Investigating Statistical Learning of Nonadjacent Dependencies: Running Statistical Learning Tasks in Non-WEIRD Populations
Rebecca Louise Ann Frost, Marisa Casillas · 2021
Language acquisition is complex.However, one thing that has been suggested to help learning is the way that information is distributed throughout language; co-occurrences among particular items (e.g., syllables and words) have been shown to help learners discover the words that a language contains and figure out how those words are used.Humans' ability to draw on this information-"statistical learning"-has been demonstrated across a broad range of studies.However, evidence from non-WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies is critically lacking, which limits theorizing on the universality of this skill.We extended work on statistical language learning to a new, non-WEIRD linguistic population: speakers of Yélî Dnye, who live on a remote island off mainland Papua New Guinea (Rossel Island).We performed a replication of an existing statistical learning study, training adults on an artificial language with statistically defined words, then examining what they had learnt using a two-alternative forced-choice test.Crucially, we implemented several key amendments to the original study to ensure the replication was suitable for remote field-site testing with speakers of Yélî Dnye.We made critical changes to the stimuli and materials (to test speakers of Yélî Dnye, rather than English), the instructions (we re-worked these significantly, and added practice tasks to optimize participants' understanding), and the study format (shifting from a lab-based to a portable tablet-based setup).We discuss the requirement for acute sensitivity to linguistic, cultural, and environmental factors when adapting studies to test new populations.Investigating Statistical Learning of Nonadjacent Dependencies: Running Statistical Learning Tasks in Non-WEIRD Populations