Author: Yana Eglit

Doing an extra post in case I’m missing some; also just came across this paper and found it kind of cool:

Ritchie & Kirby 2005 Evol Ling Comm Selection, domestication, and the emergence of learned communication systems

In a prior study, the Bengalese finch was found to have a more complex song syntax than its wild ancestor (the white-backed munia); furthermore, while the finches could learn the songs of munia, the latter could not effectively learn the complex songs of the finches, suggesting that a part of the capability was physiological. The author of that prior study, Okanoya (2002), argued that the song complexity in the Bengalese finch was driven through sex selection, as the more basic pressures (food and predation) were relieved by domestication, enabling sex selection to finally drive up the complexity; furthermore, this would have been an honest signal of the male’s fitness, as a fitter bird could produce a more complicated song.

A competing hypothesis by Deacon agrees that song complexity is kept low in the wild munia through selective pressure, but claims that the lifting of basic selective pressures after domestication enabled the songs to get more complex through other means, namely drift. Thus, processes that previously paled in comparison with the selective pressures against excess song complexity became prominent, such as the effect of songs heard at an early age and mnemonic biases; that is, songs with a more regularised syntax may be easier to recall. Deacon further extends this concept to the evolution of human language; he calls the concept “selective masking”. In short, complexity may arise in the finches’ songs without being driven by direct selective pressure.

Ritchie and Kirby set out to test the competing hypotheses through computational modeling; long story short, a bunch of learning filters are set up amid evolutionary models, and the simulation is run trough three phases: 1) Population is filtered to have a particular song type; variation is reduced (modeling the case among wild munia. 2) The population, having learned (and become “attached” to) a particular kind of song, is now bombarded with a bunch of random songs, and demonstrates resistance to be affected much by it: the simulated birds still learn the ‘correct’ song over incorrect ones. 3) Selective pressure is alleviated altogether by simply ceasing to calculate the fitness values. This simulates domestication. Population was once again bombarded with random songs.

Complexity was initially defined by Okanoya as the number of unique song notes divided by the number of unique note-to-note transitions (aka ‘Song Linearity’); Okanoya found this ratio to be lower in the Bengalese finches than the wild munia, meaning their songs were more complex (less ‘linear’). Ritchie & Kirby’s simulation also yielded similar results; though they argue that a completely random song would have the maximum complexity by such measures. Additionally, they also used Grammar Encoding Length, or the number of bits required to describe a [in this case, Probabilistic] Finite State Machine, which was used to model song learning. [Now the structural linguistics and information theory loses me completely…]. Turns out, in phase 3, the grammar encoding length did increase and the linearity did go down, supporting the increase in song complexity after domestication.

Most importantly, their simulation showed that song complexity can increase in the absense of direct selective pressure, as selection was eliminated altogether in phase 3. This suggests that [once again,] one need not necessarily evoke absurdly complex selection stories (like sex selection and ‘honest signals’) to explain the song complexification in Bengalese finches. Furthermore, these results can be extrapolated further to linguistic evolution, suggesting that perhaps not all of syntax complexification requires selective pressures behind it. In fact, the eliviation of such pressures can allow more complex syntax to arise. As a sidenote, it has been observed that the rise of writing resulted in higher complexity of clause embedding, and this complexity also rose gradually, not immediately after writing systems first appeared (Karlsson in Sampson et al. 2009 Language Complexity as an Evolving Variable). This can also be seen as a case of the lifting (aka ‘masking’) of a selective constraint resulting in elevated complexity, in this case probably not particularly adaptive either. One can convey complex ideas just as well, and in some cases better, without the [ab]use of intricate clausal embedding…

Ritchie and Kirby conclude with an idea that perhaps one mustn’t look for selective advantages of elaborate syntax found in human language, but instead investigate what may have prevented syntactic elaboration from arising in the past – what selective pressure may have been eliviated, and what may have caused them?

Ok, kind of awkward writing feedback on this, but a few observations of my own for future reference or whatever:

– more overview of where the course is headed in the first couple of weeks: introduce linguistic and cultural evol before going in depth about biology to give people some questions to think about during the more detailed analysis of evolutionary biology.

– heavier workload at the beginning rather than end, as all other courses seem to go crazy in the last month, and thus the amount of effort available towards the course dwindles towards the end. Conversely, guest lecturers become less available towards the end too, so this is still a bit of a struggle. Perhaps spread out student-led presentations a little more as opposed to doing all at the very end. MURC timing is awkward but little could be done about that.

– MURC presentations seemed to be well received — was considered good opportunity to practice speaking/presentation skills. Was said to be pulled off well as a group — that is, perhaps each person’s presentation was quite short and skimpy on the detail, but the overall panel made up for it due to group cooperation. Slightly longer talks still woudl’ve been better.

– a lot of time was spent discussing administrative issues, which is the drawback of giving more power to the students (democracies, in their very early stages (where people’s opinions still matter), also tend to have similar issues…). For future reference, perhaps coordinator should be more decisive; we were kind of brainwashed by the program’s constant reminders that the seminars are participant-led. At least sometimes, the participants actually prefer more structure, especially at the beginning.

– initially had fears about the course being too dense on content, especially in the biology section, but it didn’t seem to be too much of a problem. Seemingly, the level of content was not generally a problem. Would like some opinion on that though.

That’s all for now…

This won’t be a comprehensive review of everything I learned or was exposed to as I have a final in 7 hours, but to summarise a few key points:

– There seems to be a lot of unnecessary conflict caused by hyperpolarisation of ideas and schools of thought. A specific example is the adaptation vs. spandrel debate concerning the evolution of language itself, which for a system so huge and complex is simply stupid. Some argue language as a whole, and all its features, are the result of adaptation, whereas other argue the whole thing is a spandrel (byproduct). Such absurdities mercilessly plague nearly all academic disciplines, and I find the all-or-nothing style arguments are extremely counterproductive and may be partly the reason academia is rather slow at the whole progress thing.

– I previously suspected that a lot of noise is caused in fields of applied evolutionary theory by the misundrestanding of its very fundamentals. Suspicions were confirmed. Contrary to its appearance (as presented to the public anyway), evolutionary theory is very complex and filled with subtleties. It takes many hours of training and neutralising polarised impressions (see above) to really begin to get a grip on the subject, and many scholars seem to get too carried away with idle hypothesising to actually check the facts. Converesely, their critics are too immersed in the age-old dogmas of their fields to give appropriate consideration to the new ideas. As a result, we have theoretical scholars who basically make stuff up regardless of actual data, and ‘experimental’ (data-oriented) scholars who generate heaps of information without bothering to analyse it in a new way. Again, such problems plague most fields (both science and humanities), but seem to affect young fields the worst. Perhaps with maturity a field tends to find more middle ground. That doesn’t mean one is excused with ignoring data or ignoring hypotheses on a personal level — an effective researcher (or anyone, really) must strive to both think creatively and stay in touch with reality.

– The field of language evolution is a mess. While we were taught in class as if Universal Grammar and whatever the pet theory of the prof is are under little dispute, the reality turns out to be quite different. Being a foreigner to the field, I felt quite overwhelmed by the arguments, as I don’t have enough background to know who and what to trust. As evolution is a fairly high level explanation, it relies on a reasonable understanding of quite a few principles of the field it’s being applied to. That is, if the field is poorly charactarised, the evolutionary analysis of it would be akin to 19th century biological evolution, where the general idea is sort of there but the principles completely unknown. Evolutionary biology became a “proper” (“hard”, or in Kuhn’s sense, closer to “Normal”) science roughly around Modern Synthesis (arguably), when the main mechanism of heredity (genetics) became better understood. Likewise, in order for evolutionary linguistics to become an undisputably respected field, the mechanisms of language transmission, as well as the neurological underpinnings of language itself (like biochem for biology), must be better understood. Neurobiology is also quite murky at the moment, but there does seem to be encouraging progress in that field; perhaps someday it will be sufficient for more rigorous models of language transmission and change.

That said, the sociological aspect of things is also quite important, and greater effort must be done to reconcile sociological theories with lower level explanations. The problem with higher level theories is that the further they are from more easily characterised ‘laws’ of nature, the easier it is to spew out hypotheses that seem plausible. This sometimes escalates to the point where it becomes taboo in a field to even consider one theory as being substantially inferior to another based on logical and evidential explanations, and instead become evaluated based on social appeal. I won’t mention any names here, but the disciplines are probably quite obvious 😉

– Cultural evolution and memetics: We don’t know what culture is. We can’t agree on its definition. That is a problem. That said, we should start focusing on smaller elements of culture first, and fiddle with more specific things first before making loud sweeping statements about everything. Cultural inheritance may well be an amalgamation of several disparate systems (that still interact; like genomic and cellular inheritance), or perhaps it all does nicely fit into one paradigm. Currently, more papers seem to focus on trying to sketch out the overall theory in the dense fog, and very few work on specific datasets — although that is changing. For example, phylogenetic techniques are becoming employed in some areas of anthropology (see Mace & Holden 2005 TrEE), and there’s great potential in the fascinating anthropological data currently gathering dust in obscure ethnographies and neglected records. Likewise, sociologists also have data to offer, and it has been long noted that even anthropologists and sociologists don’t talk to each other. That is, cultural evolution is a mess, and will require genuine cooperation between the warring tribes of academia, but there is much potential and hope.

The short timeframe made it very difficult to present anything worthwhile in terms of content volume — stuff had to be extremely condensed at the expense of depth of ideas presented. The MURC timing didn’t help at all, but it’s very hard to get around it unless the term projects are assigned BEFORE the winter break! As with the rest of the course, it seems that to balance the problems from other classes toward the end of the term, much more work must be done in the beginning rather than end, leaving the rest of the term to polish off the paper. Hopefully MURC did help some of us with confidence in public speaking — my scarriest moment ever was at a professional conference, with my boss watching, but afterwards everything else seemed like a piece of cake, from 80min tutorial presentations to MURC (I was scared in my first one too!) and even just talking to faculty. Seems like public speaking requires some fear to be overcome one way or another… and like anything else, giving talks is an art that must be practised over and over again, with mistakes and the occasional embarassment, to improve.

– I agree that there has been a bit too much administrative stuff, partly due to it being a first run of this kind of course, and partly as a result of overcompensation for my tendency to kind of dominate things too much. We were fed multitudes of scary stories during the training sessions about seminars rebelling against their coordinators and such (apparently true stories!), so we decided to take it easy with the rigidity of structure/planning. Of course, part of the price to pay for flexibility is time being spent on organising the course itself.

– The introductions/transitions could definitely have been better planned; was hard to see how to make them smooth during the first run.

– Coordinating becomes quite difficult towards the middle-to-end of term as other courses pile up, as does participating, of course. To soothe this, perhaps more assignments/parts of assignments and course planning done towards the beginning could help, as well as perhaps reducing the blog entries to once every two weeks, with or without topic questions. Unfortunately, one kind of has to get through the heavily-structured and condensed biology part of the course in the beginning, but prefacing it with a topical overview (as in the point above) could help. It seems like most discussion courses tend to get more flexible, and thus more effort-intensive, towards the end of term, simultaneously…

– As for the blog entries, it was difficult to think of stuff for me too, even though I blog science stuff publicly on a regular basis. Something about this being “informal-yet-formal” makes it still difficult to write stuff. Towards the end of term though, times get busy and probably would make sense to have the posts due less often than in the beginning.