Interdisciplinary Applications of Evolutionary Theory

I think a lot of this is going to be rehashing what was said on Thursday, but eh, a blog post is a blog post! I think having a general overview of topics in the first week would be good, and I think more time spent on non-bio topics would also be good. I really liked having the guest lecturers – I know they were a little tricky to get, but it was really nice getting to talk to people about their field of study without it being a straight-up traditional lecture. Administrative stuff kind of got a little nuts, but given that this is the first time this course has been run, it’s not surprising. I feel like a lot of times, people were generally leaning in one direction on a topic, but nobody quite wanted to commit and make a solid decision, which dragged things out a little.

It’s kind of hard to make a straight-up list of facts, but I felt like I learned a lot about Arts-related topics. I’ve taken Ling 100 in the past, but all the stuff we discussed about language acquisition and historical linguistics was totally new to me. I also only knew about memetics in the somewhat unofficial way the term is used on the internet, so all that, as well as the lecture on folklore was totally new to me.

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.

Here’s a more light hearted look at memage:

http://news.bbc.co.uk/2/hi/uk_news/magazine/8617454.stm

What I think is kinda interesting is that there’s two different views of why the Hitler meme has become so popular. One person says:

“It’s really the nature of the internet that once something reaches a critical mass it starts perpetuating itself out of its own momentum,” says creator Andy Nordvall, who uses the name Masters of Humility. “The sheer randomness and seeming arbitrary nature of what goes viral becomes part of the viral-ness itself.”

and another from the comments:

“This meme is so popular because it can be attached to so many events. The leader asks a question about progress, the supporters give him the bad news, the leader expresses concern about what he thought was happening, the supporters repeat the bad news, the leader realises that the challenge has been lost and describes all the opportunities they will now never achieve. The original happens to be about a dark period in history but the key story sequence occurs every day for all of us. I am so looking forward to a few UK Election versions.” (there’s a few similar posts after this)

So, what caused the meme to be popular? Obviously it has to be funny, or no one’s going to want to watch it, but in scenario a.) it just hits a critical mass and once it become recognized by enough people, it just takes off presumably. In the other, there’s actually some intrinsic reason in the meme itself as to why it spread. I’d tend to go with A. A lot of internet memes (cough*rickrolling*cough*) don’t actually make any sense unless you’re in on the joke, and as they become more popular, more people are in on them and they spread more and more.

Heya. Yes, I’m submitting questions forty minutes before the start of class. So sue me.

Here’s what I’d really like to discuss.

How useful is it to take evolutionary biology and apply some of its principles to other fields? I know we /can/ (as our project and invited speakers have shown), but does it help us understand the fields better? We’ve seen how people study language and folklore and some of it seems very similar to the study of evolution, and I’m wondering how new these ways of thinking are, and whether they’ve consciously borrowed from biology or not (and vice versa, as I seem to recall someone talking about biology taking tree building tactics from linguistics). Also, I know many people don’t like the idea of using evolution to study non-bio fields, and I’d like to discuss whether these arguments have merit or not.

Mostly, it feels like to some degree that we have been learning about several disconnected topics, and I would just like to (attempt to!) tie them neatly together.

I’ve been reading through Robert Weisberg’s (1986) Creativity: Genius and Other Myths and it occured to me that “the artistic process,” “the scientific process,” and “the writing process,” all follow the same process of creative problem solving, whereby there is an “ill-defined problem” that takes modification of past experience and knowledge to solve.

Weisberg intially provides the simple example of “the candle problem” where a subject is prosented with a box of tacks, matches and a candle, with the instruction to attach the candle to the wall, with the candle able to burn properly. Using verbal protocol, where subjects speak their thoughts aloud rather than describe what they’re doing, he found that most individuals start with directly attaching the candle to the wall with the tacks or by melting the base of the candle, and the more knowledable folk who can see that there are problems to that solution begin to reason the next best alternative to fix the problem in the problem, which is to tac the box to the wall as a stand.

Another example of creative problem solving is presenting in this type of riddle:

“Dan comes home one night after work, as usual. He opens the door and steps into the living room. On the floor he sees Charlie lying dead. There is water on the floor, as well as some pieces of glass. Tom is also in the room. Dan takes one quick glance at the scene and immediately knows what happened. How did Charlie die?”

The audience or subjects then try to guess the answer by asking yes or no answers, refining their questions to elicite the information needed to understand the riddle. Those who are good at these games have experience with problems of this sort.

Both of these are ill-defined problems because they are missing information. In non-creative problem solving, the answer is straightforward from the instructutions. Creativity is involved in answering ill-defined problems such as how well the wax sticks to the wall, or how strong are the tacks, or how old Charlie is, or how to model DNA or make sense of the natural world, or how to string a tune, or what connections to draw while writing a poem, what words to choose, or what happens next in a plot, or where the light falls in composing a painting.

Despite self-reports of an “Aha!” moment, or seemingly unconscious processes, Weisburg presents evidence for how the subjective experience is not the most reliable means for determining how the creative process actually works. The main reason is that artists and scientists often make such reports after the experience has long passed and memory can be faulty as one was more attuned to the task at hand than observing the whole situation. People will also (consciously or not) lie about their creative process, for “One can be influenced by a stimulus without being able to report it” (Weisburg 1986:29). The pleasure one gets from “Aha!” may just be relsease from consiously working on a hard problem for a long time, which is physically exhausting. Despite taking a break, creative people often engage in what Olton calles “creative worrying,” which is mulling over the problem even while not working on it, not an unconsious process. Breaks may also help the brain rest before having the energy to go at it again.

Genius looks like a divine gift from nowhere if one cannot see the small steps which it evolved from. This type of divine creativity coming forth perfected at once was never observed in laboratory settings, and careful examination of biographies, notes, sketches and drafts show that it takes hard work and at least ten years of training for the skill to contribute anything of value to that given field. A creative person draws from their exeperience, from the physical and cultural world around them. There are no correllations to be found in personality traits shared by all creative people. Creative people are also not creative in every field, nor is everything they produce a piece of genius. Weisburg writes, “Since the sensibilities of societies change, so do its judgements of genius… then looking at the characteristics of an individual, in order to determine the basis for genius, must be doomed to failure.”” (Weisburg 1986:88).

Master chess-players memorize thousands of chess positions (or approx. 50 000 patterns). Mozart’s later work is more popular to his early work; he too had to learn to compose. Picasso had sketches upon sketches of plans and edits of paintings, as do poets and composers who write pieces and try to fit them together, manipulating elements to solve a problem, to engage in critical anaylsis of their work. Great artistry does not come from a vaccum or perfected in the first coming-into-being. I think this is rather like how people viewed biology before evolution and genetics.

In any case, he is not saying the romantic view should be demolished, because it really does feel like an “Aha!” or something beyond your consciousness putting the pieces together. Objectively speaking however, the genius is a myth.

The romantic view, Weisburg defines, is the genius view that creativity comes through great leaps of imagination through communication with God or inspirational Muses. The behaviourist view is that creativity is nothing special because creative products are accidental combinations of old notions or knowledge.

Weisburg’s position lies somewhere inbetween. Small steps  rather than great leaps are the rule; here I quote:

“Harlow’s work indicates that insightful solutions of even seemingly simple problems depend on much experience with problems of that sort. Problems that appear to be trivially simple may only seem so because of the knowledge one brings to them. As Harlow’s work demonstrates, one should not underestimate the difficulties an inexperience problem solver confronts in a problem situation… Though someone else may solve a problem from what you consider a ‘fresh viewpoint,’ it does not mean the viewpoint was fresh from their point of view. If so, then trying to make oneself find that fresh point of view may be essentially impossible because it really means that one must transform oneself into another person, with that person’s knowledge, before one can bring a new approach to the problem. But then the viewpoint would not be fresh because one had acquired all that knowledge” (Weisburg 1986:48-69).

Creativity is ordinary, he argues, because the world changes. For example, if it was not Watson and Crick in that setting or scientific community, with that background or educational experience, working on that problem deemed to be a very important problem, and any one of those factors were changed, someone else would have discovered the two-stranded DNA helix, for they were not the only ones working on the problem. No environmental situation, perception of the situation and response to the situation will be exactly the same. Therefore it is a mistake to assume creativity is something humans do not engage with on an ordinary day-to-day basis. What we must explain then, is ordinary non-creative behaviour, when people engage in generalization and go through problem solving in a standard repetitive manner.

To summarize, Weisburg thinks creative individuals possess no extraordinary characteristics, “they do what we are all capable of doing. Because everyone can modify habitual responses to deal with novel situations, no further extraordinary capacities should be needed. Though in a given case the work and individual produces may be extraordinary, extraordinary work is not necessarily the product of extraordinary practices or the results of extraordinary personal characteristics” (Weisburg 1986:12). I think this speaks to the process of evolution being alive and well when we “create” culture, which appears extraordinary.

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.