Author: Sean

Language Evolution and Language Acquisition

The way children learn language sets the adaptive landscape on which languages evolve.  This is acknowledged by many, but there are few connections between models of language acquisition and models of language Evolution (some exceptions include Yang (2002), Yu & Smith (2007) and Chater & Christiansen (2009)).

However, the chasm between the two fields may be getting smaller, as theories are defined as models which are both more interpretable to the more technically-minded Language Evolutionists and extendible into populations and generations.

Also, strangely, models of word learning have been getting simpler over time.  This may reflect a move from attributing language acquisition to specific mechanisms towards a more general cognitive explanation.  I review some older models here, and a recent publication by Fazly et al.

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Language Evolution and Tetris! Part 2

Ok, so my previous experiment was an incredible failure.  The program crashed in sixteen different ways, including suddenly deciding not to respond to key presses for no apparent reason.  A rather lazy Ghost in The Shell.  Although about 8 people participated, the data was unusable.  What on earth was I trying to achieve?

The experiment was a typical human Iterated Learning experiment (e.g. Kirby, Cornish, Smith, 2008) – there were a set of meanings (Tetris blocks) which varied along two dimensions (shape and colour).  Participants were shown the words for half of the meanings, but then asked to recall words for each meanings.  These responses were then given to the next participant as input.  Over time, other such experiments result in meanings which are compositional and more learnable.  However, the meaning space tends to ‘collapse’ as the same label is applied to many meanings.

I was trying to do an iterated learning experiment which teased apart the difference between labelling a form and labelling a function.  If participants label the function of an object, the environment will play a greater role in the evolution of the language.

There were two chains –  one played Tetris where you have to complete lines to score points – colours are irrelevant.  The other chain played “Coltris” where you scored points by placing more than 4 blocks of the same colour next to each other.  Also, each individual block in a brick finds its own lowest point (i.e. the brick breaks apart), meaning that shape is much less important. That is, for Tetris, the functionally salient feature was shape while for Coltris it was colour.

What I was hoping was that, for the Tetris players, the signal space would ‘collapse’ in the colour dimension.  That is, labels would distinguish bricks by shape, but not colour.  For the Coltris, the opposite should have happened – labels would have distinguished bricks by colour but not shape.

Gary Lupyan has shown that naming categories of objects can affect your perception of those objects (Lupyan, G. (2008). The Conceptual Grouping Effect: Categories Matter (and named categories matter more). Cognition, 108, 566-577.).  My experiment looks into where those distinct category names came from in the first place.  Having said this, the experiment would have been more neat than illuminating.

Oh Well.

Language Evolution and Tetris!

Hello, people of the Blogosphere!

Why not take some time out from your dedicated reading to do a little language evolution experiment!  And all you have to do is play Tetris!

The Evolution of Tetris

… and learn an alien language.  It takes no more than 10 minutes.

The instructions and game are here:

http://blake.ppls.ed.ac.uk/~s0451342/tetris/Tetris_Experiment.htm

Due to me being a terrible programmer, it’ll probably crash or do some weird things.  But it’s all in the name of pseudo-science!

P.S. – users of the latest Firefox will need to update java.

Time Travel, Dreams and The Origin of Knowledge

I’ve been attending a weekly seminar on the Metaphysics of Time Travel, given by Alasdair Richmond.  Yesterday, he was talking about the way knowledge arises in causal chains.  Popper (1972 and various others) argues that “Knowledge comes into existence only by evolutionary, rational processes” (quoted from Paul Nahin, ‘Time Machines: Time Travel in Physics, Metaphysics and Science Fiction, New York, American Institute of Physics, 1999: 312).  Good news for us scholars of Cultural Evolution.  However, Richmond also talked about the work of David Lewis on the nature of causality.  There are three ways that causal chains can be set up:

The first is an infinite sequence of events each caused by the previous one.  For example, I’m typing this blog because my PhD work is boring, I’m doing a PhD because I was priced in by funding, I applied for funding because everyone else did … all the way back past my parents meeting and humans evolving etc.

The second option is for a finite sequence of events – like the first option, but with an initial event that caused all the others, like the big-bang.

The third option is a circular sequence of events.  In this, A is caused by B which is caused by A.  For instance, I’m writing doing a PhD because I got funding and I got funding because I’m doing a PhD, because I got funding.  There is no initial cause, the states just are. This third option seems really odd, not least because it involves time-travel.  Where do the states come from?  However, argues Lewis, they are no more odd than any of the other two options.  Option one has a state with no cause and option two has a cause for every event but no original cause.  So, how on earth can we get at the origin of knowledge if there is no logical possibility of determining the origin of any sequence of events?

One answer is just to stop caring after a certain point.  Us linguists are unlikely to get to the point where we’re studying vowel shifts in the first few seconds of the big bang.

The other answer is noise.  Richmond suggested that ‘Eureka’ moments triggered by random occurrences, for instance (Nicholas J. J. Smith, ‘Bananas Enough for Time Travel?’, British Journal for the Philosophy of Science, Vol. 48, 1997: 363-89). mishearing someone or a strange dream, could create information without prior cause.

Spookily, the idea I submitted for my PhD application came to me in a dream.

Bayesian Bilingualism

Recently, David Burkett and Tom Griffiths have looked at iterated learning of multiple languages from multiple teachers (Burkett & Griffiths 2010, see my post here).  Here, I’ll describe a simpler model which allows bilingualism.  I show that, counter-intuitively, bilingualism may be more stable than monolingualism.

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Learning Multiple languages from Multiple teachers

As Niyogi & Berwick (2009) point out, there is a tendency in modelling of Linguistic Evolution to assume chains of single learners inheriting single grammars from single teachers.   This is, of course, not realistic – we learn language from many people and people can speak more than one language.  However, Niyogi & Berwick suggest deeper objections.

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