Individual differences in distributional statistical learning: better frequency ‘discriminators’ are better explicit ‘estimators’
Bethany Growns, Kristy A. Martire, Erwin J.A.T. Mattijssen · 2022
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 paper, we investigate how and how well 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 explicit frequencies. We identified moderate and stable 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. More complex measures also showed better reliability and internal consistency. Interestingly, a portion of the variance of task performance (between 9.7-13.4%) was also accounted for by performance on combinations of tasks. Our results suggest that distributional statistical learning is the result of the interplay between a unified mechanism for discriminating between relative frequencies and explicitly estimating them, and an individual's ability in these different tasks.