ALPARC: Artificial Languages with Phonological and Acoustic Rhythmicity Controls
Titone, L.; Milosevic, N.; Meyer, L.
Show abstract
Infants and adults show the remarkable ability to learn from statistical regularities in the environment. Seminal studies in language acquisition suggested that transitional probabilities between syllables are decisive for language learning. Yet, recent work cautioned that acoustic and phonological regularities can confound transitional probabilities, compromising interpretability. Furthermore, prior linguistic background can impact the learning of a new (artificial) language. To control for such confounds, we developed an open-source Python toolbox that generates Artificial Languages with Phonological and Acoustic Rhythmicity Controls (APLARC). First, we explain all functionalities of ALPARC through a step-by-step guide. Then, we demonstrate how ALPARC generates syllable streams encompassing pseudowords that are tailored to critical statistics of real languages. Our results show that ALPARC streams attain more stationary transitional probability distributions and minimize phonological and acoustic confounds relative to stimuli used in prior studies. We conclude that ALPARC would greatly boost the interpretability of future language learning research.
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