... It allows us to propose mechanisms behind language behaviors and demonstrate whether these behaviors can be learned (e.g., Gelderloos, Kamelabad, & Alishahi, 2020;Huebner, Sulem, Fisher, & Roth, 2021;Nikolaus, Alishahi, & Chrupała, 2022). Recently, various studies have successfully used self-supervised artificial neural network models to simulate infant statistical learning, 1 demonstrating autonomous bootstrapping of phonemic and lexical discrimination (Lavechin, de Seyssel, Titeux et al., 2022), syllable and word segmentation (Khorrami & Räsänen, 2021), and learning of referential word meanings (Khorrami & Räsänen, 2021;Merkx, Scholten, Frank, Ernestus, & Scharenborg, 2023) from auditory or audiovisual language exposure without a need for strong linguistic priors or other innate biases. In terms of modeling studies, PLE has been previously examined in the context of speech emotion recognition (Vogelsang, Vogelsang, Diamond, & Sinha, 2023) and phonetic learning (Poli, Schatz, Dupoux, & Lavechin, 2024). ...