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  • Brief Communication
  • Published: 18 July 2022

An investigation across 45 languages and 12 language families reveals a universal language network

  • Saima Malik-Moraleda   ORCID: 1 , 2 , 3   na1 ,
  • Dima Ayyash 1 , 2   na1 ,
  • Jeanne Gallée   ORCID: 3 ,
  • Josef Affourtit 1 , 2 ,
  • Malte Hoffmann   ORCID: 4 , 5 ,
  • Zachary Mineroff 1 , 2 , 6 ,
  • Olessia Jouravlev 1 , 2 , 7 &
  • Evelina Fedorenko   ORCID: 1 , 2 , 3  

Nature Neuroscience volume  25 ,  pages 1014–1019 ( 2022 ) Cite this article

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  • Auditory system
  • Cognitive neuroscience
  • Functional magnetic resonance imaging

To understand the architecture of human language, it is critical to examine diverse languages; however, most cognitive neuroscience research has focused on only a handful of primarily Indo-European languages. Here we report an investigation of the fronto-temporo-parietal language network across 45 languages and establish the robustness to cross-linguistic variation of its topography and key functional properties, including left-lateralization, strong functional integration among its brain regions and functional selectivity for language processing.

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The data that support the findings of this study are available at .

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We thank Z. Fan, F. Frank and J. Vera-Rebollar for help with finding and recording the speakers; Z. Fan, J. Vera-Rebollar, F. Frank, A. Verkerk, the Max Planck Institute in Nijmegen, C. Kidd and M. Xiang for help with locating the texts of Alice in Wonderland in different languages; I. Blank, A. Paunov, B. Lipkin, D. Greve and B. Fischl for help with some of the analyses; J. McDermott for letting us use the sound booths in his laboratory for the recordings; J. Wu, N. Jhingan and B. Lipkin for creating a website for disseminating the localizer materials and script; M. Lewis for allowing us to use the linguistic family maps from the GeoCurrents website; B. A. Cabrera for help with figures; EvLab and TedLab members and collaborators; the audiences at the Neuroscience of Language Conference at NYU-AD (2019) and at the virtual Cognitive Neuroscience Society conference (2020) for helpful feedback; T. Gibson, D. Blasi, M. Seghier and two anonymous reviewers for comments on earlier drafts of the manuscript; Y. Diachek for collecting the data for the Russian speakers (used in Supplementary Fig. 4 ); J. Pryor and S. Lall for promoting this work when it was still at the early stages; and our participants. The authors would also like to acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT and the support team (S. Shannon and A. Takahashi). S.M.-M. was supported by la Caixa Fellowship LCF/BQ/AA17/11610043, a Friends of McGovern Fellowship and the Dingwall Foundation Fellowship. E.F. was supported by NIH awards R00-HD057522, R01-DC016607 and R01-DC-NIDCD and research funds from the Brain and Cognitive Sciences Department, the McGovern Institute for Brain Research and the Simons Center for the Social Brain.

Author information

These authors contributed equally: Saima Malik-Moraleda, Dima Ayyash.

Authors and Affiliations

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

Saima Malik-Moraleda, Dima Ayyash, Josef Affourtit, Zachary Mineroff, Olessia Jouravlev & Evelina Fedorenko

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA, USA

Saima Malik-Moraleda, Jeanne Gallée & Evelina Fedorenko

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

Malte Hoffmann

Department of Radiology, Harvard Medical School, Boston, MA, USA

Eberly Center, Carnegie Mellon University, Pittsburgh, PA, USA

Zachary Mineroff

Department of Cognitive Science, Carleton University, Ottawa, ON, Canada

Olessia Jouravlev

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Conceptualization, project administration and supervision: E.F. Methodology: S.M.-M., D.A., J.G. and E.F. Investigation (data collection): S.M.-M., D.A., J.G., J.A., Z.M. and O.J. Data curation: S.M.-M., D.A. and J.A. Formal analysis: S.M.-M. Validation: S.M.-M. and J.A. Visualization: S.M.-M. and M.H. Software: S.M.-M., D.A., J.A. and Z.M. Writing—original draft: S.M.-M., D.A. and E.F. Writing—review and editing: J.G., J.A., M.H., Z.M. and O.J.

Corresponding authors

Correspondence to Saima Malik-Moraleda or Evelina Fedorenko .

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The authors declare no competing interests.

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Nature Neuroscience thanks M. Florencia Assaneo, Narly Golestani and Mohamed Seghier for their contribution to the peer review of this work.

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Extended data

Extended data fig. 1 comparison of the individual activation maps for the sentences  >  nonwords contrast and the native-language  >  degraded-language contrast in the two native-english-speaking participants..

The two maps are voxel-wise (within the union of the language parcels) spatially correlated at r = 0.77 and r = 0.99 for participants 492 and 502, respectively (the correlations are Fisher-transformed). Across the full set of participants, the average Fisher-transformed spatial correlation between the maps for the Sentences  >  Nonwords contrast in English and the Native-language  >  Degraded-language contrast in the participant’s native language (again, constrained to the language parcels) is r = 0.88 (SD = 0.43) for the left hemisphere and 0.73 (SD = 0.38) for the right hemisphere. (Note that using the union of the language parcels rather than the whole brain is conservative for computing these correlations; including all the voxels would inflate the correlations due to the large difference in activation levels between voxels that fall within the language parcels vs. outside their boundaries. Instead, we are zooming in on the activation landscape within the frontal, temporal, and parietal areas that house the language network and showing that these landscapes are spatially similar between the two contrasts in their fine-grained activation patterns).

Extended Data Fig. 2 Activation maps for the Alice language localizer contrast ( Native-language  >  Degraded-languag e) in the right hemisphere of a sample participant for each language (see Fig. 1 for the maps from the left hemisphere).

A significance map was generated for each participant by FreeSurfer 44 ; each map was smoothed using a Gaussian kernel of 4 mm full-width half-max and thresholded at the 70 th percentile of the positive contrast for each participant (this was done separately for each hemisphere; note that the same participants are used here as those used in Fig. 1 ). The surface overlays were rendered on the 80% inflated white-gray matter boundary of the fsaverage template using FreeView/FreeSurfer. Opaque red and yellow correspond to the 80 th and 99 th percentile of positive-contrast activation for each subject, respectively. Further, here and in Fig. 1 , small and/or idiosyncratic bits of activation (relatively common in individual-level language maps for example, 9, 10 ) were removed. In particular, clusters were excluded if a) their surface area was below 100 mm^2, or b) they did not overlap (by > 10%) with a mask created for a large number (n = 804 56 ) participants by overlaying the individual maps and excluding vertices that did not show language responses in at least 5% of the cohort. (We ensured that the idiosyncrasies were individual- and not language-specific: for each cluster removed, we checked that a similar cluster was not present for the second native speaker of that language.) These maps were used solely for visualization; all the statistical analyses were performed on the data analyzed in the volume.

Extended Data Fig. 3 Volume-based activation maps for the Native-language  >  Degraded-language contrast in the left hemisphere of a sample participant for each language (the same participants are used as those used in Fig. 1 and Extended Fig. 2 ).

a) Binarized maps that were generated for each participant by selecting the top 10% most responsive (to this contrast) voxels within each language parcel. These sets of voxels correspond to the fROIs used in the analyses reported in Extended Data Fig. 4 (except for the estimation of the responses to the conditions of the Alice localizer, where a subset of the runs was used to ensure independence; the fROIs in those cases will be similar but not identical to those displayed). b) Whole-brain maps that are thresholded at the p < 0.001 uncorrected level.

Extended Data Fig. 4 Percent BOLD signal change across (panel a) and within each of (panel b) the LH language functional ROIs (defined by the Native-language  >  Degraded-language contrast from the Alice localizer, cf. the Sentences  >  Nonwords contrast from the English localizer as in the main text and analyses; Fig. 3a and Supplementary Fig. 3 ) for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the spatial working memory (WM) task and the math task.

The dots correspond to languages (n = 45), and the labels (panel a only) mark the averages for each language family. In all panels, box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. Across the six fROIs, the Native-language condition elicits a reliably greater response than both the Degraded-language condition (2.32 vs. 0.91 % BOLD signal change relative to the fixation baseline; t(44)=18.57, p < 0.001) and the Unfamiliar-language condition (2.32 vs. 0.99; t(44)=18.02, p < 0.001). Responses to the Native-language condition are also significantly higher than those to the spatial working memory task (2.32 vs. 0.06; t(44)=11.16, p < 0.001) and the math task (2.32 vs. −0.02; t(40)=20.8, p < 0.001). These results also hold for each fROI separately, correcting for the number of fROIs ( Native-language  >  Degraded-language : ps<0.05; Native-language  >  Unfamiliar-language : ps<0.05; Native-language  >  Spatial WM : ps<0.05; and Native-language  >  Math : ps<0.05). All t-tests were two-tailed and corrected for the number of fROIs in the per-fROI analyses.

Extended Data Fig. 5 Percent BOLD signal change across the LH language functional ROIs (defined by the Sentences  >  Nonwords contrast) for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the spatial working memory (WM) task, and the math task shown for each language separately.

The dots correspond to participants for each language (n = 2 in all languages except Slovene, Swahili, Tagalog, Telugu, where n = 1). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. (Note that the scale of the y-axis differs across languages in order to allow for easier between-condition comparisons in each language).

Extended Data Fig. 6 A comparison of individual LH topographies between speakers of the same language vs. between speakers of different languages.

The goal of this analysis was to test whether inter-language / inter-language-family similarities might be reflected in the similarity structure of the activation patterns. To perform this analysis, we computed a Dice coefficient 57 for each pair of individual activation maps for the Intact-language  >  Degraded-language contrast (a total of n = 3,655 pairs across the 86 participants). To do so, we used the binarized maps like those shown in Extended Data Fig. 3a , where in each LH language parcel the top 10% of most responsive voxels were selected. Then, for each pair of images, we divided the number of overlapping voxels multiplied by 2 by the sum of the voxels across the two images (this value was always the same and equaling 1,358 given that each map had the same number of selected voxels). The resulting values can vary from 0 (no overlapping voxels) to 1 (all voxels overlap). a) A comparison of Dice coefficients for pairs of maps between languages (left, n = 3,655 pairs) vs. within languages (right; this could be done for 41/45 languages for which two speakers were tested). If the activation landscapes are more similar within than between languages, then the Dice coefficients for the within-language comparisons should be higher. Instead, no reliable difference was observed by an independent-samples t-test (average within-language: 0.17 (SD = 0.07), average between-language: 0.16 (SD = 0.06); t(40.7)=−0.52, p = 0.61; see also Extended Data Fig. 8 for evidence that the range of overlap values in probabilistic atlases created from speakers of diverse languages vs. speakers of the same language are comparable). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. b) Dice coefficient values for all pairs of within- and between-language comparisons (the squares in black on the diagonal correspond to languages with only one speaker tested). As can be seen in the figure and in line with the results in panel a, no structure is discernible that would suggest greater within-language / within-language-family topographic similarity. Similar to the results from the within- vs. between-language comparison in a, the within-language-family vs. between-language-family comparison did not reveal a difference (t(19.8)=0.71, p = 0.49). In summary, in the current dataset (collected with the shallow sampling approach, that is, a small number of speakers from a larger number of languages), no clear similarity structure is apparent that would suggest more similar topographies among speakers of the same language, or among speakers of languages that belong to the same language family.

Extended Data Fig. 7 Inter-region functional correlations in the language and the Multiple Demand networks during story comprehension for each of the 45 languages.

Inter-region functional correlations for the LH and RH of the language and the Multiple Demand (MD) networks during a naturalistic cognition paradigm (story comprehension in the participant’s native language) shown for each language separately.

Extended Data Fig. 8 Comparison of three probabilistic overlap maps (atlases).

Comparison of three probabilistic overlap maps (atlases): a) the Alice atlas (n = 86 native speakers of 45 languages) created from the Native-language  >  Degraded-language maps; b) the English atlas (n = 629 native English speakers; this is a subset of the Fedorenko lab’s Language Atlas (LanA 56 ) created from the Sentences  >  Nonwords maps; and) the Russian Atlas (n = 19 native Russian speakers) created from the Native-language  >  Degraded-language maps for the Russian version of the Alice localizer. All three atlases were created by selecting for each participant the top 10% of voxels (across the brain) based on the t-values for the relevant contrast in each participant, binarizing these maps, and then overlaying them in the common space. In each atlas, the value in each voxel corresponds to the proportion of participants (between 0 and 1) for whom that voxel belongs to the 10% of most language-responsive voxels. The probabilistic landscapes are similar across the atlases: within the union of the language parcels (see Extended Data Fig. 1 caption for an explanation of why this approach is more conservative than performing the comparison across the brain), the Alice atlas is voxel-wise spatially correlated with both the English atlas (r = 0.83) and the Russian atlas (r = 0.85). Furthermore, the range of non-zero overlap values is comparable between the Alice atlas (0.1–0.87; average within the language parcels=0.08, median=0.05) and each of the other atlases (the English atlas: 0.002–0.79; average within the language parcels=0.07, median=0.03; the Russian atlas: 0.05–0.84; average within the language parcels=0.13, median=0.11). The latter result suggests that the inter-individual variability in the topographies of activation landscapes elicited in 86 participants of 45 diverse languages is comparable to the inter-individual variability observed among native speakers of the same language.

Extended Data Fig. 9 Responses in the domain-general Multiple Demand network to the conditions of the Alice localizer task, the spatial working memory task, and the math task.

Percent BOLD signal change across the domain-general Multiple Demand (MD) network 15 , 52 functional ROIs for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the hard and easy conditions of the spatial working memory (WM) task, and the hard and easy conditions of the math task. The dots correspond to languages (n = 45 except for the Math Task, where n = 41). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. As in the main analyses (Fig. 3c ), the individual MD fROIs were defined by the Hard  >  Easy contrast in the spatial WM task (see 54 for evidence that other Hard  >  Easy contrasts activate similar areas). As expected given past work e.g., 54 , the MD fROIs show strong responses to both the spatial WM task and the math task, with stronger responses to the harder condition in each (3.05 vs. 1.93 for the spatial WM task, t(44)=23.1, p < 0.001; and 1.68 vs. 0.62 for the math task, t(40)=8.87, p < 0.001). These robust responses in the MD network suggest that the lack of responses to the spatial WM and math tasks in the language areas can be meaningfully interpreted. Furthermore, in line with past work e.g. 58 , 59 , 60 , MD fROIs show a stronger response to the acoustically degraded condition than the native language condition (0.26 vs. -0.10, t(44)=4.92, p < 0.01), and to the unfamiliar language condition than the native language condition (0.15 vs. -0.10, t(44)=4.96, p < 0.01). All t-tests were two-tailed with no adjustment for multiple comparisons.

Extended Data Fig. 10 Comparison of the individual activation maps for the Native-language  >  Degraded-language contrast and the Native-language  >  Unfamiliar-language contrast in four sample participants.

The activation landscapes are broadly similar: across the full set of 86 participants, the average Fisher-transformed voxel-wise spatial correlation within the union of the language parcels between the maps for the two contrasts is r = 0.66 (SD = 0.40). (Note that this correlation is lower than the correlation between the Native-language  >  Degraded-language contrast and the Sentences  >  Nonwords contrast in English (see Extended Data Fig. 1 ). This difference may be due to the greater variability in the participants’ responses to an unfamiliar language.) Furthermore, across the language fROIs, the magnitudes of the Native-language  >  Degraded-language and the Native-language  >  Unfamiliar-language effects are similar (mean = 1.02, SD(across languages)=0.41 vs. mean=1.07, SD = 0.37, respectively; t(44)=1.15, p = 0.26).

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Malik-Moraleda, S., Ayyash, D., Gallée, J. et al. An investigation across 45 languages and 12 language families reveals a universal language network. Nat Neurosci 25 , 1014–1019 (2022).

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universal language research paper

The Hidden Bias of Science’s Universal Language

The vast majority of scientific papers today are published in English. What gets lost when other languages get left out?

universal language research paper

Newton’s Principia Mathematica was written in Latin; Einstein’s first influential papers were written in German; Marie Curie’s work was published in French. Yet today, most scientific research around the world is published in a single language, English.

Since the middle of the last century, things have shifted in the global scientific community. English is now so prevalent that in some non-English speaking countries, like Germany, France, and Spain, English-language academic papers outnumber publications in the country’s own language several times over. In the Netherlands, one of the more extreme examples, this ratio is an astonishing 40 to 1.

A 2012 study from the scientific-research publication Research Trends examined articles collected by SCOPUS, the world’s largest database for peer-reviewed journals. To qualify for inclusion in SCOPUS, a journal published in a language other than English must at the very least include English abstracts; of the more than 21,000 articles from 239 countries currently in the database, the study found that 80 percent were written entirely in English. Zeroing in on eight countries that produce a high number of scientific journals, the study also found that the ratio of English to non-English articles in the past few years had increased or remained stable in all but one.

This gulf between English and the other languages means that non-English articles, when they get written at all, may reach a more limited audience. On SCImago Journal Rank —a system that ranks scientific journals by prestige, based on the citations their articles receive elsewhere—all of the top 50 journals are published in English and originate from either the U.S. or the U.K.

In short, scientists who want to produce influential, globally recognized work most likely need to publish in English—which means they’ll also likely have to attend English-language conferences, read English-language papers, and have English-language discussions. In a 2005 case study of Korean scientists living in the U.K., the researcher Kumju Hwang, then at the University of Leeds, wrote: “The reason that [non-native English-speaking scientists] have to use English, at a cost of extra time and effort, is closely related to their continued efforts to be recognized as having internationally compatible quality and to gain the highest possible reputation.”

It wasn’t always this way. As the science historian Michael Gorin explained in Aeon earlier this year, from the 15th through the 17th century, scientists typically conducted their work in two languages: their native tongue when discussing their work in conversation, and Latin in their written work or when corresponding with scientists outside their home country.

“Since Latin was no specific nation’s native tongue, and scholars all across European and Arabic societies could make equal use of it, no one ‘owned’ the language. For these reasons, Latin became a fitting vehicle for claims about universal nature,” Gordin wrote. “But everyone in this conversation was polyglot, choosing the language to suit the audience. When writing to international chemists, Swedes used Latin; when conversing with mining engineers, they opted for Swedish.”

As the scientific revolution progressed through 17th and 18th centuries, Gordin continued, Latin began to fall out of favor as the scientific language of choice:

Galileo Galilei published his discovery of the moons of Jupiter in the Latin Sidereus Nuncius of 1610, but his later major works were in Italian. As he aimed for a more local audience for patronage and support, he switched languages. Newton’s Principia (1687) appeared in Latin, but his Opticks of 1704 was English (Latin translation 1706).

But as this shift made it more difficult for scientists to understand work done outside of their home countries, the scientific community began to slowly consolidate its languages again. By the early 19th century, just three—French, English, and German—accounted for the bulk of scientists’ communication and published research; by the second half of the 20th century, only English remained dominant as the U.S. strengthened its place in the world, and its influence in the global scientific community has continued to increase ever since.

As a consequence, the scientific vocabularies of many languages have failed to keep pace with new developments and discoveries. In many languages, the  words “quark” and “chromosome,” for example, are simply transliterated from English. In a 2007 paper, the University of Melbourne linguist Joe Lo Bianco described the phenomenon of “domain collapse,” or “the progressive deterioration of competence in [a language] in high-level discourses.” In other words, as a language stops adapting to changes in a given field, it can eventually cease to be an effective means of communication in certain contexts altogether.

In many countries, college-level science education is now conducted in English—partially because studying science in English is good preparation for a future scientific career, and partially because the necessary words often don’t exist in any other language. A 2014 report from the University of Oxford found that the use of English as the primary language of education in non-English speaking countries is on the rise, a phenomenon more prevalent in higher education but also increasingly present in primary and secondary schools.

But even with English-language science education around the world, non-native speakers are still often at a disadvantage.

“Processing the content of the lectures in a different language required a big energetic investment, and a whole lot more concentration than I am used to in my own language,” said Monseratt Lopez, a McGill University biophysicist originally from Mexico.

“I was also shy to communicate with researchers, from fear of not understanding quite well what they were saying,” she added. “Reading a research paper would take me a whole day or two as opposed to a couple of hours.”

Sean Perera, a researcher in science communication from the Australian National University, described the current situation this way: “The English language plays a dominant role, one could even call it a hegemony … As a consequence, minimal room or no room at all is allowed to communicators of other languages to participate in science in their own voice—they are compelled to translate their ideas into English.”

In practice, this attitude selects for only a very specific way of looking at the world, one that can make it easy to discount other types of information as nothing more than folklore. But knowledge that isn’t produced via traditional academic research methods can still have scientific value—indigenous tribes in Indonesia , for example, knew from their oral histories how to recognize the signs of an impeding earthquake, enabling them to flee to higher ground before the 2004 tsunami hit. Similarly, the Luritja people of central Australia have passed down an ancient legend of a deadly “fire devil” crashing from the sun to the Earth—which, geologists now believe, describes a meteorite that landed around 4,700 years ago.

“It is all part of a growing recognition that Indigenous knowledge has a lot to offer the scientific community,” the BBC wrote in an article describing the Luritja story. “But there is a problem—indigenous languages are dying off at an alarming rate, making it increasingly difficult for scientists and other experts to benefit from such knowledge.”

Science’s language bias, in other words, extends beyond what’s printed on the page of a research paper. As Perera explained it, so long as English remains the gatekeeper to scientific discourse, shoehorning scientists of other cultural backgrounds into a single language comes with “the great cost of losing their unique ways of communicating ideas.”

“They gradually lose their own voice,” he said—and over time, other ways of understanding the world can simply fade away.

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Sasha McDowell

The accessibility of English as the universal language of science

May 17, 2021

Whether it is avidly writing a grant proposal to attract funding or presenting your latest research, mastery of the English language is crucial for success in science. But have you ever wondered, why English ?

If the average modern scientist were to take a time machine back to the Middles Ages and the Renaissance, they might be stumped by the natural philosophy and natural history works of that time, which were almost all written in Latin. Because Latin did not belong to any one nation, it was accessible to scholars across Europe and to Arabic societies.

Make no mistake, these scholars were polyglot and chose the language based on the audience – Latin for international communications, and one’s native tongue for local relay. This system began to break down around the time of Galileo. He reported his discovery of Jupiter’s moons in Latin in 1610 but his later major contributions were published in Italian. The same holds true for Newton’s Principia, which was written in Latin in 1687, but Opticks was published in English in 1704. As a result, by the end of the 18th century there was a variety of languages used to communicate scientific findings.

This multiplicity of available languages proved inefficient in transmitting advances in scientific knowledge, which led to a compression of the scientific languages into English, French and German in the 19th century. German was the main contender in chemistry and perhaps I would be writing this article in German had it not been for the First World War. German intellects were shut out from new international institutions of Science set up in the early 1920s and German became outlawed in the U.S. around this time. The damage was done, leading to the collapse of German as a leading scientific language. By the 1980s, English dominated discourse in most international publications in the Natural Sciences, thus emerging as the scientific lingua franca we use today.

Dr. Alfredo Ferreira

Dr. Alfredo Ferreira

A universal language for universal truth? I corresponded with Dr. Alfredo Ferreira, a Lecturer in the Science stream of the UBC Vantage College Academic English Program, on the benefits and drawbacks of having English as the language of science. Interestingly, he said that, “Science aims for universally true claims about the Universe. With these aims, it helps considerably to use a common language. Science benefits from sharing not only the mode of communication but the values and perspectives that are encoded in the shared language.”

He suggested that, as with mathematics, language is used in communities of scientists to arrive at shared understandings, our best approximations to “truth.” Dr. Ferreira recounted Killam Award-winning Math lecturer, Dr. Fok Shuen Leung’s postulation that “mathematical claims, more than any other claims made at a university, hold (or don’t hold) regardless of whether people think so.” Dr. Ferreira agrees, recognizing the respective functions of mathematics, language, and other modes within the larger remit of science to identify, model, analyze, and interpret the universe using shared tools.

“Science is a collaborative activity,” he added, “and a common language facilitates the linking of established and new knowledge among scientists.” This is, of course, curiously true, as practice and history prove.

Dr. Ferreira believes the same qualities of language that benefit science also constrain it. “English reflects a relatively stable view of the world and organization of human social interaction,” he explained, and because English is intertwined with a history of dominance of some cultures over others, it implies that, “English-only science discourse is constrained in its world view.”

Although our view of “science” has Eurocentric roots, Indigenous Peoples and cultures have made their own discoveries that lack the same visibility and stature. Valuable oral histories and traditional knowledge are at risk of being lost in the scientific wilderness. This is problematic. Additionally, publishing scientific discoveries in non-English papers largely leads to work being ignored. One dire example is that of the deadly H5N1 strain of the avian flu which, though first published in a Chinese journal at the beginning of 2004, only received wider acknowledgement in August of that year.

“This is going to be your whole life” If the Covid-19 pandemic has taught us anything, it’s that we ought to be paying close attention to scientific developments around the world, no matter the language, cultures, or nations. Apart from the constraint English imposes, it also presents a significant barrier to non-native English speakers who realize that they ought to publish in English journals to have their research globally recognized. Dr. Ferreira agrees that, “At a very practical level, much of university-level science is difficult to access for much of the world’s population.” So how do we break down this language accessibility barrier?

To gain a better understanding of the difficulties non-native English speakers face in their scientific studies, I encouraged two Life Sciences Institute trainees to share their experiences. The first interviewee was a native Spanish speaker who only learned English at school. She recalled her first days in college in her home country when one of her professors gave the class an English paper and said, “This is going to be your whole life. Better start soon.”

Despite this early language-training, upon moving to Vancouver, she found difficulty in the first couple of months adjusting to both spoken and written English. “In the beginning it’s so frustrating. You may know the answer to a question but by the time you translate it, someone else has answered,” she recounted. “People don’t understand what you’re saying — sometimes because of the accent and sometimes because of the grammar.” She shared that she felt incompetent due to the inability to express herself, since she was limited by the number of words she knew at the time. She could not reach the level of complexity needed in discussions, but she would have easily been able to do so in her first language.

This trainee motivated herself to practice oral presentations, putting in extra time and effort to become more comfortable with English. This inevitably led her to greater fluency, but there were times when she felt as though she was a burden when asking colleagues and peers for help with English writing and pronunciation. “It would be wrong to say I haven’t received help, but it isn’t something that’s easy to deal with,” she said.

Exposure to English as the language of science not enough By comparison, another LSI trainee, whose first languages are Telugu and Hindi, was fortunately more proficient in English, having grown up in a household where it was regularly spoken and despite its lack of use at the public school which she attended. She completed her Bachelor’s degree in India, which was taught in English. “Oral presentations are okay for me, but I really struggle with the writing part,” she confessed. In India, she followed the writing conventions of British English and found difficulty adjusting to the North American use of the language. “I was taught to use passive English but here active English is encouraged,” she said. “I find myself having to reframe the way I write constantly after peers have viewed my writing.”

This highlights that, even with much exposure to English as “the language of science”, its technical aspects continue to be challenging. Furthermore, as a person who holds conversations in multiple languages, for example, Hindi and English, which are often combined to form Hinglish , she found that this would interfere with her flow of language when required to stick to one. Because she grew up speaking different languages, she can relate to multilingual undergraduates who struggle with English in the classes that she teaches as a teaching assistant. “These undergrads are intimidated by the native English speakers and so they participate less in class and that affects their participation grade as well as their comfort level in asking clarifying questions.”

She shared her concerns about docking these students’ marks based on grammar and vocabulary and suggested just giving feedback without penalty. “In many multilingual countries, if you don’t speak English, you’re often looked down upon and as instructors, I don’t know if we’re perpetuating that.”

The undergrad experience – “for me this course was totally self-study” This interview led me to enquire about undergraduate experiences with English language in science. I spoke with an undergraduate transfer student from China who grew up with Chinese as her main language. She began to strengthen her English whilst at her previous university in order to pass the International English Language Test. Despite passing this test, she still experiences many difficulties.

She had a course with a flipped classroom structure, which emphasized group learning rather than the traditional instructor-to-student one-way flow of information. She found that she could not follow the fast-paced discussions within her group, which consisted of mainly native English speakers. “Because there were many tasks to be completed during the lecture time, I did not want to slow everyone down,” she recalled. “For me, this course was totally self-study.”

The student referenced another difficulty where she had to write up a scientific methodology for a course. When researching previously published methods, she found it not only challenging to understand such niche protocols, but also hard to find the right words to describe others’ work without losing the accuracy. It was saddening to hear her experiences since she had decided to study abroad for the enhanced academic resources. In a subsequent personal correspondence, she admitted that, “Sometimes, I feel it is not easy to continue my interests in science due to my poor English.”

Being able to pass the test doesn’t mean students can thrive Based on my interviews, I realized that once students have passed UBC’s English Language Admission Standard, there seems to be a general assumption that they can thrive in UBC’s academic English context. Dr. Ferreira expanded on this, saying, “I believe standardized exams such as TOEFL and IELTS are not the best tools for identifying a student’s preparation for university study in English. While these kinds of exams do test for functions of language relevant to university science, they tend to miss the mark in relation to the kinds of wording and thinking that is valued in the practice of university science.”

He highlighted that the need to identify more valid measures of student preparation for university studies has been addressed to an important extent at UBC Vantage College. Students in this program take their content courses just like all UBC students do, but also receive enhanced, credit-bearing instruction in English as it is realized in the disciplines (Arts, Applied Science and Science) and their respective 1 st year courses.

Dr. Sandra Zappa Hollman

Dr. Sandra Zappa Hollman

I spoke with the Director of the Academic English Program at UBC Vantage College, Dr. Sandra Zappa-Hollman, to provide insight into the complexity of having English as the universal language of Science and what UBC can do to better support multilingual students. Dr. Zappa-Hollman reflected that, “UBC has grown over the years to include so many more students from around the world but we need to ask how we have prepared ourselves as an institution to be able to welcome and support those students throughout their degrees.”

She has launched a project on teaching multilingual undergraduate learners. The project will be based on a survey of people who teach courses for undergraduates that include multilingual learners. It will provide an institutional perspective on the beliefs and attitudes that instructors who work with multilingual students at UBC hold and how this predisposes them to work in certain ways. It will also bring to light how prepared, pedagogically-speaking, instructors perceive themselves to be for this. She revealed that there is a call in her field to embed a focus on language, literacy, and multimodality across the curriculum.

Aligning pedagogy with needs and abilities “At Vantage we align the pedagogy to meet the abilities and needs of all students,” she explained, continuing that, “We let them adopt a lens on language where they are focusing on what they want to communicate, how they want to do it and to whom. This allows them to accomplish the communication successfully.” One challenge to incorporating this mindset into pedagogies and curriculums is that language and literacy are left to people in the periphery and the attitude that, “I am not a language teacher, why should I even care?” is of concern.

Apart from adjusting the way we teach, I spoke with Dr. Ferreira and Dr. Zappa-Hollman about resources for non-native English speakers. Dr. Zappa-Hollman recalled that there was a time when UBC offered a coaching program via the English Language Institute. This program offered free tutoring and free classes on pronunciation and writing. They were very empowering for students since the coaching approach built on what students already knew and their specific context. The students I interviewed confirmed that they would have used this valuable program if it were available.

“Unfortunately, the funding for this program ended and one of the problems it faced, was that it wasn’t well advertised,” Dr. Zappa-Hollman relayed. This might be the case for the English language resources that UBC does have to offer, such as The Centre for Writing and Scholarly Communication’s resources, the UBC Chapman Learning Commons’ writing tools, the WriteAway program co-developed by UBC’s I.K Barber Learning Centre, the Science Peer Academic Coaches, Speaking and Writing Workshops offered through Graduate and Post-doctoral Studies, and maybe more I might have missed.

Our conversation ended with Dr. Ferreira’s self-described “far-out ideas.” He suggested having an adjunct English language course attached to certain science courses. This might look like a simple one-credit English language course that could be applied to either undergraduate or graduate science courses, or both. Dr. Ferreira also proposed that international multilingual graduate students could provide a crucial link between research done in their home countries and that being done here at UBC. They could, for example, re-contextualize research that has been done in their first languages into English. This is all to say, as Dr. Zappa-Hollman put it, “we need continued investment from the university but first, an acknowledgment of the issues.”

Story by: Sasha McDowell, PhD. Cand. (Zoology) – I extend sincere thanks to and acknowledge the precious time and effort on the part of all participants to the interviews, which made this piece possible.

Sources utilized for this story that may be useful for further reading: 1) Gordin, M. D. (2021, May 3). How did science come to speak only English? – Michael D Gordin: Aeon Essays. Aeon. 2) Huttner-Koros, A. (2015, September 14). Why Science’s Universal Language Is a Problem for Research . The Atlantic. 3) Panko, B. (2017, January 2). English Is the Language of Science. That Isn’t Always a Good Thing .

Featured interviewees:

Dr. Sandra Zappa Hollman PhD and M.A. in Teaching English as a Second Language Director, Academic English Program, UBC Vantage College

Dr. Zappa-Hollman has worked extensively with English language learners (ELLs), teacher’s education programs and administratively to develop academic writing courses. Dr. Zappa-Hollman’s latest research focuses on the use of functional grammar approaches to support the academic English literacy development of ELLs across diverse disciplinary fields.

Dr. Alfredo Ferriera PhD in Language and Literacy Education and M.A. in Applied Linguistics Lecturer, Science stream of AEP UBC Vantage College

Dr. Ferriera’s research in educational linguistics focuses on the development of apprentice scholars’ capacities for varying levels of abstraction when they re-contextualize disciplinary knowledge. This approach encourages explicit instruction of the links between rhetorical aims, wording, and knowledge construction in and across academic disciplines.

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Education About Asia: Online Archives

In search of a universal language: past, present, and future.

Ever since the Tower of Babel, humans have pursued developing a universal language to use to communicate with more—ideally all— people. However, they have been only marginally successful, as indicated by both the history of a large number of failed efforts and the current situation.

Also, these efforts have their detractors. A language becomes larger when it weakens or replaces another language. This often involves “language genocide” and/or represents “language imperialism.” Attaining a universal language may be this on a grand scale.

In fact, many advocates of expanding the use of their language (and their culture, which is connected) believe it is superior to others. Many do not care if they render another language or languages extinct.

Currently, of the approximately 7,000 languages in the world, many are disappearing. According to National Geographic magazine, one becomes extinct every two weeks. 1 Most experts anticipate half will be gone by the end of the century. Some say 90 percent.

photo of a teacher and a young student writing on a blackboard

In any case, several centuries ago, Latin, originally the language of Italy, became the universal language of Europe and modern science. It spread and flourished based on the military, commercial, political, and cultural power of the Roman Empire. The Catholic Church preserved its role after the fall of the empire, though its universal status declined, and eventually Latin fell into disuse.

Before and during the seventeenth century, Galileo Galilei, Isaac Newton, and others began to write in their native languages, not only to give their works broader and more popular appeal, but also to express support for the Protestant Reformation. In addition, they reflected the nationalist sentiments of the time. However, some European scholars still worried that having no single universal language impeded scientific research and progress. With nothing promising in sight, they became multilingual, using English, French, and German. That worked to some degree.

On the other side of the world, in Asia, scientific research was done primarily in a single language: Classical Chinese. At least, it was the universal language in some of that part of the world in its written form; in its spoken form (written Chinese is not phonetic), it was not.

Chinese was also to some degree the language of business and commerce in East Asia, but it waxed in importance only when China prospered and engaged meaningfully in trade. Its usage waned beginning in the fifteenth century with China’s isolationism and eventual decline. That continued until modern times.

In the West, German lost its popularity with World War I and also after the war, when many of its top scientists moved to the US due to the rise of Nazism. Both German and French declined markedly after World War II.

Meanwhile, in the late 1800s, there was an effort to construct a truly universal language: Esperanto. Esperanto was a constructed language intended to be easy to learn and also politically neutral. For some, it would transcend nationality and politics, and contribute to world peace. However, due to the fact that it did not have a territorial, cultural, or economic base, it was not a great success. Today, its number of speakers worldwide is estimated to be only a hundred thousand to two million at most. Little is written in Esperanto.

In the eighteenth and nineteenth centuries, the British Empire greatly expanded the use of English in commerce and its number of speakers. After World War II, English became the language of science, as well as business, politics, and culture. The dominance of the United States in these three areas ensured that this would remain so. Until the last decade, nearly 98 percent of published scientific articles were written in English, and English was the undisputed language of global trade, culture, travel, and more.

The rise of Asia, in particular China, in the last three-plus decades has enabled Mandarin Chinese to compete as a global language. It has one big advantage: there are three times more native speakers of Mandarin than English (and would-be competitors such as Spanish and Arabic)—noting, of course, that most Chinese also speak a dialect or another version of Chinese, and some don’t speak Mandarin well.

In the last few years, Chinese government officials have justified saying Chinese is a language of science due to China registering more patents and producing more scientific articles than the United States, though their quality is not yet as good. In addition, China is increasing its spending on research and development annually by nearly 20 percent, while the US and Europe barely add 3 percent.

Furthermore, Chinese leaders, including top foreign ministry officials, say emphatically that Chinese should be considered a contender as an important business language. China has been growing economically around four times as fast as the US, has become the world’s largest manufacturer and trading nation, is number one in the world in foreign exchange (while the US has become a huge debtor), and is the largest purveyor of foreign aid and foreign investments.

Adding to the argument for Chinese, China has worked with Japan (the world’s third-largest economy) and South Korea (a major contributor to research in information and communications technology) to standardize the use of Chinese characters in law, commerce, and to some extent science. Meanwhile, a number of countries in Asia and elsewhere have put studying Chinese on a fast track in their universities and business training institutions. English has been demoted in importance.

As a matter of record, the number of people studying Chinese worldwide is double those learning Spanish or German and tenfold those taking Japanese. The government of China announced two years ago that forty million foreigners are studying Chinese; the number has increased exponentially since then. In recent years, China has put a large amount of money and resources into encouraging Chinese-language study—financing Confucian institutes and providing funds for Mandarin Chinese-language teachers in other countries.

Spanish, and recently, Arabic are popular in the US and European colleges and universities. However, they are essentially regional languages and are not used much in the sciences or technology, and they do not compete with English or Chinese for global status.

Hindi is one of the world’s largest languages in number of speakers. Also, India is doing well economically and is making impressive strides in science and technology. But Hindi is not spoken in all of India and is neither spoken nor used very much in other countries.

Which language then, English or Chinese, will come out on top appears to depend on whether or not China’s economic boom falters and/or whether the US (and Europe) can get their economies back on track. For now, there are two contending global languages.

It may be some time before there is a prevailing or universal language. In the interim, knowing both English and Mandarin Chinese makes it possible to communicate with around half the people in the world, which one may say is quite a feat in terms of achieving that elusive international tongue—if one believes that having a universal language is a good idea. ■

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NOTES 1. Russ Rymer, “Vanishing Languages,” National Geographic, July 2012, http://tinyurl. com/73436xn.

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  • Mol Biol Cell
  • v.23(8); 2012 Apr 15

English as the universal language of science: opportunities and challenges

English is now used almost exclusively as the language of science. The adoption of a de facto universal language of science has had an extraordinary effect on scientific communication: by learning a single language, scientists around the world gain access to the vast scientific literature and can communicate with other scientists anywhere in the world. However, the use of English as the universal scientific language creates distinct challenges for those who are not native speakers of English. In this editorial, we discuss how researchers, manuscript reviewers, and journal editors can help minimize these challenges, thereby leveling the playing field and fostering international scientific communication.

It is estimated that less than 15% of the world's population speaks English, with just 5% being native speakers ( ). This extraordinary imbalance emphasizes the importance of recognizing and alleviating the difficulties faced by nonnative speakers of English if we are to have a truly global community of scientists. For scientists whose first language is not English, writing manuscripts and grants, preparing oral presentations, and communicating directly with other scientists in English is much more challenging than it is for native speakers of English. Communicating subtle nuances, which can be done easily in one's native tongue, becomes difficult or impossible. A common complaint of nonnative speakers of English is that manuscript reviewers often focus on criticizing their English, rather than looking beyond the language to evaluate the scientific results and logic of a manuscript. This makes it difficult for their manuscripts to get a fair review and, ultimately, to be accepted for publication.

We believe that the communications advantage realized by native speakers of English obligates them to acknowledge and to help alleviate the extra challenges faced by their fellow scientists from non-English-speaking countries. Native speakers of English should offer understanding, patience, and assistance when reviewing or editing manuscripts of nonnative speakers of English. At the same time, nonnative speakers of English must endeavor to produce manuscripts that are clearly written. We offer the following guidelines for writing and evaluating manuscripts in the context of the international community of scientists:

  • Nonnative speakers of English can write effective manuscripts, despite errors of grammar, syntax, and usage, if the manuscripts are clear, simple, logical, and concise. (We note that native speakers of English sometimes write manuscripts exhibiting good grammar, yet filled with muddled and confusing logic.)
  • When possible, reviewers and editors of manuscripts should look beyond errors in grammar, syntax, and usage, and evaluate the science.
  • It is inappropriate to reject or harshly criticize manuscripts from nonnative speakers of English based on errors of grammar, syntax, or usage alone. If there are language errors, reviewers and editors should provide constructive criticism, pointing out examples of passages that are unclear and suggesting improvements. Reviewers and editors may also suggest that authors seek the assistance of expert English speakers or professional editing services in preparing revised versions of manuscripts. And finally, all involved should bear in mind that most journals employ copyeditors, whose job it is to correct any lingering errors in grammar, syntax, and usage before final publication of an article.
  • Nonnative speakers of English must be aware that reviewers, editors, and journal staff do not have the time or resources to extensively edit manuscripts for language and that reviewers and editors must be able to understand what is being reported. Thus, it is essential that nonnative speakers of English recognize that their ability to participate in the international scientific enterprise is directly related to their ability to produce manuscripts in English that are clear, simple, logical, and concise.

The fact that English is the de facto global language of science is not likely to change anytime soon. Optimizing communication among members of the international community of scientists, and thus advancing scientific progress, depends on elimination of obstacles faced by nonnative speakers of the English language. This ideal can best be achieved when all members of the scientific community work together.


This editorial was inspired by correspondence with Victor Norris of the Université de Rouen, France. We thank Yi Zuo, Karsten Weis, and Laurent Blanchoin for comments on the manuscript and Mark Leader for his excellent edits.

DOI: 10.1091/mbc.E12-02-0108


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