the variance in performance across tasks (44.3%). A measure of divergent validity (intrinsic motivation) did not significantly correlate with any statistical learning measure and accounted for a separate portion of the variance across tasks. Our results suggest that distributional statistical learning encompasses the ability to discriminate between relative frequencies and estimating them.