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Tracking Words in Noise: How Classroom Acoustics Shape Children’s Statistical Learning of Speech

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

Meli R. Ayala¹ (), Jacob P. Momsen², Xinyi Zoe Mao³, Jennie K. Grammer³, Julie M. Schneider³; ¹University of Delaware, ²Yale University, ³University of California, Los Angeles

The ability to predict word boundaries from continuous speech in childhood, known as statistical learning (SL), is foundational for later language development. However, little is known about how adverse listening conditions (e.g., noisy classrooms) influence SL. The purpose of this study is to examine how acoustic environments influence behavioral and neural indices of SL. Twenty children aged 7-12 years old will participate in an auditory SL EEG experiment consisting of loud and quiet acoustic conditions. During the exposure phase, children listen to continuous speech containing embedded patterns while being prompted to press a button as fast as they can when they hear a target syllable. During the test phase, they choose which of two words most resembles the previously presented speech stream. Preliminary behavioral analyses using a linear mixed effects model indicate that participants (N=11) were significantly better at identifying words they had heard before (compared to words they had not heard) in the quiet condition compared to the loud condition (b = -0.48, SE = .23, p = 0.03). In this preliminary sample, accuracy in the quiet (M = 63.07%, p = 0.045), but not the loud (M = 52.27%, p = 0.28), condition was significantly above chance. We will use temporal response functions (TRFs) in the delta and theta frequency bands to model how different acoustic environments affect the ability to track speech during SL. This approach allows us to determine how cortical tracking relates to the ability to quickly and accurately track speech in noisy conditions.

Topic Area: LANGUAGE: Other