Distributional Statistical Learning: How and How Well Can It Be Measured?

AbstractIndividuals 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.

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