Bayesian phylogenetic analysis of Japonic languages

Lee & Hasegawa (2011) use phylogenetic methods to trace the origins of Japonic languages and dialects.  Two hypotheses are considered:  First, the farming/language dispersal hypothesis posits that the main factor for the divergence of genetic and linguistic diversity was agricultural expansion.  Second, the diffusion/transformation hypothesis posits that cultural innovations such as farming can diffuse between societies, and so genetic and linguistic diversity should not be linked.  The estimate of the common linguistic ancestor was in accordance with the farming/language dispersal hypothesis, again suggesting that that linguistic diversity followed genetic diversity.

The study is notable in considering dialects as well as languages and using etymology dictionaries to reconstruct forms from Middle and Old Japanese.  The analysis is also done with their own reconstructions and another, unrelated set.  The technique is similar to that used by Russel Gray et al. (2009) to study Pacific settlement patterns.

Lee S, & Hasegawa T (2011). Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages. Proceedings. Biological sciences / The Royal Society PMID: 21543358

Gray, R., Drummond, A., & Greenhill, S. (2009). Language Phylogenies Reveal Expansion Pulses and Pauses in Pacific Settlement Science, 323 (5913), 479-483 DOI: 10.1126/science.1166858

Phonemic Diversity Supports a Serial Founder Effect Model of Language Expansion from Africa

Just read about an article on phoneme diversity via GNXP and Babel’s Dawn. Hopefully I’ll share some of my thoughts on the paper this weekend as it clearly ties in with work I’m currently doing (see here and here). Below is the abstract:

Human genetic and phenotypic diversity declines with distance from Africa, as predicted by a serial founder effect in which successive population bottlenecks during range expansion progressively reduce diversity, underpinning support for an African origin of modern humans. Recent work suggests that a similar founder effect may operate on human culture and language. here I show that the number of phonemes used in a global sample of 504 languages is also clinal and fits a serial founder-effect model of expansion from an inferred origin in Africa. This result, which is no explained by more recent demographic history, local language diversity, or statistical non-independence within language families, points to parallel mechanisms shaping genetic and linguistic diversity and supports an African origin of modern human languages.

Reference: Atkinson, Q.D (2011). Phonemic Diversity Supports a Serial Founder Effect Model of Language Expansion from Africa. Science 332, 346. DOI: 10.1126/science.1199295.

Update: I’ve given a lengthier response here.

How old am I?

It’s my birthday!  But how old am I?  Well, that’s not such a straightforward question.  Even a seemingly well-defined concept such as age can be affected by cultural factors

First, my age in years is a bit of an estimate of the actual amount of time I’ve been alive, due to leap-years etc.  Second, a year is a culturally determined (although not all that arbitrary) amount of time.  But these are petty squabbles.

There are bigger differences.  For instance, there are cultural differences when it comes to the recall of birth dates.  And I’m not talking about saying you’re 24 when you’re 68.  Matched comparisons of age reporting in death certificates and census data found minimal differences for white Americans (Hill et al., 2000) but nearly half were inconsistent for African-Americans (Hill et al., 1997). These may be due to economic differences.

Furthermore, the definition of age can vary cross-culturally.  Knodel & Chyovan (1991) surveyed women between the ages of 15 and 49 in Thailand.  As well as finding that up to 20% reported an age that was more than one year different to their actual age, they surmised that most calculated their age as difference between the present year and the year of their birth, disregarding whether their birthday had passed.


So in some parts of the world I’ve been 26 for four months now, or was it 25?


Hill, M., Preston, S., Elo, I., & Rosenwaike, I. (1997). Age-Linked Institutions and Age Reporting among Older African Americans Social Forces, 75 (3) DOI: 10.2307/2580528

Hill, M., Preston, S., & Rosenwaike, I. (2000). Age Reporting among White Americans Aged 85+: Results of a Record Linkage Study Demography, 37 (2) DOI: 10.2307/2648119

Knodel J, & Chayovan N (1991). Age and birth date reporting in Thailand. Asian and Pacific population forum / East-West Population Institute, East-West Center, 5 (2-3) PMID: 12343437

Colour terms and national flags

I’m currently writing an article on the relationship between language and social features of the speakers who use it. As studies such as Lupyan & Dale (2010) have discovered, language structure is partially determined by social structure.  However, it’s also probable that many social features of a community are determined by its language.

Today, I wondered whether the number of basic colour terms a language has is reflected in the number of colours on its country’s flag. The idea being that a country’s flag contains colours that are important to its society, and therefore a country with more social tools for discussing colour (colour words) will be more likely to put more colours on its flag. It was a long shot, but here’s what I found:

The World Atlas of Language Structures has data on the number of basic colours in many languages (Kay & Maffi, 2008). Wikipedia has a list of country flags by the number of colours in them.  Languages with large populations (like English, Spanish etc.) were excluded.  It’s known that the number of basic colour terms correlates with latitude, so a partial correlation was carried out.  There was a small but significant relationship between the number of colour terms in a langauge and the number of colours on the flag where that language is spoken (r = 0.15, τ = 254, p=0.01, partial correlation, 2-tailed using Kendall’s tau).

Here’s the flag of Belize, where Garífuna is spoken (9-10 colours in the language, 12 colours on the flag):

Here is the flag of Nigeria where Ejagham is spoken (3-4 colours in the langauge, 2 colours on the flag):

Interestingly, the languages with the highest number of colours in their language and flag come from Central America while the majority of the languages with the lowest number of colours in their language and flag come from Africa.  Maybe there’s some cultural influence on neighbouring flags.


Here’s a boxplot, which makes more sense:

Also, I re-ran the analysis taking into account distance from the equator, speaker population and some properties of the nearest neighbour of each language (number of colours on flag and number of basic colours in langauge).  A multiple regression showed that the number of basic colours in a language is still a significant predictor of the number of colours in its national flag (r = 0.12, F(106,16)=1.8577, p= 0.03).  This analysis was done by removing languages with populations more than 2 standard deviations from the mean (9 languages out of 140).  The relationship is still significant with the whole dataset.

There are still problems with this analysis, of course.  For example, many of the languages in the data are minority languages which may have little impact on the national identity of a country.  Furthermore, the statistics may be compromised by multiple comparisons, since there may be a single flag for more than one language.  Also, a proper measure of the influence of surrounding languages would be better.  The nearest neighbour was supposed to be an approximation, but could be improved.

Lupyan G, & Dale R (2010). Language structure is partly determined by social structure. PloS one, 5 (1) PMID: 20098492

Kay, Paul & Maffi, Luisa. (2008). Number of Basic Colour Categories.In: Haspelmath, Martin & Dryer, Matthew S. & Gil, David & Comrie, Bernard (eds.) The World Atlas of Language Structures Online. Munich: Max Planck Digital Library, chapter 133.

Prairie Dog Communication

istockphoto.comA recent NPR radio show covered the research of the biosemiotician Con Slobodchikoff of the Univeristy of Arizone on prairie dog calls. The piece is very public-orientated, but still might be worth listening to.

ResearchBlogging.orgWe’ve all (I hope) heard of the vervet monkeys, which have different alarm calls for different predators, such as for leopard (Panthera pardus), martial eagle (Polemaetus bellicosus), and python (Python sebae). (Seyfarth et al. 1980) For each of these predators, an inherent and unlearned call is uttered by the first spectator, after which the vervet  monkeys respond in a suitable manner – climb a tree, seek shelter, etc. It appears, however, that prairie dogs have a similar system, and that it is a bit more complicated.

Slobodchikoff conducted a study where three girls (probably underpaid, underprivaleged, and underappreciated (under)graduate students) walked through a prairie dog colony wearing shirts of the colors green, yellow, and blue. The call of the first prairie dog to notice them was recorded, after which the prairie dogs all fled into their burrows. The intern then walked through the entire colony, took a break for ten minutes, changed shirts, and did it again.

What is interesting is that the prairie dogs have significantly different calls (important, as they are pretty much exactly the same to human ears) for blue and yellow, but not for yellow and green. This is due to the dichromatic nature of praire dog eyesight (for a full study of the eyesight of retinal photoreceptors of subterranean rodents, consult Schleich et al. 2010). The distinction between blue and yellow is important, however, as there isn’t necessarily any reason that blue people are any more dangerous to praire dogs than yellow ones. “This in turn suggests that the prairie dogs are labeling the predators according to some cognitive category, rather than merely providing instructions on how to escape from a particular predator or responding to the urgency of a predator attack.” (Slobodchikoff 2009, pp. 438)

Another study was then done where two towers were built and a line was strung between them. When cut out shapes were slung down the line, the prairie dogs were able to distinguish a triangle from a circle, but not a circle from a square. So, the prairie dogs are not entirely perfect at encoding information. The conclusion still stands however that more information is encoded in the calls than is entirely relevant to a suitable reaction (unless one were to argue that evolutionary pressure existed on prairie dogs to distinguish blue predators from yellow ones.)

NPR labels this ‘prairiedogese’, which makes me shiver and reminds me of Punxatawney Pennsylvania, where Bill Murray was stuck on a vicious cycle in the movie Groundhog Day, forced every day to watch the mayor recite the translated proclamation of the Groundhog, which of course spoke in ‘groundhogese’. Luckily, however, there won’t be courses in this ‘language’.


Schleich, C., Vielma, A., Glösmann, M., Palacios, A., & Peichl, L. (2010). Retinal photoreceptors of two subterranean tuco-tuco species (Rodentia, Ctenomys): Morphology, topography, and spectral sensitivity The Journal of Comparative Neurology, 518 (19), 4001-4015 DOI: 10.1002/cne.22440

Seyfarth, R., Cheney, D., & Marler, P. (1980). Monkey responses to three different alarm calls: evidence of predator classification and semantic communication Science, 210 (4471), 801-803 DOI: 10.1126/science.7433999

Slobodchikoff CN, Paseka A, & Verdolin JL (2009). Prairie dog alarm calls encode labels about predator colors. Animal cognition, 12 (3), 435-9 PMID: 19116730

Tool making and Language Evolution

There’s an often cited gap in tool making history in which humans did not advance from simple Oldowan tools (which date back to about 2.5 million years ago) until about 500,000 years ago when progress became much faster. There is much debate as to whether this gap in progress is the result of the cognitive abilities to make more innovative tools or if it was an issue of dexterity.

A recent article by Faisal et al. (2010) “The Manipulative Complexity of Lower Paleolithic Stone Toolmaking” has tried to address these problems by assessing the manipulative complexity of tool making tasks from the Oldowan tools to the more advanced hand axes from much later.

A stone ‘core’ (A) is struck with a hammerstone (B) in order to detach sharp stone ‘flakes’. In Oldowan toolmaking (C, top) the detached flakes (left in photo) are used as simple cutting tools and the core (right in photo) is waste. In Acheulean toolmaking (C, bottom), strategic flake detachments are used to shape the core into a desired form, such as a handaxe. Both forms of toolmaking are associated with activation of left ventral premotor cortex (PMv), Acheulean toolmaking activates additional regions in the right hemisphere, including the supramarginal gyrus (SMG) of the inferior parietal lobule, right PMv, and the right hemisphere homolog of anterior Broca's area: Brodmann area 45 (BA 45).

The following is taken from a press release from

Researchers used computer modelling and tiny sensors embedded in gloves to assess the complex hand skills that early humans needed in order to make two types of tools during the Lower Palaeolithic period, which began around 2.5 million years ago. The cross-disciplinary team, involving researchers from Imperial College London, employed a craftsperson called a flintnapper to faithfully replicate ancient tool-making techniques.

The team say that comparing the manufacturing techniques used for both Stone Age tools provides evidence of how the human brain and human behaviour evolved during the Lower Palaeolithic period.

The flintnapper who participated in today’s study created two types tools including the razor-sharp flakes and hand-held axes. He wore a data glove with sensors enmeshed into its fabric to record hand and arm movements during the production of these tools.

After analysing this data, the researchers discovered that both flake and hand-held axe manufacturing techniques were equally complex, requiring the same kind of hand and arm dexterity. This enabled the scientists to rule out motor skills as the principal factor for holding up stone tool development.

The team deduced from their results that the axe-tool required a high level of brain processing.

This has implications for language evolution as brain scans from tool makers have shown significant overlap with areas involved in discourse-level language processing as well as complex hand gestures. The study finishes with the following:

…the anatomical overlap of Late Acheulean toolmaking and right hemisphere linguistic processing may reflect the flexible “mapping” of diverse overt behaviors onto shared functional substrates in the brain. This implies that: 1) selection acting on either language or toolmaking abilities could have indirectly favored elaboration of neural substrates important for the other, and 2) archaeological evidence of Paleolithic toolmaking can provide evidence for the presence of cognitive capacities also important to the modern human faculty for language.

Read the original article at PLoS ONE:

On Phylogenic Analogues

A recent post by Miko on Kirschner and Gerhart’s work on developmental constraints and the implications for evolutionary biology caught my eye due to the possible analogues which could be drawn with language in mind. It starts by saying that developmental constraints are the most intuitive out of all of the known constraints on phenotypic variation.  Essentially, whatever evolves must evolve from the starting point, and it cannot ignore the features of the original. Thus, a winged horse would not occur, as six limbs would violate the basic bauplan of tetrapods. In the same way, a daughter language cannot evolve without taking into account the language it derives from and language universals. But instead of viewing this as a constraint which limits the massive variation we see biologically or linguistically between different phenotypes, developmental constraints can be seen as a catalyst for regular variation.

ResearchBlogging.orgA recent post by Miko on Kirschner and Gerhart’s work on developmental constraints and the implications for evolutionary biology caught my eye due to the possible analogues which could be drawn with language in mind. It starts by saying that developmental constraints are the most intuitive out of all of the known constraints on phenotypic variation.  Essentially, whatever evolves must evolve from the starting point, and it cannot ignore the features of the original. Thus, a winged horse would not occur, as six limbs would violate the basic bauplan of tetrapods. In the same way, a daughter language cannot evolve without taking into account the language it derives from and language universals. But instead of viewing this as a constraint which limits the massive variation we see biologically or linguistically between different phenotypes, developmental constraints can be seen as a catalyst for regular variation.

A pretty and random tree showing variation among IE languages.

Looking back over my courses, I’m surprised by how little I’ve noticed (different from how much was actually said) about reasons for linguistic variation. The modes of change are often noted: <th> is fronted in Fife, for instance, leading to the ‘Firsty Ferret’ instead of the ‘Thirsty Ferret’ as a brew, for instance. However, why the <th> is fronted at all isn’t explained beyond cursory hypothesis. But that’s a bit besides the point: what is the point is that phenotypic variation is not necessarily random, as there are constraints – due to the “buffering and canalizing of development” – which limit variation to a defined range of possibilities. There clearly aren’t any homologues between biological embryonic processes and linguistic constraints, but there are developmental analogues: the input bottleneck (paucity of data) given to children, learnability constraints, the necessity for communication, certain biological constraints to do with production and perception, etc. These all act on language to make variation occur only within certain channels, many of which would be predictable.

Another interesting point raised by the article is the robustness of living systems to mutation. The buffering effect of embryonic development results in the accumulation of ‘silent’ variation.  This has been termed evolutionary capacitance. Silent variation can lay quiet, accumulating, not changing the phenotype noticeably until environmental or genetic conditions unmask them. I’ve seen little research (not that I don’t expect there to be plenty) on the theoretical implications of the influence of evolutionary capacitance on language change – in other words, how likely a language is to make small variations which don’t affect language understanding before a new language emerges (not that the term language isn’t arbitrary based on the speaking community, anyway). Are some languages more robust than others? Is robustness a quality which makes a language more likely to be used in multilingual settings – for instance, in New Guinea, if seven languages are mutually indistinguishable, is it likely the that local lingua franca is forced by its environment to be more robust in order to maximise comprehension?

The article goes on about the cost of robustness: stasis. This can be seen clearly in Late Latin, which was more robust than the daughter languages as it was needed to communicate in different environments where the language had branched off into the Romance languages, and an older form was necessary in order for communication to ensue. Thus, Latin retained usage well after the rest of it had evolved into other languages. Another example would be Homeric Greek, which retained many features lost in Attic, Doric, Koine, and other dialects, as it was used in only a certain environment and was therefore resistant to change. This has all been studied before better than I can sum it up here. But the point I am making is that analogues can be clearly drawn here, and some interesting theories regarding language become apparent only when seen in this light.

A good example, also covered, would be exploratory processes, as Kirschner and Gerhart call them. These are processes which allow for variation to occur in environments where other variables are forced to change. The example given is the growth of bone length, which requires corresponding muscular, circulatory, and other dependant systems to also change. The exploratory processes allow for future change to occur in the other systems. That is, they expedite plasticity. So, for instance, an ad hoc linguistic example would be the loss of a fixed word order, which would require that morphology step in to fill the gap. In such a case, particles or affixes or the like would have to have already paved the way for case markers to evolve, and would have had to have been present to some extent in the original word order system. (This may not be the best example, but I hope my point comes across.)

Naturally, much of this will have seemed intuitive. But, as Miko stated, these are useful concepts for thinking about evolution; and, in my own case especially, the basics ought to be brought back into scrutiny fairly frequently. Which is justification enough for this post. As always, comments appreciated and accepted. And a possible future post: clade selection as a nonsensical way to approach phylogenic variation.


Caldwell, M. (2002). From fins to limbs to fins: Limb evolution in fossil marine reptiles American Journal of Medical Genetics, 112 (3), 236-249 DOI: 10.1002/ajmg.10773

Gerhart, J., & Kirschner, M. (2007). Colloquium Papers: The theory of facilitated variation Proceedings of the National Academy of Sciences, 104 (suppl_1), 8582-8589 DOI: 10.1073/pnas.0701035104

Gerhart, J., & Kirschner, M. (2007). Colloquium Papers: The theory of facilitated variation Proceedings of the National Academy of Sciences, 104 (suppl_1), 8582-8589 DOI: 10.1073/pnas.0701035104

Domain-General Regions and Domain-Specific Networks

The notion of a domain-specific, language acquisition device is something that still divides linguists. Yet, in an ongoing debate spanning at least several decades, there is still no evidence, at least to my knowledge, for the existence of a Universal Grammar. Although, you’d be forgiven for thinking that the problem was solved many years ago, especially if you were to believe the now  sixteen-year old words of Massimo Piattelli-Palmarini (1994):

The extreme specificity of the language system, indeed, is a fact, not just a working hypothesis, even less a heuristically convenient postulation. Doubting that there are language-specific, innate computational capacities today is a bit like being still dubious about the very existence of molecules, in spite of the awesome progress of molecular biology.

Suffice to say, the analogy between applying scepticism of molecules and scepticism of Universal Grammar is a dud, even if it does turn out that the latter does exist. Why? Well, as stated above: we still don’t know if humans have, or for that matter, even require, an innate ability to process certain grammatical principles. The rationale for thinking that we have some innate capacity for acquiring language can be delineated into a twofold argument: first, children seem adept at rapidly learning a language, even though they aren’t exposed to all of the data; and second, cognitive science told us that our brains are massively modular, or at the very least, should entail some aspect that is domain specific to language (see FLB/FLN distinction in Hauser, Chomsky & Fitch, 2002). I think the first point has been done to death on this blog: cultural evolution can provide an alternative explanation as to how children successfully learn language (see here and here and Smith & Kirby, 2008). What I haven’t really spoken about is the mechanism behind our ability to process language, or to put it differently: how are our brains organised to process language?

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That’s Linguistics (Not logistics)

Linguists really need a catchy tune to match those in logistics. Any takers?

I always remember when one of my former lecturers said he was surprised by how little the average person will know about linguistics. For me, this was best exemplified when, upon enquiring about my degree, my friend paused for a brief moment and said: “Linguistics. That’s like logistics, right?” Indeed. Not really being in the mood to bash my friend’s ignorance into a bloody pulp of understanding, I decided to take a swig of my beer and simply replied: “No, not really. But it doesn’t matter.” Feeling guilty for not gathering the entire congregation of party-goers, sitting them down and proceeding to explain the fundamentals of linguistics, I have instead decided to write a series of 101 posts.

With that said, a good place to start is by providing some dictionary definitions highlighting the difference between linguistics and logistics:

Linguistics /lɪŋˈgwɪs.tɪks/ noun

the systematic study of the structure and development of language in general or of particular languages.

Logistics /ləˈdʒɪs.tɪks/ plural noun

the careful organization of a complicated activity so that it happens in a successful and effective way.

Arguably, linguistics is a logistical solution for successfully, and rigorously, studying language through the scientific method, but to avoid further confusion this is the last time you’ll see logistics in these posts. So, as you can probably infer, linguistics is a fairly broad term that, for all intensive purposes, simply means it’s a discipline for studying language. Those who partake in the study of language are known as linguists. This leads me to another point of contention: a linguist isn’t synonymous with a polyglot. Although there are plenty of linguists who do speak more than one language, many of them are quite content just sticking to their native language. It is, after all, possible for linguists to study many aspects of a language without necessarily having anything like native-level competency. In fact, other than occasionally shouting pourquoi when (drunkly) reflecting on my life choices, or ach-y-fi when a Brussels sprout somehow manages to make its way near my plate, I’m mainly monolingual.

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More on Phoneme Inventory Size and Demography

On the basis of Sean’s comment, about using a regression to look at how phoneme inventory size improved as geographic spread was incorporated along with population size, I decided to look at the stats a bit more closely (original post is here). It’s fairly easy to perform multiple regression in R, which, in the case of my data, resulted in highly significant results (p<0.001) for the intercept, area and population (residual standard error = 9.633 on 393 degrees of freedom; adjusted R-Squared = 0.1084). I then plotted all the combinations as scatterplots for each pair of variables. As you can see below, this is fairly useful as a quick summary but it is also messy and confusing. Another problem is that the pairs plot is on the original data and not the linear model.

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