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Language-based statistical learning is a stable individual trait

Poster Session C - Sunday, March 8, 2026, 5:00 – 7:00 pm PDT, Fairview/Kitsilano Ballroom

Amiya Aggarwal¹ (), Laura Batterink¹; ¹University of Western Ontario

Statistical learning (SL) is the process of becoming sensitive to patterns in the environment and occurs simply through exposure to input. An important unresolved question is whether SL within a single domain is a reliable individual trait. Some prior studies have shown reliable domain-specific individual differences, though evidence is primarily limited to explicit measures of learning. Given that explicit memory abilities are generally stable at the individual level, results from these prior studies may reflect differences in explicit memory performance, rather than sensitivity to patterns in input. To further understand whether SL is a stable trait that reliably differs among individuals, we tested 94 participants’ ability to learn syllable patterns in two distinct artificial language streams separated by a two-week delay, using multiple measures of learning. Both testing sessions included exposure to a unique artificial language composed of repeating trisyllabic words. Subsequently, learning was assessed with the following measures, ordered from most explicit to most implicit: (1) the sequence continuation task, (2) the familiarity rating task, (3) the 2-alternative forced choice test, and (4) the reaction-time based target detection task. We found statistical learning to be a stable individual trait, with all 4 measures significantly correlated across sessions. We also found that performance on the implicit target detection task correlated across sessions independently of explicit knowledge of the words. This evidence of stable individual differences encourages further investigations targeted at understanding whether implicit statistical learning performance predicts individual differences in real-world language learning.

Topic Area: LANGUAGE: Other