Culture, its evolution and anything inbetween
Last week I attended a lecture by Liz Bradley on chaos. Chaos has been used to create variations on musical and dance sequences (Dabby, 2008; Bradley & Stuart, 1998). I was interested to see whether this technique could be iterated and applied to birdsong or other culturally transmitted systems. I present a model of creative cultural transmission based on this.
Continue reading “Creative cultural transmission as chaotic sampling”
Prof. Alfred Hubler is an actual mad professor who is a danger to life as we know it. In a talk this evening he went from ball bearings in castor oil to hyper-advanced machine intelligence and from some bits of string to the boundary conditions of the universe. Hubler suggests that he is building a hyper-intelligent computer. However, will hyper-intelligent machines actually give us a better scientific understanding of the universe, or will they just spend their time playing Tetris?
Let him take you on a journey…
Continue reading “Cultural Evolution and the Impending Singularity”
Today I’ve been learning more about network structure (from Cris Moore) and I’ve applied my poor understanding and overconfidence to find language families from etymology data!
Here’s what I understand so far (see Clauset, Moore, & Newman, 2008): The modularity of a network is a measure of how many ‘communities’ it has. An optimal modularity will split the graph to maximise the average degree within modules or clusters. You can search all the possible clusterings to find this optimum. I’m still hazy on how this is actually done, and you can extend this to find hierarchies like phylogenetics, but without some assumptions. Luckily, there’s a network analysis program called gephi that does this automatically!
Continue reading “Categorising languages through network modularity”
Who are the movers and shakers in your field? You can use social network theory on your bibliographies to find out:
Today I learned about some studies looking at social networks constructed from bibliographic data (from Mark Newman, see Newman 2001 or Said et al. 2008) . Nodes on a graph represent authors and edges are added if those authors have co-authored a paper.
I scripted a little tool to construct such a graph from bibtex files – the bibliographic data files used with latex. The Language Evolution and Computation Bibliography – a list of the most relevant papers in the field – is available in bibtex format.
You can look at the program using the online Academic Networking application that I scripted today, or upload your own bibtex file to find out who the movers and shakers are in your field. Soon, I hope to add an automatic graph-visualisation, too.
A special issue of Human Biology on language evolution is now online. However, you may need special access.
Woah, I just read some of the responses to Dunn et al. (2011) “Evolved structure of language shows lineage-specific trends in word-order universals” (language log here, Replicated Typo coverage here). It’s come in for a lot of flack. One concern raised at the LEC was that, considering an extreme interpretation, there may be no affect of universal biases on language structure. This goes against Generativist approaches, but also the Evolutionary approach adopted by LEC-types. For instance, Kirby, Dowman & Griffiths (2007) suggest that there are weak universal biases which are amplified by culture. But there should be some trace of universality none the less.
Below is the relationship diagram for Indo-European and Uto-Aztecan feature dependencies from Dunn et al.. Bolder lines indicate stronger dependencies. They appear to have different dependencies- only one is shared (Genitive-Noun and Object-Verb).
However, I looked at the median Bayes Factors for each of the possible dependencies (available in the supplementary materials). These are the raw numbers that the above diagrams are based on. If the dependencies’ strength rank in roughly the same order, they will have a high Spearman rank correlation.
| Spearman Rank Correlation | Indo-European | Austronesian |
| Uto-Aztecan | 0.39, p = 0.04 | 0.25, p = 0.19 |
| Indo-European | -0.13, p = 0.49 |
Spearman rank correlation coefficients and p-values for Bayes Factors for different dependency pairs in different language families. Bantu was excluded because of missing feature data.
Although the Indo-European and Uto-Aztecan families have different strong dependencies, have similar rankings of those dependencies. That is, two features with a weak dependency in an Indo-European language tend to have a weak dependency in Uto-Aztecan language, and the same is true of strong dependencies. The same is true to some degree for Uto-Aztecan and Austronesian languages. This might suggest that there are, in fact, universal weak biases lurking beneath the surface. Lucky for us.
However, this does not hold between Indo-European and Austronesian language families. Actually, I have no idea whether a simple correlation between Bayes Factors makes any sense after hundreds of computer hours of advanced phylogenetic statistics, but the differences may be less striking than the diagram suggests.
UPDATE:
As Simon Greenhill points out below, the statistics are not at all conclusive. However, I’m adding the graphs for all Bayes Factors (these are made directly from the Bayes Factors in the Supplementary Material):
Austronesian: Bantu:
Indo-European: Uto-Aztecan:
Michael Dunn,, Simon J. Greenhill,, Stephen C. Levinson, & & Russell D. Gray (2011). Evolved structure of language shows lineage-specific trends in word-order universals Nature, 473, 79-82
Didn’t I say that alcohol affects language evolution?
The video is actually a pretty good summary of many of the main issues surrounding cultural evolution and self domestication. Surprisingly, Bacardi have actually done some research on this:
I cannot wait to make a Bacardi-WALS data cocktail.
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
A while ago, some collegues and I noticed that two prominent books on Language Evolution -Christiansen & Kirby’s Language Evolution and Fitch’s Evolution of Language – both included a picture of an acacia tree in the sunset on their covers. On closer analysis, it turned out that they were the same tree:
Thus began the Acacia Tree Hypothesis of Language Evolution.
Following this up, I was thinking about Dediu & Ladd’s discovery that linguistic tone is has certain genetic correlates. Here’s the map of languages with linguistic tone:
However, I suspected the devious influence of acacia trees and so I found some information on their geographic distribution:
As I suspected, countries in which the acacia tree Acacia nilotica grows are significantly more likely to have tonal languages:
| Tone | No Tone | |
| Acacia Trees | 163 | 117 |
| No Acacia Trees | 104 | 237 |
(Chi-squared with Yates’ continuity correction = 47.1, df = 1, p < 0.0001, data from Crop Protection Consortium and the World Atlas of Language Structures).
The plot thickens …
Dediu, D., & Ladd, D. R. (2007). Linguistic tone is related to the population frequency of the adaptive haplogroups of two brain size genes, Microcephalin and ASPM. Proceedings of the National Academy of Sciences, 104, 10944–10949.
Fitch, W. T. 2010 The evolution of language. Cambridge, UK: Cambridge University Press.
Christiansen, M. and Kirby, S. (2003). Language Evolution. Oxford University Press.
Update:
I’ve added the images David mentioned to the post:
Also, The Babel’s Dawn blog banner
NPR hosts a fascinating debate on the connections between science and art and the origins of culture. The guests include the utterly bizarre mix of novelist Cormac McCarthy (The Road, No Country for old Men), filmmaker Werner Herzog (The cave of forgotten dreams, Grizzly Man), and physicist Lawrence Krauss (The physics of Star Trek). Artificial Intelligence, Neanderthal culture and our place in the universe. And a buffalo humping a woman.