Evidence map›Paper›PMID 42220362›Full record

ArticleFrontiers in psychology2026

ALPARC: artificial languages with phonological and acoustic rhythmicity controls.

Lorenzo Titone, Nikola Milosevic, Lars Meyer

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lorenzo TitoneMax Planck Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Nikola MilosevicMethods and Development Group Neural Data Science and Statistical Computing, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Lars MeyerMax Planck Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial grammar learningfrequency-taggingphonologyPython (programming language)speechstatistical learningtoolboxtransitional probabilities

Identifiers

PMID42220362
PMCPMC13219251

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.