I haven’t had chance to read this paper, but it throws up some interesting discussion points relating to this blog. In particular, it relates to a hypothesis I put forward last year on Domain-General Regions and Domain-Specific Networks. Here is the abstract:
The ability to learn language is a human trait. In adults and children, brain imaging studies have shown that auditory language activates a bilateral frontotemporal network with a left hemispheric dominance. It is an open question whether these activations represent the complete neural basis for language present at birth. Here we demonstrate that in 2-d-old infants, the language-related neural substrate is fully active in both hemispheres with a preponderance in the right auditory cortex. Functional and structural connectivities within this neural network, however, are immature, with strong connectivities only between the two hemispheres, contrasting with the adult pattern of prevalent intrahemispheric connectivities. Thus, although the brain responds to spoken language already at birth, thereby providing a strong biological basis to acquire language, progressive maturation of intrahemispheric functional connectivity is yet to be established with language exposure as the brain develops.
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.
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).
I just heard a talk by social network creator extraordinaire Clio Andris about redefining regional boundaries in the UK based on telecommunications data. Her group took data from 12 billion telephone calls made over the space of a month and created a social network based on this (Ratti et al. , 2010). This network was then used to calculate how closely connected two neighbouring locations were. By optimising the spectral modularity, the best-fitting boundaries could be defined.
The data is fascinating, but there is little explanation. Here’s one of the maps (left) compared with a map of regional accents and a map of rail transport links (right):
One of the first things that struck me was the similarity with a map of regional accents (apologies for the quality of the accent map – I couldn’t find the one I was looking for). Apparently, people are talking to people that sound like them. Or, people who talk to each other sound like each other. This isn’t covered in the paper, but seems like an important issue.
Secondly, the rail links also seem to form the ‘backbones’ of the communications regions. This is also mentioned in the paper. However, these two features are linked.
Coming from Wales, the important fit here is the three-way split in Wales. South Wales feels like a different country to North Wales – culturally and linguistically. However, both are linked by having large amounts of natural resources: Coal in South Wales and slate in North Wales. This lead to massive migration into cities in the north and south, and rail links were set up to extract these resources to London or the nearest ports: Cardiff in the south and Liverpool in the north. Thus, it’s still a real pain to get from North Wales to South Wales. The picture is somewhat true of the east and west sides of the north of England.
So, the natural resources concentrated people and transport links. However, it also concentrated political views. The large migrant community in Wales, working for little pay in large mine institutions, became unionised. Socialism emerged, promoting political movements that lead to the minimum wage.
The point being, natural resources, transport links and politics are connected with some being historically dependent on each other. This is, perhaps, precisely why splitting the nation by who speaks to who is a good measure of political regions. It would be fascinating to see how linguistic divisions interact with these variables.
Ratti, Carlo, Sobolevsky, Stanislav, Calabrese, Francesco, Andris, Clio, Reades, Jonathan, Martino, Mauro, Claxton, Rob, & Strogatz, Steven H. (2010). Redrawing the map of Great Britain from a network of human interaction PLoS ONE, 5
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?
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!
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 Networkingapplication 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.
Right, I already referred to Atkinson’s paper in a previous post, and much of the work he’s presented is essentially part of a potential PhD project I’m hoping to do. Much of this stems back to last summer, where I mentioned how the phoneme inventory size correlates with certain demographic features, such as population size and population density. Using the the UPSID data I generated a generalised additive model to demonstrate how area and population size interact in determining the phoneme inventory size:
Interestingly, Atkinson seems to derive much of his thinking, at least in his choice of demographic variables, from work into the transmission of cultural artefacts (see here and here). For me, there are clear uses for these demographic models in testing hypotheses for linguistic transmission and change, as I see language as a cultural product. It appears Atkinson reached the same conclusion. Where we depart, however, is in our overall explanations of the data. My major problem with the claim is theoretical: he hasn’t ruled out other historical-evolutionary explanations for these patterns.
Before we get into the bulk of my criticism, I’ll provide a very brief overview of the paper.
There is a huge amount of linguistic diversity in the world. Isolation and drift due to cultural evolution can explain much of this, but there are many cases where interacting groups use several languages. In fact, by some estimates, bilingualism is the norm for most societies. If one views language as a tool for communicating about objects and events, it seems strange that linguistic diversity should be maintained over time for two reasons. First, it seems more efficient, during language use, to have a one-to-one mapping between signals and meanings. In fact, mutual exclusivity is exhibited by young children and has been argued to be an innate bias and crucial to the evolution of a linguistic system. How or why do bilinguals over-ride this bias? Secondly, learning two language systems must be more difficult than learning one. What is the motivation for expending extra effort on learning an apparently redundant system?
There is a battle about to commence. A battle in the world of cognitive modelling. Or at least a bit of a skirmish. Two articles to be published in Trends in Cognitive Sciences debate the merits of approaching cognition from different ends of the microscope.
On the side of probabilistic modelling we have Thom Griffiths, Nick Chater, Charles Kemp, Amy Perfors and Joshua Tenenbaum. Representing (perhaps non-symbolically) emergentist approaches are James McClelland, Matthew Botvinick, David Noelle, David Plaut, Timothy Rogers, Mark Seidenberg and Linda B. Smith. This contest is not short of heavyweights.
However, the first battleground seems to be who can come up with the most complicated diagram. I leave this decision to the reader (see first two images).
The central issue is which approach is the most productive for explaining phenomena in cognition. David Marr’s levels of explanation include the ‘computational’ characterisation of the problem, an ‘algorithmic’ description of the problem and an ‘implementational’ explanation which focusses on how the task is actually implemented by real brains. Structured probabilistic takes a ‘top-down’ approach while Emergentism takes a ‘bottom-up’ approach.