Replicated Typo

Altitude and Ejectives: contact and population size

On the weekend I did an analysis about a recent paper by Caleb Everett linking altitude to the presence of ejective sounds in a langauge. In this post I look at the possible effects of contact and population size. I find that controlling for population size removes the significance of the link between ejectives and elevation.

In a comment on the post, Chris Lucas suggested that languages at higher altitudes might be more isolated, and so less subject to contact-induced change:

“contact tends to make languages lose ejectives, if they ever had them. The reasoning here would be that a language’s having (contrastive) ejectives implies that it has a large consonant inventory, which implies that it does not have a history of significant numbers of people having learnt it as a second language, since this tends to lead to the elimination of typologically rare features.”

We can test this in the following way: We can get a rough proxy for langauge contact for a community by counting the number of languages within 150km (range between 0 and 44). If we run a phylogenetic genralised least squares test, predicting the presence of ejectives by elevation and number of surrounding languages, we get the following result (estimated lambda = 0.8169142 , df= 491, 489):

	Coefficient	Std.Error	t-value	p-value
Elevation	0.00004514	0.00001655	2.728177	0.0066 **
No. surrounding langs	-0.00312549	0.00147532	-2.118507	0.0346 *

While elevation is still significant, the number of surrounding languages is also a significant predictor (the effect size is also greater). The greater the number of surrounding languages, the smaller the chance of a langauge having a ejectives. This fits with the idea that contact induced change removes ejectives, rather than air pressure being the only cause. In the graph below, I’ve plotted the mean elevation for languages with and without ejectives, comparing languages with a neighbour within 150km and languages without a neighbour in 150km. The effect is stronger in the group with neighbouring languages (right), which would fit with languages loosing ejectives due to contact.

However, it’s not quite so simple, since we have to take into account the relative relatedness of languages. If we count the number of distinct language families within 150km, then the significance goes away:

	Coefficient	Std.Error	t-value	p-value
Elevation	0.00003893	0.00001645	2.366329	0.0184 *
No. surrounding families	0.00348498	0.0074656	0.466806	0.6408

What about another proxy for contact, like population size (as used by Lupyan & Dale, 2010)? I took speaker populations from the Ethnologue and ran another PGLS:

	Value	Std.Error	t-value	p-value
Elevation	0.00001786	0.00001962	0.910439	0.3632
Log population	-0.0110823	0.00817542	-1.355564	0.1761

Now we see that neither variable is significant, though larger populations tend not to have ejectives. That is, by controlling for linguistic descent and population size, the correlation between elevation and ejectives goes away.

In fact, a simple logit regression predicting elevation by elevation and log population results in the following:

	Estimate	Std.error	z-value	Pr(>\|z\|)
(Intercept)	-0.6579524	0.3874394	-1.698	0.089469 .
Elevation	0.0003757	0.0001665	2.257	0.024014 *
Log population	-0.327502	0.0920464	-3.558	0.000374 **

We can see that, even if we don’t control for phylogeny, population size is a better predictor of ejectives than elevation (although Everett uses several measures of altitude).

I also wondered if the distance to the nearest language could be a proxy for contact. Let’s put all the variables into one regression.

	Value	Std.Error	t-value	p-value
Elevation	0.0000257	0.00001988	1.290864	0.1976
No. surrounding languages	-0.0069331	0.00278402	-2.490329	0.0132 *
Minimum distance to nearest language	-0.0001173	0.00011222	-1.04528	0.2966
Log population	-0.011234	0.0081391	-1.380251	0.1684

Here we see that the number of surrounding languages is still a significant predictor of the presence of ejectives (although using the number of surrounding families doesn’t work), but elevation is not.

We can build the most likely causal graph (see my post here) for the data above. This ignores the phylogenetic relatedness of langauges, but allows us to explore more complex relationships between all the variables. Below, we see that elevation and ejectives are still linked, as Everett would predict.

The stats I’ve presented here are just rough explorations of the data, not proof or disproof of any theory. Here are some issues that are still unresolved:

What about the distance from high-elevation areas, as used in Everett’s paper?
Are the proxies above reasonable?
What is the likelihood of keeping ejectives versus losing them during contact?
In the analyses above, I’m not controlling for geographical relatedness, this could be done by selecting independent samples or Mantel tests.
There are links between phoneme inventory size, the geographic area a langauge covers, morphology and demography (see James’ posts here and here). What is the best way to approach the complex relationships between these features?

Of course, laboratory experiments or careful idographic work could address these issues better than more statistics.

Culture Memes Information WTF!

I’ve been thinking a lot about information recently, mostly as a consequence of reading Dan Dennett on memetics. I’m uncomfortable with his usage, and similar ones, and I can’t quite figure out why. Let me offer two passages, and then some comments.

The first passage is from George Williams, a biologist. It’s in a chapter from a book edited by John Brockman, The Third Culture: Beyond the Scientific Revolution:

Evolutionary biologists have failed to realize that they work with two more or less incommensurable domains: that of information and that of matter. I address this problem in my 1992 book, Natural Selection: Domains, Levels, and Challenges. These two domains will never be brought together in any kind of the sense usually implied by the term “reductionism.” You can speak of galaxies and particles of dust in the same terms, because they both have mass and charge and length and width. You can’t do that with information and matter. Information doesn’t have mass or charge or length in millimeters. Likewise, matter doesn’t have bytes. You can’t measure so much gold in so many bytes. It doesn’t have redundancy, or fidelity, or any of the other descriptors we apply to information. This dearth of shared descriptors makes matter and information two separate domains of existence, which have to be discussed separately, in their own terms.

The gene is a package of information, not an object. The pattern of base pairs in a DNA molecule specifies the gene. But the DNA molecule is the medium, it’s not the message. Maintaining this distinction between the medium and the message is absolutely indispensable to clarity of thought about evolution.

Just the fact that fifteen years ago I started using a computer may have had something to do with my ideas here. The constant process of transferring information from one physical medium to another and then being able to recover that same information in the original medium brings home the separability of information and matter. In biology, when you’re talking about things like genes and genotypes and gene pools, you’re talking about information, not physical objective reality. They’re patterns.

I was also influenced by Dawkins’ “meme” concept, which refers to cultural information that influences people’s behavior. Memes, unlike genes, don’t have a single, archival kind of medium. Consider the book Don Quixote: a stack of paper with ink marks on the pages, but you could put it on a CD or a tape and turn it into sound waves for blind people. No matter what medium it’s in, it’s always the same book, the same information. This is true of everything else in the cultural realm. It can be recorded in many different media, but it’s the same meme no matter what medium it’s recorded in.

It seems to me that that is more or less how the concept of information is used in many discussions. It’s certainly how Dennett tends to use it. Here’s a typical passage (it’s the fifth and last footnote in From Typo to Thinko: When Evolution Graduated to Semantic Norms):

There is considerable debate among memeticists about whether memes should be defined as brain-structures, or as behaviors, or some other presumably well-anchored concreta, but I think the case is still overwhelming for defining memes abstractly, in terms of information worth copying (however embodied) since it is the information that determines how much design work or R and D doesn’t have to be re-done. That is why a wagon with spoked wheels carries the idea of a wagon with spoked wheels as well as any mind or brain could carry it.

Here I can’t help but think that Dennett’s pulling a fast one. Information has somehow become reified in a way that has the happy effect of relieving Dennett of the task of thinking about the actual mechanisms of cultural evolution. That in turn has the unhappy effect of draining his assertion of meaning. In what way does a wagon with spoked wheels carry any idea whatsoever, much less the idea of itself? Continue reading “Culture Memes Information WTF!”

Systematic reviews 101: How to phrase your research question

keep-calm-and-formulate-your-research-question — Image from the JEPS Bulletin

As promised, and first thing’s first, when writing a systematic review, how should we phrase our research question? This is useful when phrasing questions for individual studies too.

PICO is a useful mnemonic for building research questions in clinical science:

Patient group
Intervention
Comparison/Control group
Outcome measures

How does this look in practice?

What is the effect of [intervention] on [outcome measure] in [patient group] (compared to [control group])?

How can we make this more applicable for language evolution?

I guess we can change the mnemonic:

Population (either whole language populations in large scale studies, small sample populations either in the real world or under a certain condition in a laboratory experiment, or a population of computational or mathematical agents or population proxy)

Intervention
Comparison/Control group
Outcome measures

Here are some examples of what this might look like using language evolution research:

What is the effect of [L2 speakers] on [morphological complexity] in [large language populations] compared to [small language populations]?

What is the effect of [speed of cultural evolution] on [the baldwin effect] in [a population of baysian agents]?

What is the effect of [iterated learning] on [the morphosyntactic structure in an artificial language] in [experimental participants]?

What is the effect of [communication] on the [distribution of vowels] in [a population of computational agents]?

All of the above are good research questions for individual studies, but I’m not sure it would be possible to do a review on any of the above research questions simply because there is not enough studies, and even when studies have investigated the same intervention and outcome measure, they haven’t used the same type of population.

In clinical research the same studies are done again and again, with the same disease, intervention and population. This makes sense as one study does not necessarily create enough evidence to risk people’s lives on the results. We don’t have this problem in language evolution (thank god), however I feel we may suffer from a lack of replication of studies. There has been quite a lot of movement recently (see here) to make replication of psychological experiments encouraged, worthwhile and publishable. It is also relatively easy to replicate computational modelling work, but the tendency is to change the parameters or interventions to generate new (and therefore publishable) findings. And real world data is a problem because we end up analysing the same database of languages over and over again. However, I suppose controlling for things like linguistic family, and therefore treating each language family as its own study, in a way, is a sort of meta-analysis of natural replications.

I’m not sure there’s an immediate solution to the problems I’ve identified above, and I’m certainly not the first person to point them out, but thinking carefully about your research question before starting to conduct a review is very useful and excellent practice, and you should remember that when doing a systematic review, the narrower your research question, the easier, more thorough and complete your review will be.

Altitude and Ejectives: Hypotheses up in the air

A recent paper in PLOS ONE by Caleb Everett looks at whether geography can affect phoneme inventories. Everett finds that language communities that live at higher altitudes are more likely to have ejective sounds in their phoneme inventories. One of Everett’s hypotheses is that the lower air pressure at higher altitudes makes ejectives easier to produce, and drier climates at higher altitudes “may help to mitigate rates of water vapor loss through exhaled air”. While I don’t have anything against this kind of theory in principle, and I’m not going to comment on the plausibility of this theory, I wanted to check whether the stats held up.

This sounds suspiciously like one of our spurious correlations – links between cultural features that come about by accidents of cultural history rather than being causally related. Although Everett notes that the tests he uses include languages from many language families, there’s no real control for historical descent. James and I have also submitted a paper to PLOS ONE about this phenomenon more generally, and we suggest a few statistical tests that should be applied to this kind of claim. These include comparing the correlation of the variables of interest with similar variables that you don’t think are related, and controlling for historical descent by using, for example, phylogenetic generalised least squares. In this post, I apply these tests.

First, I test whether the link between ejectives and elevation is stronger than the link between elevation and many other linguistic features. I ran a correlation for each variable in the WALS database. Elevation (altitude) does indeed significantly predict the presence of ejectives. Surprisingly, only 2 other variables resulted in stronger predictors of elevation. That is, the presence of ejectives is in the top 1.4% of variables for predicting elevation. The presence of ejectives resulted in a correlation that was significantly stronger than 94.4% of variables (above 1.98 standard deviations). This is surprisingly good news for Everett!

Below is a histogram of the results (F-score of the model fit), with a red line indicating the strength of the ejectives variable :

The linguistic variables that gave better results than ejectives were the Order of Object and Verb and the Relationship between the Order of Object and Verb and the Order of Adjective and Noun. I can’t think of a good reason that these would be linked. See below:

The next test involved controlling for common descent of languages. I built a phylogenetic tree from the linguistic classifications from the Ethnologue. We’re predicting elevation (continuous) given the presence of ejectives (discrete), so we’ll use a phylogenetic generalised least squares test (you can learn more about doing this at the excellent tutorials by Charles Nunn and others, here). This weights the observations by how related they are, given a particular model of trait evolution. The elevation variable has a strong phylogenetic signal (Pagel’s lambda = 0.3, sig. > 0, p<0.00001; sig. different from 1, p<0.00001), so we’ll use Pagel’s covarience matrix.

Surprisingly, the correlation holds up, even when controlling for phylogeny (491 languages, df = 419, residual df = 489, estimated lambda = 0.2787271, coef = 358.9542, t = 3.51, p = 0.0005). Edit: If you use ejectives as the dependent variable, the result is similar (estimated lambda = 0.8169142, coef = 0.00003975, t = 2.42, p = 0.0157).

I’d like to make two points: First, this kind of analysis is easy to do, and makes the test more rigorous (I did the above analyses at Singapore airport). Secondly, while the stats might hold up, this kind of approach can only point towards future research, rather than supplying definitive proof of the hypothesis. It’s an interesting proposal, and I look forwards to some modelling or experimental evidence.

EDIT:

The phylogenetic tree assumed languages within families evolved over 6,000 years and there was a common ancestor for all language families 60,000 years ago. You can see a diagram of the tree here, with WALS codes.

The altitude data I used comes from the 90-meter NASA database (SRTM3), extracted using the GPS Visualiser, while Everett uses surveys by Google Earth and ArcGIS v. 10.0. I checked some points and there are very slight differences in the order of a few meters.

Systematic reviews 101: Systematic reviews vs. Narrative reviews

Last week I went to a workshop on writing systematic reviews run by SYRCLE. The main focus of this workshop, and indeed the main focus within most of the literature on systematic reviews, is on clinical and preclinical research. However, I think that other disciplines can really benefit from some of the principles of systematic reviewing, so I thought I’d write a quick series on how to improve the scientific rigor of writing reviews within the field of language evolution.

So first thing’s first, what is a systematic review? A systematic review is defined (by the Centre for Reviews and Dissemination at the University of York) as “a review of the evidence on a clearly formulated question that uses systematic and explicit methods to identify, select and critically appraise relevant primary research, and to extract and analyse data from the studies that are included in the review.”

This is in contrast to more narrative or literature reviews, more traditionally seen in non-medical disciplines. Reviews within language evolution are usually authored by the main players in the field and are generally on a very broad topic, they use informal, unsystematic and subjective methods to search for, collect and interpret information, which is often summarised with a specific hypothesis in mind, and without critical appraisal, and summarised with an accompanying convenient narrative. Though these narrative reviews are often conducted by people with expert knowledge of their field, it may be the case that this expertise and experience may bias the authors. Narrative reviews are, by definition, arguably not objective in assessing the literature and evidence, and therefore not good science. Some are obviously more guilty than others, and I’ll let you come up with some good examples in the comments.

So how does one go about starting a systematic review, either as a stand alone paper or as part of a wider thesis?

Systematic reviews require the following steps:

1. Phrase the research question

2. Define in- and exclusion criteria (for literature search)

3. Search systematically for all original papers

4. Select relevant papers

5. Assess study quality and validity

6. Extract data

7. Analyse data (with a meta-analysis if possible)

8. Interpret and present data

In the coming weeks I will write posts on how to phrase the research question of your review, tips on searching systematically for relevant studies, how to assess the quality and validity of the papers and studies you wish to cover, and then maybe a post on meta-analysis (though this is going to be difficult with reference to language evolution because of its multidisciplinary nature and diversity within the relevant evidence, I’ll have a good think about it)

References

Undertaking Systematic Reviews of Research on Effectiveness. CRD’s Guidance for those Carrying Out or Commissioning Reviews. CRD Report Number 4 (2nd Edition). NHS Centre forReviews and Dissemination, University of York. March 2001.

Greater learnability is not sufﬁcient to produce cultural universals

I always feel the need to mention these cultural learning in the lab papers when they pop up.

This one, by Rafferty, Griffiths & Ettlinger, to appear in Cognition, uses an iterated learning experiment to challenge the idea that tendencies across cultures is the result of some structures and concepts being easier to learn than others, as things being easier to learn means they will be more accurately transmitted from one generation to the next. Mini artificial languages in iterated paradigms (most notably Kirby, Cornish & Smith, 2008), have shown that languages become more structured as the result of generational turnover (and with an added pressure for expressivity), and this is hypothesised to be because of pressures for learnability (as well as expressivity/communication).

If we can show empirically that cultural features which are more prevalent are more “learnable”, than this adds extra weight to the hypothesis that the driving force because culturally universal concepts are the result of learnability. However, this paper finds the opposite, if a concept is more learnable, then that does not necessarily result in it being more prevalent in transmission chains.

Their first argument is that more learnable cultural features are not likely to be (re)produced in transmission failure. This was shown in an experiment which featured “distinctive items”, such as the word “Elephant” on a shopping list. In this context, the word “Elephant” was much more likely to be remembered than other items on a list, but once it had been lost in a transmission chain, it was never regenerated. Participants were much more likely to regenerate mundane food items which are likely to feature on a shopping list, such as “apple”.

They also showed this mathematically, showing that agents are more likely to arrive at H2 if they learn from an agent with H2, even if H1 is more learnable. This is based on the assumptions that learners rarely learn a particular hypothesis unless they receive data generated speciﬁcally from that hypothesis, less learnable hypotheses are more likely to be confused with one another and so will arise more often through transmission, and that learnable hypotheses are unlikely to arise as the result of transmission errors, just like the word “elephant”.

Continue reading “Greater learnability is not sufﬁcient to produce cultural universals”

Watch Out, Dan Dennett, Your Mind’s Changing Up on You!

I want to look at two recent pieces by Daniel Dennett. One is a formal paper from 2009, The Cultural Evolution of Words and Other Thinking Tools (Cold Spring Harbor Symposia on Quantitative Biology, Volume LXXIV, pp. 1-7, 2009). The other is an informal interview from January of 2013, The Normal Well-Tempered Mind. What interests me is how Dennett thinks about computation in these two pieces.

In the first piece Dennett seems to be using the standard-issue computational model/metaphor that he’s been using for decades, as have others. This is the notion of a so-called von Neumann machine with a single processor and a multi-layer top-down software architecture. In the second and more recent piece Dennett begins by asserting that, no, that’s not how the brain works, I was wrong. At the very end I suggest that the idea of the homuncular meme may have served Dennett as a bridge from the older to the more recent conception.

Words, Applets, and the Digital Computer

As everyone knows, Richard Dawkins coined the term “meme” as the cultural analogue to the biological gene, or alternatively, a virus. Dennett has been one of the most enthusiastic academic proponents of this idea. In his 2009 Cold Spring Harbor piece Dennett concentrates his attention on words as memes, perhaps the most important class of memes. Midway through the paper tells he us that “Words are not just like software viruses; they are software viruses, a fact that emerges quite uncontroversially once we adjust our understanding of computation and software.”

Those first two phrases, before the comma, assert a strong identification between words and software viruses. They are the same (kind of) thing. Then Dennett backs off. They are the same, providing of course, that “we adjust our understanding of computation and software.” Just how much adjusting is Dennett going to ask us to do?

This is made easier for our imaginations by the recent development of Java, the software language that can “run on any platform” and hence has moved to something like fixation in the ecology of the Internet. The intelligent composer of Java applets (small programs that are downloaded and run on individual computers attached to the Internet) does not need to know the hardware or operating system (Mac, PC, Linux, . . .) of the host computer because each computer downloads a Java Virtual Machine (JVM), designed to translate automatically between Java and the hardware, whatever it is.

The “platform” on which words “run” is, of course, the human brain, about which Dennett says nothing beyond asserting that it is there (a bit later). If you have some problems about the resemblance between brains and digital computers, Dennett is not going to say anything that will help you. What he does say, however, is interesting.

Notice that he refers to “the intelligent composer of Java applets.” That is, the programmer who writes those applets. Dennett knows, and will assert later on, that words are not “composed” in that way. They just happen in the normal course of language use in a community. In that respect, words are quite different from Java applets. Words ARE NOT explicitly designed; Java applets ARE. Those Java applets seem to have replaced computer viruses in Dennett’s exposition, for he never again refers to them, though they (viruses) figured emphatically in the topic sentence of this paragraph.

The JVM is “transparent” (users seldom if ever encounter it or even suspect its existence), automatically revised as needed, and (relatively) safe; it will not permit rogue software variants to commandeer your computer.

Computer viruses, depending on their purpose, may also be “transparent” to users, but, unlike Java applets, they may also commandeer your computer. And that’s not nice. Earlier Dennett had said:

Our paradigmatic memes, words, would seem to be mutualists par excellence, because language is so obviously useful, but we can bear in mind the possibility that some words may, for one reason or another, flourish despite their deleterious effects on this utility.

Perhaps that’s one reason Dennett abandoned his talk of computer viruses in favor of those generally helpful Java applets. Continue reading “Watch Out, Dan Dennett, Your Mind’s Changing Up on You!”

Ways To Protolanguage 3 Conference

Today is the first day of the “Ways to Protolanguage 3” conference. which takes place on 25–26 May in in Wrocław, Poland. The Plenary speakers are Robin Dunbar, Joesp Call, and Peter Gärdenfors

Both Hannah and I are at the conference and we’re also live-tweeting about the conference using the hashtag #protolang3

Hannah’s just given her talk

Jack J. Wilson, Hannah Little (University of Leeds, UK; Vrije Universiteit Brussel, Belgium) – Emerging languages in esoteric and exoteric niches: evidence from rural sign languages (abstract here)

And I’m due tomorrow.

Michael Pleyer (Heidelberg University, Germany) – Cooperation and constructions: looking at the evolution of language from a usage-based and construction grammar perspective (abstract here)

The Programme can be found here: (Day 1 / Day 2)

Roles in Cultural Selection: Replicators, Interactors, and Beneficiaries, or, Where’s the Memes?

Once again, cultural evolution, and the problem of memes: What are they? Where are they? What do they do? While the general case does interest me, culture is so various that it is impossible to think about it directly. One has to think about specific cases. As details are important, I want to choose a fairly specific case, that of jazz in mid-20th-Century America. I want you to imagine that you’re in a jazz club in, say, Philadelphia, in, say, mid-October of 1952. It’s 1:30 in the morning, and the tune is Charlie Parker’s “Dexterity.” The piano player counts it off–ah one, ah two, one two three four…

But we’re getting ahead of ourselves. We need a little conceptual equipment before considering the example. It’s the conceptual equipment that’s in question. Make no mistake, the concept of memes is conceptual equipment, and it’s confused and confusing.

Roles in Cultural Selection

Genes and phenotypes play certain roles in a more or less standard account of biological evolution. The phenotype interacts with the environment, where it either succeeds or fails at reproduction, depending on the “fit” between its traits and that environment. Where the phenotype is successful at reproduction, it is the genes which are said to carry heredity from one generation to the next.

In one very widespread account genes are said to be replicators. That is to say, replication is the role they play in evolutionary change. Here’s what Peter Godfrey-Smith has to say about that (The Replicator in Retrospect, Biology and Philosophy 15 (2000): 403-423.):

In The Selfish Gene (1976), Richard Dawkins had argued that individual genes must be seen as the units of selection in evolutionary processes within sexual populations. This is primarily because the other possible candidates, notably whole organisms and groups, do not “replicate.” Organisms and groups are ephemeral, like clouds in the sky or dust storms in the desert. Only a replicator, which can figure in selective processes over many generations, can be a unit of selection.

At the same time Dawkins coined the term “meme” to name entities filling the replicator role in cultural evolution. Later on he used the term “vehicle” to designate the entity that interacts with the environment. In biological evolution it is phenotypes that are the vehicles. In cultural evolution, well, that’s a matter of some dispute. And that more general dispute–what are the roles in cultural evolution and what kinds of things occupy them?–is what interests me.

However, I don’t particularly like the term “vehicle.” As Godfrey-Smith has noted, following others, it is a gene-centric term, characterizing what entities do from the so-called “gene’s eye” perspective. I’d prefer a more neutral perspective and so will use a term coined by Richard Hull, “interactor.” Here are definitions as Godfrey-Smith gives them:

Replicator: an entity that passes on its structure largely intact in successive replications.
Interactor: an entity that interacts as a cohesive whole with its environment in such a way that this interaction causes replication to be differential. Continue reading “Roles in Cultural Selection: Replicators, Interactors, and Beneficiaries, or, Where’s the Memes?”

The myth of linguistic diversity

There was a debate today between Peter Hagoort and Stephen Levinson on ‘The Myth of Linguistic Diversity”. Hagoort arguing the case for universalist accounts. He admitted that language does exhibit a large amount of diversity, but that this diversity is constrained. He argued that linguistics should be interested in which universal mechanisms explain the boundary conditions for linguistic diversity. The most likely domain in which to find these mechanisms is the brain. It comes with internal structure that defines the boundary conditions on the surface structures of human behaviours. These boundary conditions include the learnability of input, and that language is processed incrementally and under time constraints. Brains operate under these constraints so that linguistic processing of all languages happens in roughly the same processing stages. Hagoort argued that proponents of a diversity approach to linguistics think that variation is unbounded or constrained only by culture. While there is variation between individuals and between languages, it is the general types that we should be focussed on.

In contrast, Levinson suggested that we should be moving away from the picture of the modal individual with a fixed language architecture. Instead, we should embrace population thinking and recognise the variation inherent at every level of language from typology to processing and brain structures. While languages are constrained by the processing structures of the brain, these processing structures are plastic and adapt to the language and cultures in which they are embedded. Adults lose the ability to distinguish sounds that are not part of their language. Recent work on linguistic planning using eye-tracking shows that the elements of a scene that speakers attend to before starting to speak differs with the canonical word order of their language. More fundamentally, brain structures can be affected by cultural experience, such as bilingualism or singing (indeed, the effect of bilingualism on processing shows that variation itself is a fundamental constraint). So, brains do constrain learning and processing, but are themselves subject to constraints from interaction between individuals. Brains also change over evolutionary time, adapting to a range of pressures. Therefore, there is a complex ecology of systems that co-evolve to define the constraints on language, and understanding these systems requires focussing on diversity.

Hagoort conceded that there was impressive variation at each level, but wondered what was meant by “fundamental” differences. For instance, how important is the precise neural architecture of an individual? Even within the variation pointed out, complex linguistic processing isn’t being done in the thalamus, and this is a constraint that sets a boundary on variation. Hagoort might have pointed out that, if there was so much variation between individuals, how do they communicate so effectively and how does basic interaction happen so easily between diverse individuals? This points to brain processing universals that explain the constraints on language.

Both sides agreed that the basic aim of any science, including linguistics, is to discover general principles that explain the data. However, are researchers focussing on the same data? What is the object of study that linguistics are trying to find generalisations for? It seems to me that the debate came down to what each proponent thought was the domain that was most likely to yield general explanations. Hagoort suggests that we should be focussed on brain structures and processing in the individual. Levinson, on the other hand, suggests that the interaction between individuals is a key domain (e.g. the interaction engine). Proponents of cultural evolution such as Simon Kirby might argue that cultural transmission is a key domain. It’s possible that the most relevant ‘universals’ in each of these domains may be very different. A constructive step would be to describe how each of these domains constrain the other. For instance, constraints on language processing in the brain certainly constrain interaction between individuals, but the requirements of interaction may affect how processing is employed.

There were some good points from the floor, including Peter Seuren pointing out that neither view was particularly close to proving their point, since proving universals, or their absence is very difficult. A paper under review by Steven Piantadosi and Edward Gibson attempts to answer whether it is possible in principle or practice to amass sufficient evidence for a statistical test that would demonstrate a universal. They conclude that it is possible in principle, but that there are not enough datapoints (languages) in order to achieve the required statistical power. There was also an appeal for the study of diversity for the sake of diversity – that there are different motivations for explaining phenomena in the world, and that one of them is to understand human diversity.

The general message: Proponents of universals need to take diversity into account, and proponents of diversity need to be more specific about how diversity maps onto processing and how different domains of language co-evolve.