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.

Forensic feature-comparison examiners compare-or "match"-evidence samples (e.g., fingerprints) to provide judgments about the source of the evidence. Research demonstrates that examiners in select disciplines possess expertise in this task by outperforming novices-yet the psychological mechanisms underpinning this expertise are unclear. This article investigates one implicated mechanism: statistical learning, the ability to learn how often things occur in the environment. This ability is likely important in forensic decision-making as samples sharing rarer statistical information are more likely to come from the same source than those sharing more common information. We investigated 46 fingerprint examiners' and 52 novices' statistical learning of fingerprint categories and application of this knowledge in a source-likelihood judgment task. Participants completed four measures of their statistical learning (frequency discrimination judgments, bounded and unbounded frequency estimates, and source-likelihood judgments) before and after familiarization to the "ground-truth" category frequencies. Compared to novices, fingerprint examiners had superior domain-specific statistical learning across all measures-both before and after familiarization. This suggests that fingerprint expertise facilitates domain-specific statistical learning-something that has important theoretical and applied implications for the development of training programs and statistical databases in forensic science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).