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Sketchpad Series

Is language-based statistical learning a stable individual trait?

Poster Session E - Monday, March 31, 2025, 2:30 – 4:30 pm EDT, Back Bay Ballroom/Republic Ballroom

Amiya Aggarwal¹ (aaggar7@uwo.ca), Laura Batterink¹; ¹University of Western Ontario

Over the past 30 years, statistical learning has been established as a learning mechanism that seems to be universal across development. However, previous research shows that individual statistical learning performance does not reliably correlate across tasks, modalities or even domains within the same modality (e.g. syllables versus nonlinguistic tones). An important question yet to be fully addressed is whether statistical learning within a single domain is a reliable individual trait. Some studies have shown domain-specific individual differences, though primarily using 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 statistical learning is a stable trait that reliably differs among individuals, we will test participants’ ability to segment words from two distinct artificial language streams at two time-points separated by a two-week delay, using multiple measures of learning. Both testing sessions will include exposure to a unique artificial language composed of repeating trisyllabic words. Subsequently, learning will be assessed with two explicit measures (a familiarity rating task and a 2-alternative forced choice test) as well as an implicit, reaction-time based target detection task. If statistical learning is a stable individual trait, we expect performance on the implicit target detection task to correlate across sessions, independently of explicit memory abilities. Such a result will encourage further investigations targeted at understanding whether implicit statistical learning performance predicts individual differences in real-world language learning.

Topic Area: LANGUAGE: Other