ECHNICal co ○MMEN statistics were distorted by WALS's data fication, which truncated the high ends Comment on Phonemic Diversity scales(2) For example, WALS binned the vowel qual- ity inventories into three groups: small (2 to 4 Supports a Serial Founder Effect Model qualities, medium (s to qualitis, and lage of Language Expansion from Africa ity inventory varies from 2 to 20 and is distrib- Ited unequally among the geographic regions (4). Most large vowel quality inventories appear in Eurasia, whereas only small inventories can be found in the americas and australia. The Ger- Atkinson(Reports, 15 April 2011, p. 346)reported a declined trend of phonemic diversity manic languages and the wu Chinese dialects from Africa that indicated the african exodus of modern e his claim was only have the largest vowel quality inventories in the upported when the phonemic diversities were binned into Analyses using world, mostly larger than 10-for example, the raw data without simplification suggest a decline from central Asia from africa Standard Swedish has at least 16 vowel quali- ties, and the Donda Wu spoken in southem A kinson()analyzed the phoneme num- simplified(the exact counts of vow Shanghai has 20 vowel qualities. In contrast, few g bers of 504 languages around the world tones, and consonants ) Languages fron languages from Africa have more than 10 vowel O and found a strong inverse relationship show higher diversities of vowel qualities qualities(Fig. IB). Therefore, a lower limit of between the phonemic diversity and distance from (Fig. 1B). Therefore, we argued that seven qualities for large inventory in WALS's an inferred origin in Africa, which supports an African origin of modem languages. Although statistically significant declined trend of phonen Vowel quality Vowel quality diversity from Africa can be observed from the inventory nalyses of his normalized data set, his conclu- sion was questionable because of the simplifica tion of the phoneme inventories. The simplified data used in Atkinson's analy- ses were obtained directly from the World Atlas of Language Structures(WALS)(2), where the 5o品E8o phoneme numbers of the languages were simply kind of simplification of the data lost most in- <.*:%Tone inventory Tone inventory ormation of the phonemic diversity and might have resulted in bias conclusion. For example, to more than 80 among the world languages (3), while only five levels were counted in Atkinson's 503sE9oS We collected a new data set of world pho- nemic diversity, including 579 languages from 95 linguistic families(table S1). The phoneme Consonant ventories were displayed without any simplifi- inventory cation. To balance among the linguistic families we excluded 69 samples of some well-studied lin- guistic families(ie, Indo-European, Austronesia and Sino-Tibetan) from our analyses. This made our data comparable to Atkinsons(table S2) Our analyses were based on the remaining 510 languages. WALS maps Total Phoneme tkinson's normalized data set, the diversities of Total Phoneme Diversity vowel quality, tone, and consonant(Fig. 1A)all exhibit significant declines from Africa to the rest of the world. however. the declines from africa will not be that pronounced when the data are ne fe scences, ya and Lite Fudan University. hai 200433 China ic distribution of the phonemic diversities of the worlds languages. (A) Simplified *To whom correspondence should be addressed. E-mail: ies used by WALS (B) Exact phoneme inventory counts and the corresponding total lhui.fudan@gmail.com www.sciencemag.orgScieNceVol33510FebrUary2012 657
Comment on “Phonemic Diversity Supports a Serial Founder Effect Model of Language Expansion from Africa” Chuan-Chao Wang, Qi-Liang Ding, Huan Tao, Hui Li* Atkinson (Reports, 15 April 2011, p. 346) reported a declined trend of phonemic diversity from Africa that indicated the African exodus of modern languages. However, his claim was only supported when the phonemic diversities were binned into three or five levels. Analyses using raw data without simplification suggest a decline from central Asia rather than from Africa. Atkinson (1) analyzed the phoneme numbers of 504 languages around the world and found a strong inverse relationship between the phonemic diversity and distance from an inferred origin in Africa, which supports an African origin of modern languages. Although a statistically significant declined trend of phonemic diversity from Africa can be observed from the analyses of his normalized data set, his conclusion was questionable because of the simplification of the phoneme inventories. The simplified data used in Atkinson’s analyses were obtained directly from the World Atlas of Language Structures (WALS) (2), where the phoneme numbers of the languages were simply binned into three or five levels. However, this kind of simplification of the data lost most information of the phonemic diversity and might have resulted in bias conclusion. For example, the consonant inventory varies from less than 10 to more than 80 among the world languages (3), while only five levels were counted in Atkinson’s analyses. We collected a new data set of world phonemic diversity, including 579 languages from 95 linguistic families (table S1). The phoneme inventories were displayed without any simplification. To balance among the linguistic families, we excluded 69 samples of some well-studied linguistic families (i.e., Indo-European, Austronesian, and Sino-Tibetan) from our analyses. This made our data comparable to Atkinson’s (table S2). Our analyses were based on the remaining 510 languages. Judged from the original WALS maps and Atkinson’s normalized data set, the diversities of vowel quality, tone, and consonant (Fig. 1A) all exhibit significant declines from Africa to the rest of the world. However, the declines from Africa will not be that pronounced when the data are not simplified (the exact counts of vowel qualities, tones, and consonants). Languages from Eurasia show higher diversities of vowel qualities and tones (Fig. 1B). Therefore, we argued that Atkinson’s statistics were distorted by WALS’s data simplification, which truncated the high ends of the scales (2). For example, WALS binned the vowel quality inventories into three groups: small (2 to 4 qualities), medium (5 to 6 qualities), and large (7 to 14 qualities). Actually, the basic vowel quality inventory varies from 2 to 20 and is distributed unequally among the geographic regions (4). Most large vowel quality inventories appear in Eurasia, whereas only small inventories can be found in the Americas and Australia. The Germanic languages and the Wu Chinese dialects have the largest vowel quality inventories in the world, mostly larger than 10—for example, the Standard Swedish has at least 16 vowel qualities, and the Dônđäc Wu spoken in southern Shanghai has 20 vowel qualities. In contrast, few languages from Africa have more than 10 vowel qualities (Fig. 1B). Therefore, a lower limit of seven qualities for large inventory in WALS’s TECHNICALCOMMENT Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, and Department of Chinese Language and Literature, Fudan University, Shanghai 200433 China. *To whom correspondence should be addressed. E-mail: lihui.fudan@gmail.com Fig. 1. Geographic distribution of the phonemic diversities of the world’s languages. (A) Simplified phonemic diversities used by WALS. (B) Exact phoneme inventory counts and the corresponding total phoneme diversity. www.sciencemag.org SCIENCE VOL 335 10 FEBRUARY 2012 657-c on February 9, 2012 www.sciencemag.org Downloaded from
TECHNICAL COMMENT A B at)( Pearson correlation, r=-0.4413)or r=0.5127 r=-0,4386 -0.4391)as the origin than choosing P=1.56×10 P=2.18×103 -0.4386)(Fig 2B). Moreover, when using mean diversity across language families, central Asia (=-0.5503, P=5.08x 10)ex- hibited even stronger comelation than Europe(r 05∵∴…“.; 0.4945,P=354×10) or africa(r=-050 “"; vever. when the wals simplification was applied to our data set, the 001000015000200002500030000 300 10000 15000 20000 25000 30000 strongest negative correlation was still found be- Distance from Africa( Km) Distance from Africa(Km) tween the diversity and distance from Africa r=0.4697 r=-04391 To further test the robustness of these find- P=2.4D10 P=1.88×1025 ings, we repeated regressions after controlling for modern speaker population size (6). Africa never exhibited strongest correlation unless the data were simplified(table $3). Because there are some clear outliers among the sampled lan- guages, we applied a robust linear model to min- s x…“·一 imize the possible influence of outliers(7). In this model, the best-fit orgin also tumed from a 001000015000200002500030000 0o 10000 15000 20000 25000 30000 Africa to Asia when the data was not simplified Distance from Distance from Europe (Km) Thus. we demonstrated that WALs's data r=0.4636 -0.4413 simplification has distorted Atkinson's results. Ap- P=1.56×10 P=1.02×1025 = be more reliable. Therefore Asia(where the Babel o :… was supposed to be ) might be a more appropriate best-fit origin for modem languages if modem 0“”1 : languages have a common origin. References and Notes 1. Q D. Atkinson, Science 332, 346(2011). 2. I. Maddieson, in The World Atlas of language Structures 20000 nline, M.S. Dryer, M. Haspelmath, Eds. ( Max Distance from Central Asia( Km) DistancefromCentral Asia(Km) igital Library, Munich, 2011): chaps. 1, 2, and 13. 3. P. Ladefoged, L Maddieson, The Sounds of the Worid's Correlation between the distance from the best-fit origin of the languages and total phoneme Red lines are the fitted regression lines. Pearson correlation r and P values are shown in the 4. H Li, Commun. Contemp. Anthropol. 2, 42(2008) right. (A) Estimated from simplified phoneme inventories. B)Estimated from exact phoneme L0.4236/coca.2008.21007:http://comonca.orgcn/abs/ 5. Intemational Phonetic Association, Handbook of the 503sE9oS International Phonetic Association(Cambridge data set eliminated the difference about the vowel of tone diversity between the two continents is diversity levels between the African and Eur- also screened in WALS. Using the raw data of Online1,470005) asian languages. Besides the curtness in the all phonemes of the 510 languages without 7. W N Venables, B D. Ripley, Modem Applied Statistics wel inventory counts, Atkinsons analysis ig- simplification, we analyzed the total phoneme with S (Springer, New York, 200 nored all other phonetic features of the vowels, diversity (table S1). The highest diversity is de- such as nasalization, diphthong, and length, monstrably in Asia( Fig. 1B), and the top three Acknowledgments: this work was supported by shanghai which vary tremendously among the world's languages are Dondac (3.91), Kam(2.87), and (11204)and Shanghai Professional Development Funding (2010001). Y. Hu helped with some of the statistics Similar problems happened to the simplifi- We further redid the correlatio cation of tone diversity. Most tonal languages in tween the total phoneme diversity Suprofen rting Online Material Africa have less than four tones whereas most from the "best-fit origin "Different tonal languages in Asia have more than four. chosen as potential best-fit origins References The Kam spoken in southwest China has the ly, stronger negative correlation was observed 3 May 2011; accepted 3 January 2012 largest tone inventory(15 tones). The difference when choosing central Asia( the exact locus was 10.1126/science.1207846 10FebrUarY2012Vol335scIencEwww.sciencemag.org
data set eliminated the difference about the vowel diversity levels between the African and Eurasian languages. Besides the curtness in the vowel inventory counts, Atkinson’s analysis ignored all other phonetic features of the vowels, such as nasalization, diphthong, and length, which vary tremendously among the world’s languages (5). Similar problems happened to the simplification of tone diversity. Most tonal languages in Africa have less than four tones, whereas most tonal languages in Asia have more than four. The Kam spoken in southwest China has the largest tone inventory (15 tones). The difference of tone diversity between the two continents is also screened in WALS. Using the raw data of all phonemes of the 510 languages without simplification, we analyzed the total phoneme diversity (table S1). The highest diversity is demonstrably in Asia (Fig. 1B), and the top three languages are Dônđäc (3.91), Kam (2.87), and Buyang (2.49). We further redid the correlation analyses between the total phoneme diversity and distance from the “best-fit origin.” Different regions were chosen as potential best-fit origins. Interestingly, stronger negative correlation was observed when choosing central Asia (the exact locus was Ashgabat) (Pearson correlation, r = –0.4413) or Europe (r = –0.4391) as the origin than choosing Africa (r = –0.4386) (Fig. 2B). Moreover, when using mean diversity across language families, central Asia (r = –0.5503, P = 5.08 × 10−5 ) exhibited even stronger correlation than Europe (r = –0.4945, P = 3.54 × 10−4 ) or Africa (r = –0.5053, P = 2.49 × 10−4 ). However, when the WALS simplification was applied to our data set, the strongest negative correlation was still found between the diversity and distance from Africa (Fig. 2A). To further test the robustness of these findings, we repeated regressions after controlling for modern speaker population size (6). Africa never exhibited strongest correlation unless the data were simplified (table S3). Because there are some clear outliers among the sampled languages, we applied a robust linear model to minimize the possible influence of outliers (7). In this model, the best-fit origin also turned from Africa to Asia when the data was not simplified (table S4). Thus, we demonstrated that WALS’s data simplification has distorted Atkinson’s results. Apparently, the results without simplification should be more reliable. Therefore, Asia (where the Babel was supposed to be) might be a more appropriate best-fit origin for modern languages if modern languages have a common origin. References and Notes 1. Q. D. Atkinson, Science 332, 346 (2011). 2. I. Maddieson, in The World Atlas of Language Structures Online, M. S. Dryer, M. Haspelmath, Eds. (Max Planck Digital Library, Munich, 2011); chaps. 1, 2, and 13. 3. P. Ladefoged, I. Maddieson, The Sounds of the World's Languages (Blackwell, Oxford, 1996). 4. H. Li, Commun. Contemp. Anthropol. 2, 42 (2008), 10.4236/coca.2008.21007; http://comonca.org.cn/Abs/ 2008/007.htm. 5. International Phonetic Association, Handbook of the International Phonetic Association (Cambridge Univ. Press, Cambridge, 1999). 6. L. Excoffier, G. Laval, S. Schneider, Evol. Bioinform. Online 1, 47 (2005). 7. W. N. Venables, B. D. Ripley, Modern Applied Statistics with S (Springer, New York, 2002). Acknowledgments: This work was supported by Shanghai Commission of Education Research Innovation Key Project (11zz04) and Shanghai Professional Development Funding (2010001). Y. Hu helped with some of the statistics. Supporting Online Material www.sciencemag.org/cgi/content/full/335/6069/657-c/DC1 Tables S1 to S4 References 3 May 2011; accepted 3 January 2012 10.1126/science.1207846 Fig. 2. Correlation between the distance from the best-fit origin of the languages and total phoneme diversity. Red lines are the fitted regression lines. Pearson correlation r and P values are shown in the upper right. (A) Estimated from simplified phoneme inventories. (B) Estimated from exact phoneme inventory counts. 657-c 10 FEBRUARY 2012 VOL 335 SCIENCE www.sciencemag.org TECHNICAL COMMENT on February 9, 2012 www.sciencemag.org Downloaded from
