Summary: A new study presents a multilingualism calculator that quantifies how multilingual a person really is, offering a clearer alternative to vague labels like “bilingual.” By combining age of acquisition with self-assessed skills in listening, speaking, reading and writing across languages, the tool generates both a multilingualism score and a language proficiency profile.
Validation between younger and older adults showed that the calculator matches the accuracy of much more complex assessment methods. This work provides a simplified, evidence-based way of describing the origins of language for research, education, and clinical use.
Key facts
New measurement tool: The calculator integrates age of language acquisition and self-assessed skills across all modalities. Accurate scoring: Validated with complex linguistic background methods with almost identical results. Proficiency Profile: Calculates which language is stronger based on comparative proficiency. Extensive language support: Works in ~50 languages, including sign language and custom input.
Source: New York University
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 in multiple languages and accurately describe linguistic origins.
A team of researchers at New York University has created a calculator that scores multilingualism, allowing users to see how multilingual they really are and which language is dominant.
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, assistant professor of psychology and neural sciences at New York University and lead author of the paper.
“These new formulas provide a clear, evidence-based way to understand your language strengths and how multilingual you really are, bringing scientific clarity to an everyday part of millions of people’s lives.”
The calculator works in nearly 50 languages, including American Sign Language, and allows users to fill in a language not listed.
Blanco-Elorrieta and Xuanyi Jessica Chen, a doctoral student at New York University and lead author of the paper, developed the formulas (built into a multilingual calculator that users can deploy to measure their multilingualism and language proficiency) that are drawn from two main variables:
Age of language acquisition for listening, reading, speaking and writing. Self-assessed linguistic competence for listening, reading, speaking and writing.
The calculator then returns a multilingualism score, which indicates how multilingual a person is on a scale from monolingual to perfect polyglot. Language proficiency is tabulated separately by calculating the difference in ability between languages.
The authors, both multilingual speakers, point out that previous 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-assessment bias.
They add that age of language acquisition has similarly been shown to be a predictor of skill: the earlier you learn a language, the more likely you are to be able to master that language as if you were a native.
The researchers validated their measure by testing it in two different populations: healthy young bilinguals and older bilinguals with language problems. They compared their results with those obtained with existing methods that are based on the acquisition of much more extensive linguistic information. In both groups, the formulas produced language proficiency results that were almost identical to those generated by more complicated measures, demonstrating that the new approach is simple and accurate.
“Instead of simply labeling someone as ‘bilingual’ or ‘monolingual,’ this tool quantifies how multilingual they are,” Chen says.
“This calculator offers a transparent quantitative tool that researchers, clinicians and educators can adopt to better characterize multilingual populations and ultimately improve the quality of research and real-world applications, from language teaching to clinical evaluation,” 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 do not capture significant variation in proficiency or proficiency.
A: Quantifies multilingualism using age of acquisition and self-assessed listening, speaking, reading, and writing skills for each language.
A: Validation testing shows that the calculator produces dominance profiles comparable to traditional assessments, which require more time.
Editorial notes:
This article was edited by a Neuroscience News editor. Magazine article reviewed in its entirety. Additional context added by our staff.
About this news about research in neurotechnology and multilingualism
Author: James Devitt
Source: New York University
Contact: James Devitt – New York University
Image: Image is credited to Neuroscience News.
Original research: Open access.
“A theoretical and empirically supported calculation for linguistic dominance and the degree of multilingualism” by Esti Blanco-Elorrieta et al. Bilingualism Language and Cognition
Abstract
A theoretically and empirically based calculation of linguistic dominance and the degree of multilingualism
Research on bilingualism has long been challenged by the lack of a unified approach to quantifying linguistic dominance and the degree of multilingualism.
Although numerous questionnaires (e.g., LHQ, BLP, LEAP-Q, and LUQ) provide valuable data on language background variables, they lack a standardized formula for calculating key measures from them.
We present two formulas that synthesize critical linguistic variables to efficiently calculate language proficiency and a multilingualism score that ranges from perfect monolingualism to native-like proficiency in multiple languages.
Validation on two large data sets shows that our dominance measure aligns closely with more complex PCA methods while being simpler and more efficient.
