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.

It’s the unwritten rule of every talk on cultural evolution:  there must be at least one picture of someone whispering into someone else’s ear.  This represents language being passed on from one generation to the next, with the language possibly changing (like in the child’s game broken telephone or chinese whispers).  This classic image often makes an appearance:

However, most are boring old stock images.  So, I’m setting a challenge:  who can find the most awesome ‘broken telephone’ picture?

This is my submission:

Image by Craig Barritt / Getty Images, found at The 45 Most Legendary Pictures Ever Taken.

A paper from the Berkeley economic history laboratory published online last week finds a correlation between speaking a language with grammatical gender distinctions and the economic empowerment of women.  Gay, Santacreu-Vasut and Shoham (2013) find that women in countries with languages that make gender distinctions are less likely to participate in the labour market or politics and less able to get credit or own land.

The study uses a series of regressions to demonstrate robust correlations between grammatical gender and various economic variables from a range of databases.  The gender variables include whether a language has a sex-based gender system, how the genders are used in pronouns, the intensity of the gender system (languages with 2 genders vs languages with 1 or more than 2 genders) and whether gender is assigned semantically or formally.  The correlations control for geographical variables (distance from the equator), climate (tropics, frost days, access to the sea), history of colonisation, continent, religion and cultural beliefs and values.  The findings include statistics such as “Having a sex-based gender system decreases the female labor force participation rate by 13 pp % relative to the base-line value in countries with no gender system”.

The approach is very similar to Keith Chen’s study of future tense and economic savings behaviour, and uses some of the same data including the world atlas of language structures (WALS) and the World Values Survey.  Indeed, Gay et al. find that “women living in countries whose dominant language marks gender more intensively are less likely than men to save”.  The paper follows other studies on the cultural transmission of agricultural technology and the role of women in society (Alesina, Guiliano & Nunn, 2011, see here).

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This is a guest post by Justin Quillinan (of Chimp Challenge fame).

Cast your reminisce pods back a few days and recall Sean’s iterated learning experiment using the automated transcription of YouTube videos. The process went as follows:

1. Record yourself saying something.
2. Upload the video to YouTube
3. Let it be automatically transcribed (usually takes about 10 minutes for a short video)
4. Record yourself saying the text from the automatic transcription
5. Go to 2

Sean took a short extract from Kafka’s Metamorphosis and found that, as in human iterated learning experiments, both the error rate and compression ratio decreases with successive iterations. He also found that the process resulted in a text with longer and more unique words.

I was curious to see whether we could remove human participants entirely and run computer generated speech through this automated transcription. Here’s the process:

1. Generate an audio file from some text using a speech synthesis program;
2. Generate a transcription of the audio file;
3. Repeat from 1. with the new transcription.

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I recently discovered that videos uploaded to YouTube are automatically transcribed (if they’re in English).  As you might guess, the transcriptions are not perfect, so there will be a discrepancy between what the speaker actually said and what is transcribed.  This is essentially all you need to run an iterated learning experiment (e.g. Kirby, Cornish & Smith, 2008).  Iterated learning is a process of repeatedly transmitting a signal through a bottleneck.  For instance, language is transmitted from adults to children, who learn its rules.  These children then go on to transmit this language to their own children.

Simon Kirby and colleagues have discovered that this process leads to languages becoming both more learnable and more expressive over time.  This happens by the emergence of compositionality: parts of a word become systematically linked to parts of its meaning.  See some posts by Hannah and Wintz on these experiments.

But can we see the same process with non-human learners?  Here’s how iterated learning with YouTube works:

1. Record yourself saying something.
2. Upload the video to YouTube
3. Let it be automatically transcribed (usually takes about 10 minutes for a short video)
4. Record yourself saying the text from the automatic transcription
5. Go to 2

Here’s a diagram of the procedure:

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It’s job frenzy out there. You can see here seven postdoctoral positions in the Dutch research consortium ”Language in Interaction” including one on language evolution below:

WP 5: Language evolution and diversity

The goal of this WP is to contribute to a better understanding of the biological underpinnings of linguistic universality as well as diversity, both at the population level (between languages and between species) and at the individual level (within a language). We are looking for a postdoctoral researcher in this area. The preferred area of specialization is evolutionary modelling of language with respect to diversity in communication. Other possible areas of expertise may include language diversity, individual differences in language abilities, animal communication, and genetic influences on speech and language.

Contact information WP 5: Prof. Pieter Muysken, p.muysken@let.ru.nl

The deadline for applications is May 15, 2013 for a September start date. More details on the document here:

https://docs.google.com/file/d/1HErCprWm1KZauFiKNEaswRmblG2HJZlydRSbDODEKfVkZ48BQzen3ems-h43/edit

The university of St. Andrews is on a hiring frenzy:

Applications are invited to join an interdisciplinary research programme directed by Professors Kevin Laland (School of Biology) and Andrew Whiten (School of Psychology and Neuroscience) at the University of St Andrews’ Centre for Social Learning and Cognitive Evolution. “Exploring the Evolutionary Origins of Culture Complexity, Creativity and Trust” is funded through a major grant from the John Templeton Foundation. Successful candidates will join a team of over 20 researchers working on the project, studying aspects of social learning, innovation and cultural evolution in monkeys, apes and human participants, employing a diversity of techniques including systematic observation, experiments and statistical modelling.

Two Lectureships: Lecturer in Behavioural and Evolutionary Biology (School of Biology);

Lecturer in Comparative, Evolutionary or Developmental Psychology (School of Psychology). Salary £37,382 – £45,941 per annum. Ref No: ML1133. Closing Date 7 April 2013.

Eight Postdoctoral Research Assistantships: £30,424 – £36,298 per annum. Ref No: SB1299.Closing Date 5 April 2013.

Up to ten PhD Scholarships. For further particulars and how to apply see http://lalandlab.st-andrews.ac.uk/opportunities.html.

Positions are for 33 months (salaried posts) or three years (PhD), commencing 1st September 2013 or as soon as possible thereafter. For the Lectureships & Postdoctoral Research Assistants only, we encourage applicants to apply online at www.vacancies.st-andrews.ac.uk/welcome.aspx, where further particulars of all posts can be viewed. However if you are unable to do this, please call +44 (0)1334462571 for an application pack.

Please quote the appropriate reference number on all correspondence.

The University is committed to equality of opportunity.

The University of St Andrews is a charity registered in Scotland (No SC013532).

The press release announcing the grant states:

The new project will use comparative studies of social learning among monkeys, apes and human children together with sophisticated statistical modeling and a diverse range of other methods to address a suite of such ‘Big Questions’ about the evolution of culture, a field in which St Andrews is a world leader. “When we talk of ‘culture’ in this project, we include everything that is learned from others, from our language to our technology and moral codes. Our cultural nature is arguably the most important characteristic that separates us from even our closest primate relatives”, says Professor Whiten. “Nevertheless, we can learn much about the evolutionary roots of our cultural capacities by studying the social traditions of monkeys and apes, and that will be an important part of this project”.

“Our unique human ability to make cultures evolve cumulatively, building on what others achieved before us, depends on two essential elements highlighted in the project title”, adds Professor Laland: “creativity, which produces new innovations, and trust, which guides which innovations are adopted and spread. We will be investigating how humans and other animals decide whom to trust as sources of cultural information and what other forms of cultural filtering are important”.

So it sounds very relevant for Language Evolution bods!

Zach Weinersmith of SMBC comics and various science folk are putting on a Festival of Bad Ad Hoc Hypotheses.  The festival will include presentations of “well-argued and thoroughly researched but completely incorrect evolutionary theory”.  They’re looking for people to give 5 minute presentations.  It takes place at MIT on the 20th April, submissions are due 10th March.

Finally, a place for all our hard work on spurious correlations in culturally evolved systems.

More details here

Yesterday I posted an analysis of some work by Prof. Keith Chen on the link between future tense marking and economic decisions.  Prof. Chen made some suggestions about changes to the analysis, some of which I’ve carried out here.  The new results below indicate that the link between future tense and the propensity to save is more robust than the previous post suggested, which is quite embarrassing, but I submit the findings here anyway.

One of Prof. Chen’s points was that I was using simple linear regression, while his analysis used conditional logit modelling.  This is much more computationally intense, and it’s not feasible for me to run 145 logit models for the given size of dataset (R was telling me it needed 13GB of memory to run an analysis of one linguistic variable! Help, anyone?).

Another suggestion was to look at which linguistic variables explain the residual variation in a model with non-linguistic variables.  That is, controlling for non-linguistic variables such as age, sex and number of children, how much extra variance does a particular linguistic variable account for?

I analysed this by comparing two models for each linguistic variable (using ANOVAS, although the results are equivalent with regressions).  Each model had the propensity to save as the dependent variable and independent variables including age, sex, employment status, marriage status, level of education, religion, number of children and survey year.  The second model also included the linguistic variable.  I then compared the improvement in the model fit using the F-score of the difference in residuals.  (There are some problems here, because different linguistic features will be represented in different sub-sets of the data, but we’ll ignore this for now.)

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