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ALPARC: artificial languages with phonological and acoustic rhythmicity controls
Lorenzo Titone1, Nikola Milosevic2,3, Lars Meyer1,4,5
1Max Planck Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Frontiers in Psychology
|June 1, 2026
Summary
This study introduces ALPARC, a Python toolbox for creating artificial languages. ALPARC helps researchers study statistical learning by controlling for acoustic and phonological confounds in language acquisition research.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Computational Linguistics
Background:
- Statistical learning is crucial for language acquisition in infants and adults.
- Previous studies on statistical learning are limited by acoustic and phonological confounds.
- Prior linguistic experience can influence the learning of new languages.
Purpose of the Study:
- To introduce ALPARC, an open-source Python toolbox for generating artificial languages.
- To provide a comprehensive guide to ALPARC's functionalities.
- To demonstrate ALPARC's utility in controlling for confounds in statistical learning research.
Main Methods:
- Development of the ALPARC toolbox using Python.
- Generation of syllable streams with controlled phonological and acoustic properties.
- Tailoring pseudoword statistics to mirror those found in real languages.
Main Results:
- ALPARC-generated language streams exhibit stable transitional probability distributions.
- ALPARC effectively minimizes acoustic and phonological regularities that confound statistical learning.
- The toolbox provides a controlled environment for investigating language acquisition.
Conclusions:
- ALPARC offers a robust solution to address confounds in statistical learning research.
- This tool facilitates more accurate investigations into word learning mechanisms.
- ALPARC is a valuable resource for future studies on statistical learning and language acquisition.
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