Summary: A new study introduces a multilingualism calculator that quantifies how multilingual a person truly is, offering a clearer alternative to vague labels like “bilingual.” By combining age of acquisition with self-rated listening, speaking, reading, and writing skills across languages, the tool generates both a multilingualism score and a language-dominance profile.
Validation across young and older adults showed that the calculator matches the accuracy of far more complex assessment methods. This work provides a streamlined, evidence-based way to describe language backgrounds for research, education, and clinical use.
Key Facts
- New Measurement Tool: Calculator integrates age of language acquisition and self-rated skills across modalities.
- Accurate Scoring: Validated against complex language-background methods with nearly identical results.
- Dominance Profile: Computes which language is strongest based on comparative proficiency.
- Wide Language Support: Works across ~50 languages, including sign language and custom entries.
Source: NYU
More than half of the world’s population speaks more than one language—but there is no consistent method for defining “bilingual” or “multilingual.” This makes it difficult to accurately assess proficiency across multiple languages and to describe language backgrounds accurately.
A team of New York University researchers has now created a calculator that scores multilingualism, allowing users to see how multilingual they actually are and which language is their dominant one.
The work, which uses innovative formulas to build the calculator, is reported in the journal Bilingualism: Language and Cognition.
“Multilingualism is a very broad label,” explains Esti Blanco-Elorrieta, an assistant professor of psychology and neural science at NYU and the paper’s senior author.
“These new formulas provide a clear, evidence-based way to understand your language strengths and how multilingual you truly are, bringing scientific clarity to an everyday part of life for millions of people.”
The calculator works in nearly 50 languages, including American Sign Language, and allows users to fill in an unlisted language.
Blanco-Elorrieta and Xuanyi Jessica Chen, an NYU doctoral student and the paper’s lead author, developed the formulas—embedded in a multilingual calculator that users can deploy to measure their multilingualism and language dominance—that are drawn from two primary variables:
- Age of language acquisition for listening, reading, speaking, and writing
- Self-rated language proficiency for listening, reading, speaking, and writing
The calculator then yields a multilingualism score, which indicates how multilingual a person is on a scale from monolingual to perfect polyglot. The language-dominance is separately tabulated by calculating the difference in ability between languages.
The authors—both multilingual speakers—note that past research has shown that self-rated language proficiency is, in fact, an accurate and efficient measure of actual language proficiency. The researchers also implemented other statistical controls to minimize self-rating bias.
They add that, similarly, age of language acquisition has been shown to be a predictor of abilities: the earlier one learns a language, the more likely it is they will be able to master native-like proficiency in that language.
The researchers validated their measure by testing it in two distinct populations: healthy young bilinguals and older bilinguals with language impairments. They compared their results to those obtained from existing methods that rely on acquiring much more extensive language background information. Across both groups, the formulas produced language-dominance results that were nearly identical to those generated by more complicated measures, showing that the new approach is both simple and accurate.
“Rather than just labeling someone as ‘bilingual’ or ‘monolingual,’ this tool quantifies how multilingual one is,” notes Chen.
“This calculator offers a transparent, quantitative tool that researchers, clinicians, and educators can adopt to better characterize multilingual populations, ultimately improving research quality and real-world applications—from language education to clinical assessment,” concludes Blanco-Elorrieta.
Funding: The research was supported by grants from the National Institutes of Health (R00DC019973) and the National Science Foundation (2446452).
Key Questions Answered:
A: Individuals differ in how and when they learn each language, so broad labels don’t capture meaningful variation in proficiency or dominance.
A: It quantifies multilingualism using age of acquisition and self-rated abilities in listening, speaking, reading, and writing for each language.
A: Validation tests show that the calculator produces dominance profiles comparable to traditional, more time-consuming assessments.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this neurotech and multilingualism research news
Author: James Devitt
Source: NYU
Contact: James Devitt – NYU
Image: The image is credited to Neuroscience News
Original Research: Open access.
“A theoretically driven and empirically grounded calculation for language dominance and degree of multilingualism” by Esti Blanco-Elorrieta et al. Bilingualism Language and Cognition
Abstract
A theoretically driven and empirically grounded calculation for language dominance and degree of multilingualism
Bilingualism research has long been challenged by a lack of a unified approach to quantifying language dominance and degree of multilingualism.
While numerous questionnaires (e.g., LHQ, BLP, LEAP‑Q, and LUQ) provide valuable data on language background variables, they lack a standardized formula to compute key measures from it.
We introduce two formulas that synthesize critical linguistic variables to efficiently calculate language dominance and a multilingualism score that ranges from perfect monolingualism to native-like proficiency in multiple languages.
Validation across two large datasets shows our dominance measure closely aligns with more complex PCA methods while being simpler and more efficient.
