Hannah

What is combinatorial structure?

Languages have structure on two levels. The level on which small meaningless building blocks (phonemes) make up bigger meaningful building blocks (morphemes), and the level of structure at which these meaningful building blocks make up even bigger meaningful structures (words, sentences, utterances). This was identified way back in the 1960s as one of Hockett’s design features for language know as “duality of patterning”, and in most of linguistics people refer to these different levels of structure as “phonology” and “(morpho)syntax”.

However, in recent years these contrasting levels of structure have started to be talked about in the context of language evolution, either in reference to artificial language learning experiments or experimental semiotics, where a proxy for language is used so it doesn’t make sense to talk about phonological or morphosyntactic structure, or when talking about animal communication where it also doesn’t make sense to talk about terms which pertain to human language. Instead, terms such as “combinatorial” and “compositional” structure are used, occasionally contrastively, or sometimes they get conflated to mean the same thing.

In the introduction to a recent special issue in Language and Cognition on new perspectives on duality of patterning, Bart de Boer, Wendy Sandler and Simon Kirby helpfully outline their preferred use of terminology:

Duality of patterning (Hockett, 1960) is the property of human language that enables combinatorial structure on two distinct levels: meaningless sounds can be combined into meaningful morphemes and words, which themselves could be combined further. We will refer to recombination at the first level as combinatorial structure, while recombination at the second level will be called compositional structure.

You will notice that they initially call both levels of structure “combinatorial”, and they both arguably are, and my point in this blog post isn’t necessarily that only structure on the first level should be called combinatorial, but that work talking about combinatorial structure should establish what their terminology means.

A recent paper by Scott-Philips and Blythe (2013), which is entitled “Why is combinatorial communication rare in the natural world, and why is language an exception to this trend?” presents an agent based model to show how limited the conditions are from which combinatorial communication can emerge. Obviously, in order to do this they need to define what they mean by combinatorial communication and present this figure by way of explanation:

They explain:

In a combinatorial communication system, two (or more) holistic signals (A and B in this figure) are combined to form a third, composite signal (A + B), which has a different effect (Z) to the sum of the two individual signals (X + Y). This figure illustrates the simplest combinatorial communication system possible. Applied to the putty-nosed monkey system, the symbols in this figure are: a, presence of eagles; b, presence of leopards; c, absence of food; A, ‘pyow’; B, ‘hack’ call; C = A + B ‘pyow–hack’; X, climb down; Y, climb up; Z ≠ X + Y, move to a new location. Combinatorial communication is rare in nature: many systems have a signal C = A + B with an effect Z = X + Y; very few have a signal C = A + B with an effect Z ≠ X + Y.

In this example, the building blocks which make C , A and B, are arguably meaningful because they act as signals in their own right, therefore, if C had a meaning which was a combination of the meanings of A and B, this system (using de Boer, Sandler and Kirby’s definition) would be compositional (this isn’t represented in the figure above). However, if the meaning of C is not a combination of the meanings of A and B, then A and B are arguably meaningless building blocks (and their individual expressions just happen to have meaning, for example the individual phoneme /a/ being an indefinite determiner in English, but not having this meaning when it is used in the word “cat”). In this case, the system would be combinatorial (as defined by the figure above, as well as under the definition of de Boer, Sadler and Kirby). So far so good, it looks like we are in agreement.

However, later in their paper Scott-Philips and Blythe go on to argue:

Coded ‘combinatorial’ signals are in a sense not really combinatorial at all. After all, there is no ‘combining’ going on. There is really just a third holistic signal, which happens to be comprised of the same pieces as other existing holistic signals. Indeed, the most recent experimental results suggest that the putty-nosed monkeys interpret the ‘combinatorial’ pyow–hack calls in exactly this idiomatic way, rather than as the product of two component parts of meaning. By contrast, the ostensive creation of new composite signals is clearly combinatorial: the meaning of the new, composite signal is in part (but only in part) a function of the meanings of the component pieces.

The argument they are giving here is that unless the meaning of C is a combination of A and B (or compositional as defined above), then it is not really a combinatorial signal.

Scott-Philips and Blythe definitely know and demonstrate that there is a difference between the two levels of structure, but they conflate them both under one term, “combinatorial”, which makes it harder to understand that there is a very clear difference. Also, changing the definition of what they mean by “combinatorial” between the introduction of their paper and their discussion confuses their argument.

Perhaps we should all agree to adopt the terminology proposed by de Boer, Sandler and Kirby, but given the absence of a consensus on the matter, at the very least I think outlining exactly what is meant by combinatorial (or compositional) needs to be established at the beginning of every paper using these terms.

References

de Boer, B., Sandler, W., & Kirby, S. (2012). New perspectives on duality of patterning: Introduction to the special issue. Language and Cognition, 4(4).

Hockett, C. 1960. The origin of speech. Scientific American 203. 88–111.

Scott-Phillips, T. C., & Blythe, R. A. (2013). Why is combinatorial communication rare in the natural world, and why is language an exception to this trend?. Journal of The Royal Society Interface, 10(88), 20130520.

EHBEA 2014 Bristol Conference: Abstract Submission Now Open

Abstract submission is now open for the European Human Behaviour and Evolution Association 2014 Conference (University of Bristol, 6 – 9 April 2014).

Further information about the conference, and a link to the abstract submission page, can be found here:

http://www.bristol.ac.uk/expsych/events/ehbea2014

Abstract submission is open until 31st December.

Language Evolution or Language Change?

You sometimes hear people complaining about the use of the term “language evolution” when what people really mean is historical linguistics, language change or the cultural evolution of language. So what’s the difference?

Some people argue that evolution is a strictly biological phenomenon; how the brain evolved the structures which acquire and create language, and any linguistic change is anything outside of this.

Sometimes this debate gets reduced to the matter of whether there are enough parallels between the cultural evolution of language and biological evolution to justify them both having the “evolution” label. George Walkden recently did a presentation in Manchester on why language change is not language evolution and dedicated quite a large chunk of a presentation to where the analogy between languages and species fall down. It is true that there are a lot of differences between languages and species, and how these things replicate and interact, and of course it is difficult to find them perfectly analogous.

However, focussing on the differences between biological and cultural evolution in language causes one to overlook why a lot of evolutionary linguistics work looks at cultural evolution. Work on cultural evolution is trying to address the same question as studies looking directly at physiology, why is language structured the way it is? Obviously how structure evolved is the main question here, but how much of this was biological, and how much is cultural is still a very open question. And any work which looks at how structure comes about, either through biological or cultural evolution can, in my opinion, legitimately be called evolutionary linguistics.

Additionally, in the absence of direct empirical evidence in language evolution, the indirect evidence that we can gather, either through observing the structure of the world’s languages, or by using artificial learning experiments, can help us answer questions about our cognitive abilities.

Furthermore, Kirby (2002) outlined 3 timescales of language evolution on the levels of biological evolution (phylogenetic), cultural evolution (glossogentic) and individual development (ontogentic). All of these timescales interact and influence each other, so it’s necessary to consider all of these levels in language evolution research, and to say work on any of these timescales is not language evolution research is not respecting the big picture.

So what’s the difference between language change and language evolution? As with almost everything, it’s not a black and white issue. I would say though that studies looking at universal trends in language, or cultural evolution experiments in the lab, are very relevant to language evolution. What I’d label historical linguistics, or studies on language change, however, is work which presents data from just one language, as it is hard to make inferences about the evolution of our universal capability for language with just one data point.

Figure 1 from: Kirby, S. (2002b). Natural language from artificial life. Artificial Life, 8(2):185-215.

CogSci 2013 – the others

It’s over but there’s loads of cool papers I didn’t cover. Below is probably not a definitive list because there are SO MANY papers here, but here’s a good flavour of of the more language evolutiony offerings. In no particular order.

Language and Gesture Evolution by Call, Goldin-Meadows, Hobaiter, Liebal and Tomasello

Abstract:

In humans, gestural communication is closely intertwined with language: adults perform a variety of manual gestures, head movements and body postures while they are talking, children use gestures before they start to speak, and highly conventionalized sign systems can even replace spoken language. Because of this role of gestures for human com-munication, theories of language evolution often propose a gestural origin of language. In searching for the evolutionary roots of language, a comparative approach is often used to investigate whether any precursors to human language are also present in our closest relatives, the great apes, because of our shared phylogenetic history. Therefore, the aim of this symposium is to present recent progress in the field of language evolution from both a developmental and compa-rative perspective and to discuss the question if and to what extent a comparison with nonhuman primates is suitable to shed light on possible scenarios of language evolution.

Individuals recapitulate the proposed evolutionary development of spatial lexicons by Carstensen and Regier

Abstract:

When English speakers successively pile-sort colors, their sorting recapitulates an independently proposed hierarchy of color category evolution during language change (Boster, 1986). Here we extend that finding to the semantic domain of spatial relations. Levinson et al. (2003) have proposed a hierarchy of spatial category evolution, and we show that English speakers successively pile-sort spatial scenes in a manner that recapitulates that proposed evolutionary hierarchy. Thus, in the spatial domain, as in color, proposed universal patterns of language change based on cross-language observations appear to reflect general cognitive forces that are available in the minds of speakers of a single language.

Systems from Sequences: an Iterated Learning Account of the Emergence of Systematic Structure in a Non-Linguistic Task by Cornish, Smith and Kirby

Abstract:

Systematicity is a basic property of language and other culturally transmitted behaviours. Utilising a novel experimental task consisting of initially independent sequence learning trials, we demonstrate that systematicity can unfold gradually via the process of cultural transmission.

Experimental insights on the origin of combinatoriality by Roberts and Galantucci

Abstract:

Combinatoriality—the recombination of a small set of basic forms to create an inﬁnite number of meaningful units—has long been seen as a core design feature of language, but its origins remain uncertain. Two hypotheses have been suggested. The ﬁrst is that combinatoriality is a necessary solution to the problem of conveying a large number of meanings; the second is that it arises as a consequence of conventionalisation. We tested these hypotheses in an experimental-semiotics study. Our results supported the hypothesis based on conventionalisation but offered little support for the hypothesis based on the number of meanings.

Combinatorial structure and iconicity in artificial whistled languages by Verhoef, Kirby and de Boer

Abstract:

This article reports on an experiment in which artificial languages with whistle words for novel objects are culturally transmitted in the laboratory. The aim of this study is to investigate the origins and evolution of combinatorial structure in speech. Participants learned the whistled language and reproduced the sounds with the use of a slide whistle. Their reproductions were used as input for the next participant. Cultural transmission caused the whistled systems to become more learnable and more structured. In addition, two conditions were studied: one in which the use of iconic form-meaning mappings was possible and one in which the use of iconic map- pings was experimentally made impossible, so that we could investigate the influence of iconicity on the emergence of structure.

Linguistic structure is an evolutionary trade-off between simplicity and
expressivity by Smith, Tamariz and Kirby

Abstract:

Language exhibits structure: a species-unique system for expressing complex meanings using complex forms. We present a review of modelling and experimental literature on the evolution of structure which suggests that structure is a cultural adaptation in response to pressure for expressivity (arising during communication) and compressibility (arising during learning), and test this hypothesis using a new Bayesian iterated learning model. We conclude that linguistic structure can and should be explained as a consequence of cultural evolution in response to these two pressures.

Communicative biases shape structures of newly acquired languages by Fedzechkina, Jaeger and Newport

Abstract:

Languages around the world share a number of commonalities known as language universals. We investigate whether the existence of some recurrent patterns can be explained by the learner’s preference to balance the amount of information provided by the cues to sentence meaning. In an artificial language learning paradigm, we expose learners to two languages with optional case-marking – one with fixed and one with flexible word order. We find that learners of the flexible word order language, where word order is uninformative of sentence meaning, use significantly more case-marking than the learners of the fixed word order language, where case is a redundant cue. The learning outcomes in our experiment parallel a variety of typological phenomena, providing support for the hypothesis that communicative biases can shape language structures.

Regularization behavior in a non-linguistic domain by Ferdinand, Thompson, Kirby and Smith

Abstract:

Language learners tend to regularize unpredictable variation and some claim that is due to a language-specific regularization bias. We investigate the role of task difficulty on regularization behavior in a non-linguistic frequency learning task and show that adults regularize variable input when tracking multiple frequencies concurrently, but reliably reproduce the variation they have observed when tracking one frequency. These results suggest that regularization behavior may be due to domain-general factors, such as memory limitations.

Learning, Feedback and Information in Self-Organizing Communication Systems by Spike, Stadler, Kirby and Smith

Abstract:

Communication systems reliably self-organize in populations of interacting agents under certain conditions. The various fields which model this – game theory, cognitive science and evolutionary linguistics – make different assumptions about the learning and behavioral processes which are responsible. We created an exemplar-based framework to directly compare these approaches by reproducing previously published models. Results show that a number of mechanisms are shared by the systems which can construct optimal communication. Three general factors are then proposed to underlie any self-organizing learned system.

A robustness approach to theory building: A case study of language evolution by Irvine, Roberts and Kirby

Abstract:

Models of cognitive processes often include simplifications, idealisations, and fictionalisations, so how should we learn about cognitive processes from such models? Particularly in cognitive science, when many features of the target system are unknown, it is not always clear which simplifications, idealisations, and so on, are appropriate for a research question, and which are highly misleading. Here we use a case-study from studies of language evolution, and ideas from philosophy of science, to illustrate a robustness approach to learning from models. Robust properties are those that arise across a range of models, simulations and experiments, and can be used to identify key causal structures in the models, and the phenomenon, under investigation. For example, in studies of language evolution, the emergence of compositional structure is a robust property across models, simulations and experiments of cultural transmission, but only under pressures for learnability and expressivity. This arguably illustrates the principles underlying real cases of language evolution. We provide an outline of the robustness approach, including its limitations, and suggest that this methodology can be productively used throughout cognitive science. Perhaps of most importance, it suggests that different modelling frameworks should be used as tools to identify the abstract properties of a system, rather than being definitive expressions of theories.

And last but not least…replicated typo’s very own Sean Roberts with A Bottom-up approach to the cultural evolution of bilingualism

Abstract:

The relationship between individual cognition and cultural phenomena at the society level can be transformed by cultural transmission (Kirby, Dowman, & Griffiths, 2007). Top-down models of this process have typically assumed that individuals only adopt a single linguistic trait. Recent extensions include ‘bilingual’ agents, able to adopt multiple linguistic traits (Burkett & Griffiths, 2010). However, bilingualism is more than variation within an individual: it involves the conditional use of variation with different interlocutors. That is, bilingualism is a property of a population that emerges from use. A bottom-up simulation is presented where learners are sensitive to the identity of other speakers. The simulation reveals that dynamic social structures are a key factor for the evolution of bilingualism in a population, a feature that was abstracted away in the top-down models. Top-down and bottom-up approaches may lead to different answers, but can work together to reveal and explore important features of the cultural transmission process.

CogSci 2013: Communication Leads to the Emergence of Sub-optimal Category Structures

Next up is Catriona Silvey, Simon Kirby and Kenny Smith, who use an experiment which gets participants to categorise shapes from a continuous space either in a communicative condition or a non-communicative condition. You can read it here (you should, it’s really awesome, and I only describe it briefly below): http://www.lel.ed.ac.uk/~s1024062/silvey_cogsci.pdf

Silvey et al. are interested in how semantic categories emerge. They set up an experiment in which participants were given a continuous semantic space:

In one condition single participants had to simply divide the space up into different labeled categories. In another condition, participants had to play a communication game. Participants communicated in pairs using the same semantic space. In both conditions, participants were given nonsense words to label their categories. Participants could assign as many categories as they liked.

In the communication condition one participant was the sender and one was the receiver. The sender chose a word to communicate a given shape from the grid above, and the receiver had to chose which shape they thought the word referred too. Participants were then given feedback as to whether the receiver had chosen the right image. They were then given a score based on how close the chosen shape was to the original given shape. The sender and received swapped roles every trial. Together they labelled every shape 4 times throughout the experiment and the last label for each shape chosen by both participants were taken for analysis.

An optimal categorisation strategy within this game would be to give every shape its own label, however, memory constraints are likely to stop participants using this strategy. Given that you will score more if shapes are closer, it was expected that participants would use small, clustered and equally sized categories in order to optimise getting the right shape, and if not, maximising their score.

In the non-communicative experiment, participants arranged the categories in fairly balanced chunks that would have served relatively optimally for the communication game. However, despite expectations, participants in the communicative condition behaved sub-optimally and did not maximise their communicative success (their score in the game) in that their categories weren’t clustered or optimal in colour or size. This could have been caused by the communicative condition having extra pressure for the learnability of categories, as well as a pressure for communicative success, which the non-communicative condition did not have. The authors argue that this is possibly a demonstration for how real languages arrive at suboptimal categories, e.g. where words vary as to whether they represent a very small category or represent a much broader part of the semantic space.

CogSci 2013: The Impact of Communicative Constraints on the Emergence of a Graphical Communication System

The Annual Conference of the Cognitive Science Society is happening next week with quite a but of language evolution stuff going on. On the run up I’ll post a few evolutionary linguisticsy papers which will be presented. If you’re presenting something please feel free to send me it to be covered here or write a guest post and I’ll post it up!

Firstly, Bergmann, Lupyan & Dale are presenting a paper called “The Impact of Communicative Constraints on the Emergence of a Graphical Communication System”. The paper can be seen here: http://www.tillbergmann.com/papers/cogsci_squiggle_bergmann_dale_lupyan.pdf

The authors present an experiment in which participants had to communicate using graphical symbols (called squiggles) of faces. The experiment worked as a communication game but also had generational turnover as in iterated learning experiments. They investigated the effect of different features of the input faces and the effect of changing the comprehension condition and how these affected the structure of the symbols that emerged.

The study used Dale & Lupyan’s (2010) squiggle framework but used human faces as the input. Participants were to “squiggle” the face in just 5 seconds to prevent them from building representations which were too detailed and prevented participants from writing words etc. In the first experiment they found that participants used strategies such as using hair shape/length, face shape and features to differentiate between faces and different strategies were more common in some gender/age categories, e.g. women were more likely to be defined by their hair.

The second experiment was the same as the first one but differed in the “listening” round, where participants were to choose which original face a squiggle represented. In experiment 1, participants had to choose between any two faces, in experiment 2 they were to choose between an “opposite” face. Faces differed in being male or female and young or old. So in experiment 2 young females were always pitted against old males, and old females pitted against young males. This was to see if the environment in interrupting the squiggles effected how they were represented and interrupted. This experiment found that squiggles in experiment 2 had less detail in order to be successful, and the amount of complexity fell over generations. I suppose this is an effect of the differentiating features between faces being more salient in the second experiment where faces always differ in both age and gender. This shows that the context in which communication occurs can shape the structure and complexity of that communication.

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.

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”

Language Evolution Coursera Proxy

There is not currently a coursera on Language Evolution, so as a vague substitute, I thought I’d do a run down of places on the internet you can find some pretty decent free lectures on the evolution of language by some pretty big names.

1) The first are the videos of the plenaries from last year’s EvoLang conference in Kyoto. http://ocw.kyoto-u.ac.jp/en/international-conference-en/31/video-en

I’m posting the direct links to all the videos because the above link keeps breaking:

1	Massimo, Piattelli-Palmarini Three Models (and a Half) for the Description of Language Evolution	Video
2	Minoru Asada Towards Language Acquisition by Cognitive Developmental Robotics	Video
3	Cedric Boeckx Homo Combinans	Video
4	Simon Kirby Why Language Has Structure: New Evidence from Studying Cultural Evolution in the Lab and What It Means for Biological Evolution	Video
5	Jenny Saffran Out of the Brains of Babes: Domain-general Learning Mechanisms and Domain-specific Systems	Video
6	Simon Fisher Molecular Windows into Speech and Language	Video
7	Russell Gray The Evolution of Language Without Miracles	Video
8	Rafael Núñez The Irreducible Semantic Communicative Drive	Video
9	Tetsuro Matsuzawa Outgroup: The Study of Chimpanzees to Know the Human Mind	Video
10	Tom Griffiths Neutral Models for Language Evolution。	Video
11	Terrence Deacon Neither Nature nor Nurture: Coevolution, Devolution, and Universality of Language	Video

The videos for the biolinguistics workshop can be found here: http://ocw.kyoto-u.ac.jp/en/international-conference-en/30/video-en

2) On the CARTA website you can find videos of speakers such as Terence Deacon talking about Symbolic Communication: Why is Human Thought so Flexible? as well as V.S. Ramachandran, Colin Renfrew and Patricia Churchland.

3) The videos from 2011’s ProtoLang can be viewed here: http://www.protolang.umk.pl/videos_and_links

There’s a link to the videos from 2009’s protolang at the bottom of that too, but they all seem to be broken. But you can actually still find them by searching for the author’s name on http://tv.umk.pl/

For example, searching Bart de Boer, you can find: http://tv.umk.pl/#movie=521

4) YouTube.

Highlights include Simon Kirby’s inaugural lecture at Edinburgh University, Kenny Smith at the University of Southampton earlier this year, more Terence Deacon, Luc Steels on robots and loads of other stuff, I am sure you are capable or googling the names of some language evolution folk.

Also, you can watch bbc horizon’s why do we talk featuring Techumseh Fitch, Simon Kirby and others here: http://www.youtube.com/watch?v=75XxjJYuV7I&list=PL9DD35E568234CA7F

And by request in the comments: Peter Richerson – How Possibly Language Evolved http://www.youtube.com/watch?v=zxJMtZUaeZU

If anyone else has some good video resources please add them in the comments!