ALPARC: artificial languages with phonological and acoustic rhythmicity controls

Lorenzo Titone; Nikola Milosevic; Lars Meyer

doi:10.3389/fpsyg.2026.1786300

ALPARC: artificial languages with phonological and acoustic rhythmicity controls

Front Psychol. 2026 May 15:17:1786300. doi: 10.3389/fpsyg.2026.1786300. eCollection 2026.

Authors

Lorenzo Titone¹, Nikola Milosevic^{2

3}, Lars Meyer^{1

4

5}

Affiliations

¹ Max Planck Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
² Methods and Development Group Neural Data Science and Statistical Computing, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
³ Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany.
⁴ Department of English and Linguistics, Johannes Gutenberg University Mainz, Mainz, Germany.
⁵ Clinic for Phoniatrics and Pediatric Audiology, University Hospital Münster, Münster, Germany.

Abstract

Infants and adults show the remarkable ability to learn from statistical regularities in the environment. Seminal studies on statistical learning in language acquisition suggested that transitional probabilities between syllables are decisive for word learning. Yet, recent work cautioned that acoustic and phonological regularities 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 (ALPARC). First, we explain all functionalities of ALPARC and provide 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 stationary transitional probability distributions and reduce acoustic and phonological confounds relative to stimuli used in prior studies. We conclude that ALPARC is a useful tool to overcome current uncertainties in future SL studies.

Keywords: Python (programming language); artificial grammar learning; frequency-tagging; phonology; speech; statistical learning; toolbox; transitional probabilities.