A comprehensive theory of human language requires a data set that is representative of the typological diversity present in the world’s 7,000+ languages (Evans & Levinson, 2009). However, the treatment of linguistic diversity in cognitive science has been historically uneven. While many subfields of linguistics explicitly aim to describe and explain variation (e.g., linguistic typology, phonology), adjacent fields like psycholinguistics have suffered from an overreliance on English (Blasi et al., 2022; Christiansen et al., 2022a; Kidd & Garcia, 2022), mimicking cognitive science in general (Henrich et al., 2010). This large data skew prevents the field from fully explaining how humans acquire, represent, and process language, one of the core defining features of our species.

Attempts to connect individual differences in statisticallearning with broader aspects of cognition have receivedconsiderable attention, but have yielded mixed results. Apossible explanation is that statistical learning is typicallytested using the two-alternative forced choice (2AFC) task.As a meta-cognitive task relying on explicit familiarityjudgments, 2AFC may not accurately capture implicitlyformed statistical computations. In this paper, we adapt theclassic serial-recall memory paradigm to implicitly teststatistical learning in a statistically-induced chunking recall(SICR) task. We hypothesized that artificial languageexposure would lead subjects to chunk recurring statisticalpatterns, facilitating recall of words from the input.Experiment 1 demonstrates that SICR offers more fine-grained insights into individual differences in statisticallearning than 2AFC. Experiment 2 shows that SICR hashigher test-retest reliability than that reported for 2AFC. Thus,SICR offers a more sensitive measure of individualdifferences, suggesting that basic chunking abilities mayexplain statistical learning.