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Jim Hurford: What is wrong, and what is right, about current theories of language, in the light of evolution? (2)

September 11, 2012 in Conference, Evolution, Science

This post continues my summary of Jim Hurford’s discussion of two contrasting extreme positions on language evolution in his plenary talk at the Poznan Linguistic Meeting. Here’s the summary of these two positions from my last post:

Position A:

(1) There was a single biological mutation which (2) created a new unique cognitive domain, which then (3) immediately enabled the “unlimited command of complex structures via the computational operation of merge. (4) This domain is used primarily for advanced private thought and only derivatively for public communication. (5) It was not promoted by natural selection.

Position B:

(1) There were many cumulative mutations which (2) allowed the expanding interactions of pre-existing cognitive domains creating a new domain, which however is not characterized by principles unique to language. This then (3) gradually enabled the command of successively more complex structures. Also, on this view, this capacity was used primarily for public communication, and only derivatively for advanced private thought and was (5) promoted by natural selection.

Hurford criticized the position that the biological changes enabling languages primarily evolved for private thought, because this would imply that the first species in the Homo lineage that developed the capacity for unlimited combinatorial private thought (i.e. “merge”) were non-social and isolated clever hominids. This, as Hurford rightly points out, is quite unrealistic given everything we know about human evolution regarding, for example, competition, group size, neocortex side and tactical deception. There is in fact very strong evidence that what characterizes humans the most is the exact opposite as would be predicted by the “Merge developed in the service of enhancing private thought” position: We have the largest group size of any primate, the largest neocortex (which has been linked to the affordances of navigating a complex social world) and have the most pronounced capacity for tactical deception.

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Jim Hurford: What is wrong, and what is right, about current theories of language, in the light of evolution?

September 10, 2012 in Conference, Science

As I mentioned in my previous post, the 2012 Poznań Linguistic Meeting (PLM) features a thematic section on “Theory and evidence in language evolution research.” This section’s invited speaker was Jim Hurford, who is Emeritus Professor at Edinburgh University. Hurford is a very eminent figure in language evolution research and has published two very influential and substantive volumes on “Language in the Light of Evolution”: The Origins of Meaning (2007) and The Origins of Grammar (2011).

In his Talk, Hurford asked “What is wrong, and what is right, about current theories of language, in the light of evolution?” (you can find the abstract here).

Hurford presented two extreme positions on the evolution of language (which nevertheless are advocated by quite a number of evolutionary linguists) and then discussed what kinds of evidence and lines of reasoning support or seem to go against these positions.

Extreme position A, which basically is the Chomskyan position of Generative Grammar, holds that:

(1) There was a single biological mutation which (2) created a new unique cognitive domain, which then (3) immediately enabled the unlimited command of complex structures via the computational operation of merge. Further, according to this extreme position, (4) this domain is used primarily for advanced private thought and only derivatively for public communication and lastly (5) it was not promoted by natural selection.

On the other end of the spectrum there is extreme position B, which holds that:

(1) there were many cumulative mutations which (2) allowed the expanding interactions of pre-existing cognitive domains creating a new domain, which however is not characterized by principles unique to language. This then (3) gradually enabled the command of successively more complex structures. Also, on this view, this capacity was used primarily for public communication, and only derivatively for advanced private thought and was (5) promoted by natural selection.

Hurford then went on to discuss which of these individual points were more likely to capture what actually happened in the evolution of language.

He first looked at the debate over the role of natural selection in the evolution of language. In Generative Grammar there is a biological neurological mechanism or computational apparatus, called Universal Grammar (UG) by Chomsky, which determines what languages human infants could possibly acquire. In former Generative Paradigms, like the Government & Binding Approach of the 1980s, UG was thought to be extremely complex. What was more, some of these factors and structures seemed extremely arbitrary. Thus, from this perspective, it seemed inconceivable that they could have been selected for by natural selection. This is illustrated quite nicely in a famous quote by David Lightfoot:

“Subjacency has many virtues, but I am not sure that it could have increased the chances of having fruitful sex (Lightfoot 1991: 69)”

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PLM2012 Coverage: Dirk Geeraerts: Corpus Evidence for Non-Modularity

September 9, 2012 in Conference, Science, Uncategorized

The first plenary talk at this year’s Poznań Linguistic Meeting was by Dirk Geeraerts, who is professor of linguistics at the University of Leuven, Belgium.

In his talk, he discussed the possibility that corpus studies could yield evidence against the supposed modularity of language and mind endorsed by, for example, Generative linguists (you can find the abstract here)

Geeraerts began his talk by stating that there seems to be a paradigm shift in linguistics from an analysis of structure that is based on introspection to analyses of behaviour based on quantitative linguistic studies. More and more researchers are adopting quantified corpus-based analyses, which test hypotheses using statistical testing of language behaviour. As a data-set they use experimental data or large corpora. In his talk, he discussed the possibility that corpus studies could yield evidence against the supposed modularity of language and mind endorsed by, for example, Generative linguists (you can find the abstract here)

Multifactoriality

One further trend Geeraerts identified in this paradigm shift is that these kinds of analyses become more and more multifactorial in that they include multiple different factors which are both internal and external to language. Importantly, this way of doing linguistics is fundamentally different than the mainstream late 20th century view of linguistics.

What is important to note here when comparing this trend to other approaches to studying language is that multifactoriality goes against Chomsky’s idea of grammar as an ideal mental system that can be studied through introspection. In the traditional view, it is supposed that there is some kind of ideal language system which everyone has access to. This line of reasoning then justifies introspection as a method of studying the whole system of language and making valid generalizations about it. However, this goes against the emerging corpus linguistic view of language. On this view a random speaker is not representative for the linguistic community as a whole. The linguistic system is not homogenous across all speakers, and therefore introspection doesn’t suffice.

Modularity

The main thrust of Geeraerts’ talk was that research within this emerging paradigm also might call into question the assumption of the modularity of the mind (as advocated, for example by Jerry Fodor or Neil Smith): The view of the mind as a compartmentalized system consisting of discrete components or modules (for example, the visual system, language) plus a central processor.

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The power of diversity: New Scientist recognises the growing work on social structure and linguistic structure

December 14, 2011 in Uncategorized

A feature article in last week’s New Scientist asks why there is so much linguistic diversity present in the world, and what are the forces that drive it.  The article reads like a who’s who of the growing field of language structure and social structure:  Mark Pagel, Gary Lupyan, Quentin Atkinson, Robert Munroe, Carol and Melvin Ember, Dan Dediu and Robert Ladd, Stephen Levinson (click on the names to see some Replicated Typo articles about their work).  This is practically as close as my subject will come to having a pull-out section in Vanity Fair.  Furthermore, it recognises the weakening grip of Chomskyan linguistics.

Commentators have already gotten hung-up on whether English became simplified before or after spreading, but this misses the impact of the article:  There is an alternative approach to linguistics which looks at the differences between languages and recognises social factors as the primary source of linguistic change.  Furthermore, these ideas are testable using statistics and genetic methods.  It’s a pity the article didn’t mention the possibility of experimental approaches, including Gareth Roberts’ work on emerging linguistic diversity and work on cultural transmission using the Pictionary paradigm (Simon Garrod, Nick Fay, Bruno Gallantucci, see here and here).

David Robson (2011). Power of Babel: Why one language isn’t enough New Scientist, 2842 Online

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Advances in Visual Methods for Linguistics (AVML2012)

December 8, 2011 in Academia

Some peeps over the the University of York are organising a conference on the advances in visual methods for linguistics (AVML) to take place in September next year. This might be of interest to evolutionary linguists who use things like phylogenetic trees, networks, visual simulations or other fancy dancy visual methods. The following is taken from their website:

Linguistics, like other scientific disciplines, is centrally reliant upon visual images for the elicitation, analysis and presentation of data. It is difficult to imagine how linguistics could have developed, and how it could be done today, without visual representations such as syntactic trees, psychoperceptual models, vocal tract diagrams, dialect maps, or spectrograms. Complex multidimensional data can be condensed into forms that can be easily and immediately grasped in a way that would be considerably more taxing, even impossible, through textual means. Transforming our numerical results into graphical formats, according to Cleveland (1993: 1), ‘provides a front line of attack, revealing intricate structure in data that cannot be absorbed in any other way. We discover unimagined effects, and we challenge imagined ones.’ Or, as Keith Johnson succinctly puts it, ‘Nothing beats a picture’ (2008: 6).

So embedded are the ways we visualize linguistic data and linguistic phenomena in our research and teaching that it is easy to overlook the design and function of these graphical techniques. Yet the availability of powerful freeware and shareware packages which can produce easily customized publication-quality images means that we can create visual enhancements to our research output more quickly and more cheaply than ever before. Crucially, it is very much easier now than at any time in the past to experiment with imaginative and innovative ideas in visual methods. The potential for the inclusion of enriched content (animations, films, colour illustrations, interactive figures, etc.) in the ever-increasing quantities of research literature, resource materials and new textbooks being published, especially online, is enormous. There is clearly a growing appetite among the academic community for the sharing of inventive graphical methods, to judge from the contributions made by researchers to the websites and blogs that have proliferated in recent years (e.g. Infosthetics, Information is Beautiful, Cool Infographics, BBC Dimensions, or Visual Complexity).

In spite of the ubiquity and indispensability of graphical methods in linguistics it does not appear that a conference dedicated to sharing techniques and best practices in this domain has taken place before. This is less surprising when one considers that relatively little has been published specifically on the subject (exceptions are  Stewart (1976), and publications by the LInfoVisgroup). We think it is important that researchers from a broad spectrum of linguistic disciplines spend time discussing how their work can be done more efficiently, and how it can achieve greater impact, using the profusion of flexible and intuitive graphical tools at their disposal. It is also instructive to view advances in visual methods for linguistics from a historical perspective, to gain a greater sense of how linguistics has benefited from borrowed methodologies, and how in some cases the discipline has been at the forefront of developments in visual techniques.

The abstract submission deadline is the 9th January.

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Great Andamanese: The key to more than one linguistic puzzle?

November 9, 2011 in Uncategorized

Last week we had a lecture from Anvita Abbi on rare linguistic structures in Great Andamanese – a language spoken in the Andaman Islands.  The indigenous populations of the Andaman Islands lived in isolation for tens of thousands of years until the 19th Century, but still exhibit some common features of south-east Asian languages such as retroflex consonants.  This could be evidence for the migration route of humans from India to Australia.  Indeed, recent genetic research has shown that the Andamanese are descendants of the first human migration from Africa in the Palaeolithic, though Abbi suggested that the linguistic evidence is also a strong marker of human migration and an “important repository of our shared human history and civilization”.

Although the similarities are fascinating for studies of cultural evolution, the rarity of some structures in Great Andamanese are even more intriguing.

The Andaman Islands

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Tea Leaves and Lingua Francas: Why the future is not easy to predict

October 27, 2011 in Linguistics, Science

We all take comfort in our ability to project into the future. Be it through arbitrary patterns in Spring Pouchong tea leaves, or making statistical inferences about the likelihood that it will rain tomorrow, our accumulation of knowledge about the future is based on continued attempts of attaining certainty: that is, we wish to know what tomorrow will bring. Yet the difference between benignly staring at tea leaves and using computer models to predict tomorrow’s weather is fairly apparent: the former relies on a completely spurious relationship between tea leaves and events in the future, whereas the latter utilises our knowledge of weather patterns and then applies this to abstract from currently available data into the future. Put simply: if there are dense grey clouds in the sky, then it is likely we’ll get rain. Conversely, if tea-leaves arrange themselves into the shape of a middle finger, it doesn’t mean you are going to be continually dicked over for the rest of your life. Although, as I’ll attempt to make clear below, these are differences in degrees, rather than absolutes.

So, how are we going to get from tea-leaves to Lingua Francas? Well, the other evening I found myself watching Dr Nicholas Ostler give a talk on his new book, The Last Lingua Franca: English until the Return to Babel. For those of you who aren’t familiar with Ostler, he’s a relatively well-known linguist, having written several successful books popularising socio-historical linguistics, and first came to my attention through Razib Kahn’s detailed review of Empires of the Word. Indeed, on the basis of Razib’s post, I was not surprised by the depth of knowledge expounded during the talk. On this note alone I’m probably going to buy the book, as the work certainly filters into my own interests of historical contact between languages and the subsequent consequences. However, as you can probably infer from the previous paragraph, there were some elements I was slightly-less impressed with — and it is here where we get into the murky realms between tea-leaves and knowledge-based inferences. But first, here is a quick summary of what I took away from the talk:

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Cultural differences in lateral transmission: Phylogenetic trees are OK for Linguistics but not biology

October 5, 2011 in Uncategorized

The three areas under analysis

An article in PLos ONE debunks the myth that hunter-gatherer societies borrow more words than agriculturalist societies. In doing so, it suggests that horizontal transmission is low enough for phylogenetic analyses to be a valid linguistic tool.

Lexicons from around 20% of the extant languages spoken by hunter-gatherer societies were coded for etymology (available in the supplementary material). The levels of borrowed words were compared with the languages of agriculturalist and urban societies taken from the World Loanword Database.  The study focussed on three locations:  Northern Australia, northwest Amazonia, and California and the Great Basin.

In opposition to some previous hypotheses, hunter-gatherer societies did not borrow significantly more words than agricultural societies in any of the regions studied.

The rates of borrowing were universally low, with most languages not borrowing more than 10% of their basic vocabulary.  The mean rate for hunter-gatherer societies was 6.38% while the mean for 5.15%.  This difference is actually significant overall, but not within particular regions.  Therefore, the authors claim, “individual area variation is more important than any general tendencies of HG or AG languages”.

Interestingly, in some regions, mobility, population size and population density were significant factors.  Mobile populations and low-density populations had significantly lower borrowing rates, while smaller populations borrowed proportionately more words.  This may be in line with the theory of linguistic carrying capacity as discussed by Wintz (see here and here).  The level of exogamy was a significant factor in Australia.

The study concludes that phylogenetic analyses are a valid form of linguistic analysis because the level of horizontal transmission is low.  That is, languages are tree-like enough for phylogenetic assumptions to be valid:

“While it is important to identify the occasional aberrant cases of high borrowing, our results support the idea that lexical evolution is largely tree-like, and justify the continued application of quantitative phylogenetic methods to examine linguistic evolution at the level of the lexicon. As is the case with biological evolution, it will be important to test the fit of trees produced by these methods to the data used to reconstruct them. However, one advantage linguists have over biologists is that they can use the methods we have described to identify borrowed lexical items and remove them from the dataset. For this reason, it has been proposed that, in cases of short to medium time depth (e.g., hundreds to several thousand years), linguistic data are superior to genetic data for reconstructing human prehistory “

Excellent – linguistics beats biology for a change!

However, while the level of horizontal transmission might not be a problem in this analysis, there may be a problem in the paths of borrowing.  If a language borrows relatively few words, but those words come from many different languages, and may have many paths through previous generations, there may be a subtle effect of horizontal transition that is being masked.  The authors acknowledge that they did not address the direction of transmission in a quantitative way.

A while ago, I did study of English etymology trying to quantify the level of horizontal transmission through time (description here).  The graph for English doesn’t look tree-like to me, perhaps the dynamics of borrowing works differently for languages with a high level of contact:

Claire Bowern, Patience Epps, Russell Gray, Jane Hill, Keith Hunley, Patrick McConvell, Jason Zentz (2011). Does Lateral Transmission Obscure Inheritance in Hunter-Gatherer Languages? PLoS ONE, 6 (9) : doi:10.1371/journal.pone.0025195

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Cognitivism and the Critic 2: Symbol Processing

July 14, 2011 in Linguistics, Modelling

It has long been obvious to me that the so-called cognitive revolution is what happened when computation – both the idea and the digital technology – hit the human sciences. But I’ve seen little reflection of that in the literary cognitivism of the last decade and a half. And that, I fear, is a mistake.

Thus, when I set out to write a long programmatic essay, Literary Morphology: Nine Propositions in a Naturalist Theory of Form, I argued that we think of literary text as a computational form. I submitted the essay and found that both reviewers were puzzled about what I meant by computation. While publication was not conditioned on providing such satisfaction, I did make some efforts to satisfy them, though I’d be surprised if they were completely satisfied by those efforts.

That was a few years ago.

Ever since then I pondered the issue: how do I talk about computation to a literary audience? You see, some of my graduate training was in computational linguistics, so I find it natural to think about language processing as entailing computation. As literature is constituted by language it too must involve computation. But without some background in computational linguistics or artificial intelligence, I’m not sure the notion is much more than a buzzword that’s been trendy for the last few decades – and that’s an awful long time for being trendy.

I’ve already written one post specifically on this issue: Cognitivism for the Critic, in Four & a Parable, where I write abstracts of four texts which, taken together, give a good feel for the computational side of cognitive science. Here’s another crack at it, from a different angle: symbol processing.

Operations on Symbols

I take it that ordinary arithmetic is most people’s ‘default’ case for what computation is. Not only have we all learned it, it’s fundamental to our knowledge, like reading and writing. Whatever we know, think, or intuit about computation is built on our practical knowledge of arithmetic.

As far as I can tell, we think of arithmetic as being about numbers. Numbers are different from words. And they’re different from literary texts. And not merely different. Some of us – many of whom study literature professionally – have learned that numbers and literature are deeply and utterly different to the point of being fundamentally in opposition to one another. From that point of view the notion that literary texts be understood computationally is little short of blasphemy.

Not so. Not quite.

The question of just what numbers are – metaphysically, ontologically – is well beyond the scope of this post. But what they are in arithmetic, that’s simple; they’re symbols. Words too are symbols; and literary texts are constituted of words. In this sense, perhaps superficial, but nonetheless real, the reading of literary texts and making arithmetic calculations are the same thing, operations on symbols. Read the rest of this entry →

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Statistics and Symbols in Mimicking the Mind

June 4, 2011 in Linguistics

MIT recently held a symposium on the current status of AI, which apparently has seen precious little progress in recent decades. The discussion, it seems, ground down to a squabble over the prevalence of statistical techniques in AI and a call for a revival of work on the sorts of rule-governed models of symbolic processing that once dominated much of AI and its sibling, computational linguistics.

Briefly, from the early days in the 1950s up through the 1970s both disciplines used models built on carefully hand-crafted symbolic knowledge. The computational linguists built parsers and sentence generators and the AI folks modeled specific domains of knowledge (e.g. diagnosis in elected medical domains, naval ships, toy blocks). Initially these efforts worked like gang-busters. Not that they did much by Star Trek standards, but they actually did something and they did things never before done with computers. That’s exciting, and fun.

In time, alas, the excitement wore off and there was no more fun. Just systems that got too big and failed too often and they still didn’t do a whole heck of a lot.

Then, starting, I believe, in the 1980s, statistical models were developed that, yes, worked like gang-busters. And these models actually did practical tasks, like speech recognition and then machine translation. That was a blow to the symbolic methodology because these programs were “dumb.” They had no knowledge crafted into them, no rules of grammar, no semantics. Just routines the learned while gobbling up terabytes of example data. Thus, as Google’s Peter Norvig points out, machine translation is now dominated by statistical methods. No grammars and parsers carefully hand-crafted by linguists. No linguists needed.

What a bummer. For machine translation is THE prototype problem for computational linguistics. It’s the problem that set the field in motion and has been a constant arena for research and practical development. That’s where much of the handcrafted art was first tried, tested, and, in a measure, proved. For it to now be dominated by statistics . . . bummer.

So that’s where we are. And that’s what the symposium was chewing over.

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