《汉语与认知》课程教学资源（阅读文献）认知语言学扩展阅读文献：Comment on “Phonemic Diversity Supports a Serial Founder Effect Model of Language Expansion from Africa”

TECHNICALCOMMENT statistics were distorted by WALS's data simpli- fication,which truncated the high ends of the 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 (5 to 6 qualities),and large (7 to 14 qualities).Actually,the basic vowel qual- of Language Expansion from Africa" ity inventory varies from 2 to 20 and is distrib- uted unequally among the geographic regions (4).Most large vowel quality inventories appear Chuan-Chao Wang,Qi-Liang Ding,Huan Tao,Hui Li* 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 languages.However,his claim was only have the largest vowel quality inventories in the supported when the phonemic diversities were binned into three or five levels.Analyses using world,mostly larger than 10-for example,the raw data without simplification suggest a decline from central Asia rather than from Africa. Standard Swedish has at least 16 vowel quali- ties,and the Dondac Wu spoken in southem tkinson (/analyzed the phoneme num- simplified (the exact counts of vowel qualities, Shanghai has 20 vowel qualities.In contrast,few bers of 504 languages around the world tones,and consonants).Languages from Eurasia languages from Africa have more than 10 vowel and found a strong inverse relationship show higher diversities of vowel qualities and tones qualities (Fig.1B).Therefore,a lower limit of between the phonemic diversity and distance from (Fig.1B).Therefore,we argued that Atkinson's seven qualities for large inventory in WALS's an inferred origin in Africa,which supports an African origin of modem languages.Although a statistically significant declined trend of phonemic Vowel quality Vowel quality KJeniqey diversity from Africa can be observed from the inventory inventory analyses 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 phoneme numbers of the languages were simply binned into three or five levels.However,this kind of simplification of the data lost most in- Tone inventory Tone inventory formation 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 pho- nemic diversity,including 579 languages from 95 linguistic families (table S1).The phoneme in- Consonant Consonant ventories were displayed without any simplifi- inventory inventory cation.To balance among the linguistic families we excluded 69 samples of some well-studied lin- guistic 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 Total Phoneme Total Phoneme Atkinson's normalized data set,the diversities of Diversity 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 not Ministry of Education Key Laboratory of Contemporary Anthropology,School of Life Sciences,and Department of Chinese Language and Literature,Fudan University, Shanghai 200433 China. Fig.1.Geographic distribution of the phonemic diversities of the world's languages.(A)Simplified *To whom correspondence should be addressed.E-mail: phonemic diversities used by WALS.(B)Exact phoneme inventory counts and the corresponding total lihui.fudan@gmail.com phoneme diversity. www.sciencemag.org SCIENCE VOL 335 10 FEBRUARY 2012 657-c

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 num￾bers 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 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 phoneme numbers of the languages were simply binned into three or five levels. However, this kind of simplification of the data lost most in￾formation 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 pho￾nemic diversity, including 579 languages from 95 linguistic families (table S1). The phoneme in￾ventories were displayed without any simplifi￾cation. To balance among the linguistic families, we excluded 69 samples of some well-studied lin￾guistic 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 simpli￾fication, which truncated the high ends of the scales (2). For example, WALS binned the vowel qual￾ity inventories into three groups: small (2 to 4 qualities), medium (5 to 6 qualities), and large (7 to 14 qualities). Actually, the basic vowel qual￾ity inventory varies from 2 to 20 and is distrib￾uted 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￾manic 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 quali￾ties, 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 Ashgabat)(Pearson correlation,r=-0.4413)or 15 =-0.5127 r=-0.4386 Europe (r=-0.4391)as the origin than choosing P=1.56×10 P=2.1810 Africa (r=-0.4386)(Fig.2B).Moreover,when 2 using mean diversity across language families, central Asia (r=-0.5503.P=5.08 x 10)ex- hibited even stronger correlation than Europe (r= 05 -0.4945,P=3.54×10 )or Africa(r=-0.5053 P=2.49 x 10).However.when the WALS 线参细参年中 -15 simplification was applied to our data set,the 5000 100001500020000 25000 30000 5000 100001500020000 25000 30000 strongest negative correlation was still found be- Distance from Africa (Km) Distance from Africa(Km) tween the diversity and distance from Africa (Fig.2A). To further test the robustness of these find- r=-0.4697 =-0.4391 15 P=2,40x10s P1.88105 ings,we repeated regressions after controlling for modem speaker population size (6).Africa 2 never exhibited strongest correlation unless the data were simplified (table S3).Because there are some clear outliers among the sampled lan- 0.5 guages,we applied a robust linear model to min- imize the possible influence of outliers (7).In 年带e华 15 this model.the best-fit origin also tumed from o 0 5000 10000 1500020000 25000 30000 5000 10000 15000 20000 25000 30000 Africa to Asia when the data was not simplified Distance from Europe(Km) Distance from Europe (Km) (table S4). Thus,we demonstrated that WALS's data 2 =-0.4636 r=-0.4413 simplification has distorted Atkinson's results.Ap- 1.5 年量。绿金 P=1.56x10m P=1.02x105 parently,the results without simplification should 1 be more reliable.Therefore.Asia (where the Babel uo 0.5 was supposed to be)might be a more appropriate 6J0 best-fit origin for modem languages if modem languages have a common origin. -0.5 810 References and Notes 1 年卡●参非修带◆号 1.Q.D.Atkinson,Science 332,346 (2011) bewaouelos' 2.I.Maddieson,in The World Atlos of Language Structures 15 5000 10000 Online,M.5.Dryer,M.Haspelmath,Eds.(Max Planck 15000 20000 5000 10000 15000 20000 Distance from Central Asia(Km) Distance from Central Asia(Km) Digital Library,Munich,2011);chaps.1,2,and 13. 3.P.Ladefoged,I.Maddieson,The Sounds of the World's Fig.2.Correlation between the distance from the best-fit origin of the languages and total phoneme Longuages (Blackwell,Oxford,1996). diversity.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), 'MM woy upper right.(A)Estimated from simplified phoneme inventories.(B)Estimated from exact phoneme 10.4236/coca.2008.21007;http://comonca.org.cn/Abs/ 20D8/007htm inventory counts. 5.International Phonetic Association,Handbook of the International Phonetic Association (Cambridge data set eliminated the difference about the vowel of tone diversity between the two continents is Univ.Press,Cambridge,1999). diversity levels between the African and Eur- also screened in WALS.Using the raw data of 6.L.Excoffier,G.Laval,S.Schneider,EvoL Bioinform. 0nine1,472005). asian languages.Besides the curtness in the all phonemes of the 510 languages without 7.W.N.Venables,B.D.Ripley,Modem Applied Statistics vowel inventory counts,Atkinson's analysis ig- simplification.we analyzed the total phoneme with S (Springer,New York,2002) 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 Commission of Education Research Innovation Key Project which vary tremendously among the world's languages are Dondac (3.91),Kam (2.87),and (11z204)and Shanghai Professional Development Funding languages (5) Buyang (2.49) (2010001).Y.Hu helped with some of the statistics. Similar problems happened to the simplifi- We further redid the correlation analyses be- cation of tone diversity.Most tonal languages in tween the total phoneme diversity and distance Supporting Online Material www.sciencemag.org/cgi/content/full335/6069/657-c/DC1 Africa have less than four tones,whereas most from the"best-fit origin."Different regions were Tables S1 to S4 tonal languages in Asia have more than four. chosen as potential best-fit origins.Interesting- 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 657-c 10 FEBRUARY 2012 VOL 335 SCIENCE www.sciencemag.org

data set eliminated the difference about the vowel diversity levels between the African and Eur￾asian languages. Besides the curtness in the vowel inventory counts, Atkinson’s analysis ig￾nored 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 simplifi￾cation 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 de￾monstrably 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 be￾tween the total phoneme diversity and distance from the “best-fit origin.” Different regions were chosen as potential best-fit origins. Interesting￾ly, 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 ) ex￾hibited 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 be￾tween the diversity and distance from Africa (Fig. 2A). To further test the robustness of these find￾ings, 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 lan￾guages, we applied a robust linear model to min￾imize 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. Ap￾parently, 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