Individuals are readily able to extract and encode statistical
information from their environment (or statistical learning).
However, the bulk of the literature 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). In this
paper, we investigate how and how well distributional learning
can be measured by exploring the relationship between and
psychometric properties of two measures: discrimination
judgements and frequency estimates. Reliable performance
was observed in both measures across two different
distributional learning tasks (natural and artificial).
Discrimination judgements and frequency estimates also
significantly correlated with one another in both tasks, and
performance on all tasks accounted for the majority of variance
across tasks (55%). These results suggest that distributional
learning can be measured reliably, and may tap into both the
ability to discriminate between relative frequencies and to
explicitly estimate them.