Culture, its evolution and anything inbetween
The Speculative Grammarian has published startling new findings that the Acacia Tree is central to Language Evolution. This ground-breaking work was based on my post here.
Happy EvoLang deadline day!
Sean Geraint (2011). Language Evolution and the Acacia Tree Speculative Grammarian, Vol CLXII (4)
Do a fun language experiment!*
You can take part in a pilot experiment about language learning: It takes about 8 minutes (and is NOT an iterated learning experiment, although it looks a bit like one). I’ll release the results (and the hypothesis) right here on Replicated Typo.
* may not be loads of fun.
Does your social network determine your rational rationality? When trying to co-ordinate with a number of other people on a cultural feature, the locally rational thing to do is to go with the majority. However, in certain situations it might make sense to choose the minority feature. This means that learning multiple features might be rational in some situations, even if there is a pressure against redundancy. I’m interested in whether there are situations in which it is rational to be bilingual and whether bilingualism is stable over long periods of time. Previous models suggest that bilingualism is not stable (e.g. Castello et al. 2007), therefore an irrational strategy (at least not a primary strategy), but these were based on locally rational learners.
This week we had a lecture from Simon DeDeo on system-wide timescales in the behaviour of macaques. He talked about Spin Glasses and rationality, which got me thinking. A Spin Glass is a kind of magnetised material where the ‘spin’ or magnetism (plus or minus) of the molecules does not reach a consensus, but flips about chaotically. This happens when the structure of the material creates ‘frustrated’ triangles where a molecule is trying to co-ordinate with other molecules with opposing spins, making it difficult to resolve the tensions. Long chains of interconnected frustrated triangles can cause system-wide flips on the order of hours or days and are difficult to study both in models (Ising model) and in the real world.
One of the risks of blogging is that you can fire off ideas into the public domain while you’re still excited about them and haven’t really tested them all that well. Last month I blogged about a random walk model of linguistic complexity (the current post won’t make much sense unless you’ve read the original). Essentially, it was trying to find a baseline for the expected correlation between a population’s size and a measure of linguistic complexity. It assumed that the rate of change in the linguistic measure was linked to population size. Somewhat surprisingly, correlations between the two measures (similar to the kind described in Lupyan & Dale, 2010) emerged, despite there being no directional link.
However, these observations were made on the basis of a relatively small sample size. In order to discover why the model was behaving like this, I needed to run a lot more tests. The model was running slowly in python, so I transliterated it to R. When I did, the results were very different: In the first model an inverse relationship between the population size and the rate of change of linguistic complexity yielded a negative correlation between population size and linguistic complexity (perhaps explaining results such as Lupyan & Dale’s). However in the R model this did not occur. In fact, significant correlations only appeared 5% of the time, with that 5% being split exactly between positive and negative correlations. That is, the baseline model has a standard confidence interval, not the much stricter one I had suggested in the last post.
Why was this happening? In short: Rounding errors and small sample sizes.
I checked the Python code, but couldn’t find a bug, so the correlations really were appearing, and really were favouring a negative correlation. Here’s my best explanation: First, the sample of runs was too low to capture the proper distribution. However, strong correlations were appearing. This could be because although the linguistic complexity measure started out pretty randomly distributed, the individual communities were synchronising at the maximum and minimum of the range as they bumped up against it. This caused temporary clusters in the low ranges where the linguistic complexity was changing rapidly (and therefore more likely to synchronise), creating tied ranks in the corners. In addition to this, the Python script I was using had a lower bit depth for its numbers than R, so was more prone to rounding errors. I have to assume as well that my Python script somehow favoured numbers closer to 1 than to 0. It’s still not a very satisfactory explanation, but the conclusion remains that, as one would expect, affecting just the rate of change of linguistic complexity does not produce correlations.
Modelling evolutionary systems often runs into these kinds of problems: The search spaces are often intractable for some approaches. Also I am not, as a mere linguist, aware of some of the more advanced computational techniques. It’s one of the reasons that Evolutionary Linguistics requires a pluralist approach and tools from many different disciplines.
It’s embarrassing to have to correct previous statements, but I guess that’s what Science is about. In the blogging age ideas can get out before they’re fully tested and potentially affect other work. This has its advantages – good ideas can get out faster. But it also means that the reader must be more critical in order to catch poor ideas like the one I’m correcting here.
Sorry, Science.
Here’s a link to the R script (25 lines of code!).
Lupyan G, & Dale R (2010). Language structure is partly determined by social structure. PloS one, 5 (1) PMID: 20098492
One of this week’s xkcd comics makes the point that combinatorial passwords (sequence of common words) may be better than holistic ones (semi-random string). This may be because we’re fooled into thinking that a password that is difficult to remember will be difficult to guess. This turns out not to be the case. I’m currently thinking about whether combinatoriality would emerge from an iterated learning chain even if the participants were told to give answers that they thought nobody else would give.
Here’s a video of a talk I gave at the Santa Fe Institute‘s Complex Systems Summer School (written with roboticist Andrew Tinka-check out him talking about his fleet of floating robots). The talk was a response to the “Evolution Challenge”:
I also blogged about a part of this talk here (why a mad scientist’s attempt at creating A.I. to make new scientific discoveries was doomed).
The talk was given a prise for best talk by the judging panel which included David Krakauer, Tom Carter and best-selling author Cormac McCarthy. At several points in the talk, I completely forget what I was supposed to say because the people filming the event asked me to set my screen up in a way so I couldn’t see my notes.
Sperl, M., Chang, A., Weber, N., & Hübler, A. (1999). Hebbian learning in the agglomeration of conducting particles Physical Review E, 59 (3), 3165-3168 DOI: 10.1103/PhysRevE.59.3165
Chater N, & Christiansen MH (2010). Language acquisition meets language evolution. Cognitive science, 34 (7), 1131-57 PMID: 21564247
Ay N, Flack J, & Krakauer DC (2007). Robustness and complexity co-constructed in multimodal signalling networks. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 362 (1479), 441-7 PMID: 17255020
Ackley, D.H., and Cannon, D.C.. “Pursue Robust Indefinite Scalability”. In Proceedings of the Thirteenth Workshop on Hot Topics in Operating Systems (HOTOS-XIII) (2011, May). Abstract, PDF.
Guttal V, & Couzin ID (2010). Social interactions, information use, and the evolution of collective migration. Proceedings of the National Academy of Sciences of the United States of America, 107 (37), 16172-7 PMID: 20713700
When thinking about bilingualism and language evolution, there appears to be a paradox: Children are adept at learning more than one language at a time and there are many bilingual societies in the world. However, pressures on memory and redundancy makes it unclear what the adaptive advantage of a cognitive capacity for learning multiple languages at an early stage of language evolution would be. For instance, Hagen (2008) has argued that a bilingual ability would not have been adaptive in early societies and so could not have been selected for. Furthermore, many models have suggested that bilingualism is an unstable trait in a society (e.g. Castello et al., 2008). How can we account for the evolution of this ability? Would an early population of language users most likely be monolingual or bilingual? Here, I take a top down and a bottom up approach and show that they tends to lead to two different conclusions.
Continue reading “The Bilingual paradox in Language Evolution: Top down versus bottom up approaches”
Through this post on Sprogmuseet about Atkinson’s analysis of the out of Africa hypothesis, I found an article by Ember & Ember (2007) (who also quantified the link between colour lexicon size and distance from the equator, see my post here) on Sonority and climate. The article extends work by Fought et al. (2004) which finds that a language’s sonority is related to climate. Sonority is a measure of amplitude (loudness) as is greater for vowels than for consonants (for example, see here). Basically, the warmer the climate, the greater the sonority of the phoneme inventory of the population. The theory is that “people in warmer climates generally spend more time outdoors and communicate at a distance more often than people in colder climates”.
Continue reading “Sonority and Sex: Why smaller communities are louder”
Digital Humanities Sandbox Goes to the Congo, Part II
While Kurtz is the center of attention in Heart of Darkness, he doesn’t appear until relatively late in the story. He isn’t mentioned until about 8000 words into the 38000 word text nor do we know much about him until a long paragraph that starts roughly 23,000 words into the text. That paragraph, which I’ve called the nexus, is structurally central to the text, and is roughly 1500 words long.
I decided to investigated Kurtz’s presence in the text by the simple expedient of noting where the name “Kurtz” occurs. The result, my colleague Tim Perper subsequently told me, is what’s called a periodogram (PDF):
I was thinking about Daniel Nettle’s model of linguistic diversity which showed that linguistic variation tends to decline even with a small amount of migration between communities. I wondered if statistics about population movement would correlate with linguistic diversity, as measured by the Greenberg Diversity Index (GDI) for a country (see below). However, this is a cautionary tale about obsession and use of statistics. (See bottom of post for link to data).
Continue reading “Linguistic diversity and traffic accidents”