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:

From: YourHealthNet in Australia
From: YourHealthNet in Australia

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.

 

Numerical vs. analytical modelling

ResearchBlogging.org

Since its resurgence in the 90s Multi-agent models have been a close companion of evolutionary linguistics (which for me subsumes both the study of the evolution of Language with a capital L as well as language evolution, i.e. evolutionary approaches to language change). I’d probably go as far as saying that the early models, oozing with exciting emergent phenomena, actually helped in sparking this increased interest in the first place! But since multi-agent modelling is more of a ‘tool’ rather than a self-contained discipline, there don’t seem to be any guides on what makes a model ‘good’ or ‘bad’. Even more importantly, models are hardly ever reviewed or discussed on their own merits, but only in the context of specific papers and the specific claims that they are supposed to support.

This lack of discussion about models per se can make it difficult for non-specialist readers to evaluate whether a certain type of model is actually suitable to address the questions at hand, and whether the interpretation of the model’s results actually warrants the conclusions of the paper. At its worst this can render the modelling literature inaccessible to the non-modeller, which is clearly not the point. So I thought I’d share my 2 cents on the topic by scrutinising a few modelling papers and highlight some caveats, and hopefully also to serve as a guide to the aspiring modeller!

Continue reading “Numerical vs. analytical modelling”

Language Evolution 101: Gene’s Eye vs. DST

Broad hypothese are better than narrow ones as they can be applied to a wider range of things. That’s probably a controversial thing to say, but it’s certainly true that the beauty of most evolutionary theory lies in its simplicity, and therefore its ability to be applied to more than just biology. So how do different evolutionary theories fair when applied to the world of language? I’ll look here at the gene’s eye view of evolution and developmental systems theory.

The gene’s eye view of evolution

The gene’s eye view of evolution splits evolution up into the two processes, replication and interaction. The replicators are the things which are copied (generally genes) and the interactors are the organisms which interact with their environment. In this post I will be sticking with the terms ‘replicator’ and ‘interactor’ as posited by Hull (1980) as opposed to Dawkins’ ‘replicator’ and ‘vehicle’ as Hull’s terms are much more applicable to language as Hull formalised it as a generalised theory which Hull himself has applied to cultural evolution (Hull 1988).

Maynard-Smith and Szathmáry (1995) argue that since language and the genome are recursive then only these two mechanisms have an infinite number of heritable states which is why a replicator view of natural selection can only account for these two mechanisms. Many Linguists have tried to apply a replicator view to the evolution of language, both with regards to language’s biological and cultural evolution. Regarding the cultural evolution of language, there seems to be many parallels with biological evolution which can be drawn with the controversy as to what can be considered a replicator. David Hull (1980) defines a replicator as “an entity that passes on its structure directly in replication”. Within language this could qualify anything which allows us to say the same thing in a different way. This means that replicators can lie at a phonemic level, in that vowels can vary and some realisations will be more successful than others with regards to contrastive difference from other vowels. Morphemes can also vary and be more selectively successful in terms of productivity. Selection can work all the way up to lexemes and syntax, both on a wide scale, or on a narrow scale, with a specific idiosyncratic structure emerging in some frequently used phrases. If one of two interlocutors in a communicative act uses an idiosyncratic structure to express something, and is successful in being understood, then they will see little point in changing the utterance next time they want to express that proposition, this, presumably, would ‘catch on’. Croft (2000) lumps all of these possible replicators under a general heading of ‘lingueme’ to make them more analogous with genes. This may be an oversimplification, as layers of structure as they appear in language are not present in the DNA sequence (or at least not understood to the same level as they are in language) past the distinction of nucleotides, codons and ‘genes’, and even upon this distinction it is usually argued that single nucleotides and codons cannot be replicators, whereas, it seems that the smallest particles of language structure can be.

Croft (2000) argues that the selection of linguistic replicators is driven by social factors as he claims that speakers select variants with regards to their social values. However, as in biology, selection where not only functional selection, but sexual selection and social selection, also exist, it seems odd that language evolution would not also be driven by a combination of factors, both functional and social.

Language does not pass purely from vertical transmission from one generation to the next, as genes do, horizontal transmission is also present and there is linguistic input from more than just the two parents of an individual. Horizontal gene transfer, which occurs when an organism acquires genetic material from a different organism, but not through the process of replication or reproduction, could be described as analogous to this but this certainly isn’t the norm within genetic evolution as it is in the transmission of language (Pagel, 2009).

Developmental Systems Theory

Developmental Systems Theory (DST) is an approach to evolution in opposition to replicator/interactor view of natural selection. It takes the position that more things need to be taken into account than just replicators and interactors and that if anything is the unit of selection then it is the entire developmental system an organism takes. This stresses the importance of non-genetic factors and their role in evolution. Many layers of structure need to be considered and each of these layers of structure can only be accounted for in their own terms. A DST approach to the emergence of language is one which takes the whole developmental cycle of language acquisition and communication into account. The learning biases of children certainly counts as a unique event which is responsible for individual differences in each generation. As well as this, the learning biases of adults can also contribute to language evolution from a DST approach in societies where there are many second language speakers (Wray and Grace 2005). Learning biases in transmission are often cited exclusively in the context of cultural evolution; however, learning biases have now come to give us a good explanation as to how linguistic constraints may have become genetically assimilated after cultural transmission occurred though mechanisms such as the Baldwin Effect (Baldwin, 1896). If there’s any call for it I’ll post a 101 on the Baldwin Effect in the near future.

Refs

Baldwin, M. J. (1896) A New Factor in Evolution. The American Naturalist,  Vol. 30, No. 354, 441-451.

Croft, W. (2000) Explaining language change: an evolutionary approach.  Harlow: Pearson.

Hull, D. L., (1980). Individuality and  Selection. Annual Review of Ecology and Systematics, 11: 311–332.

Hull, D. L. (1988) Science as a process: an evolutionary account of the  social and conceptual development of science. Chicago: University of  Chicago Press.

Maynard-Smith, J. and Szathmáry, E. (1995) The major transitions in  evolution.

Pagel, M. (2009). Human language as a culturally transmitted replicator. Nature Reviews Genetics10(6), 405-415.

Pinker, S. and P. Bloom (1990). Natural Language and Natural Selection.  Behavioral and Brain Sciences 13.4: 707-726.

Wray, A. and Grace, G. (2005) The consequences of talking to strangers:  Evolutionary corollaries of socio-cultural influences on linguistic  form. Lingua, 117 (3), 543-578

 

Literary History, the Future: Kemp Malone, Corpus Linguistics, Digital Archaeology, and Cultural Evolution

In scientific prognostication we have a condition analogous to a fact of archery—the farther back you draw your longbow, the farther ahead you can shoot.
– Buckminster Fuller

The following remarks are rather speculative in nature, as many of my remarks tend to be. I’m sketching large conclusions on the basis of only a few anecdotes. But those conclusions aren’t really conclusions at all, not in the sense that they are based on arguments presented prior to them. I’ve been thinking about cultural evolution for years, and about the need to apply sophisticated statistical techniques to large bodies of text—really, all the texts we can get, in all languages—by way of investigating cultural evolution.

So it is no surprise that this post arrives at cultural evolution and concludes with remarks on how the human sciences will have to change their institutional ways to support that kind of research. Conceptually, I was there years ago. But now we have a younger generation of scholars who are going down this path, and it is by no means obvious that the profession is ready to support them. Sure, funding is there for “digital humanities” and so deans and department chairs can get funding and score points for successful hires. But you can’t build a profound and a new intellectual enterprise on financially-driven institutional gamesmanship alone.

You need a vision, and though I’d like to be proved wrong, I don’t see that vision, certainly not on the web. That’s why I’m writing this post. Consider it sequel to an article I published back in 1976 with my teacher and mentor, David Hays: Computational Linguistics and the Humanist. This post presupposes the conceptual framework of that vision, but does not restate nor endorse its specific recommendations (given in the form of a hypothetical program for simulating the “reading” of texts).

The world has changed since then and in ways neither Hays nor I anticipated. This post reflects those changes and takes as its starting point a recent web discussion about recovering the history of literary studies by using the largely statistical techniques of corpus linguistics in a kind of digital archaeology. But like Tristram Shandy, I approach that starting point indirectly, by way of a digression.

Who’s Kemp Malone?

Back in the ancient days when I was still an undergraduate, and we tied an onion in our belts as was the style at the time, I was at an English Department function at Johns Hopkins and someone pointed to an old man and said, in hushed tones, “that’s Kemp Malone.” Who is Kemp Malone, I thought? From his Wikipedia bio:

Born in an academic family, Kemp Malone graduated from Emory College as it then was in 1907, with the ambition of mastering all the languages that impinged upon the development of Middle English. He spent several years in Germany, Denmark and Iceland. When World War I broke out he served two years in the United States Army and was discharged with the rank of Captain.

Malone served as President of the Modern Language Association, and other philological associations … and was etymology editor of the American College Dictionary, 1947.

Who’d have thought the Modern Language Association was a philological association? Continue reading “Literary History, the Future: Kemp Malone, Corpus Linguistics, Digital Archaeology, and Cultural Evolution”

“Hierarchical structure is rarely…needed to explain how language is used in practice”

How hierarchical is language use?

Stefan L. Frank, Rens Bod and Morten H. Christiansen

Abstract: It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science.

Published online before print September 12, 2012, doi: 10.1098/rspb.2012.1741
Proceedings of the Royal Society B

Full text online HERE.

What Does It Mean To Mean?

I’ve been agonizing somewhat over what to write as my first post. I am currently delving into the wonderful word of pragmatics via a graduate seminar at the University of Virginia, but I do not yet feel proficient enough to comment on the complex philosophical theories that I am reading. So, I am going to briefly present an overview of what I will be attempting to accomplish in my year-and-a-half long thesis project. Upcoming entries will most likely be related to this topic, similar topics, and research done that bears on the outcome of my investigation.

I recently was watching a debate between Richard Dawkins and Rowan Williams, the Archbishop of Canterbury, on the nature of the human species and its origin. To no one’s surprise, language was brought up when discussing human origins.  Specifically, recursive, productive language as a distinguishing marker of the human species. What may seem obvious to the evolutionary linguists here actually came with some interesting problems, from a biological perspective. As Dawkins discusses in the debate, evolution is rather difficult for the animal kingdom. Whereas for plants, there may be distinct moments at which one can point and say “Here is when a new species emerged!”, this identifiable moment is less overt for animals.  One key problem with determining the exact moment of a new species’ emergence is the question of interbreeding.

If we consider the development of a language (a system of communication with the aforementioned characteristics) to be a marker of the human species, then do we suppose at one point we have a child emerging with the ability to form a language with mute or animalistic parents? To whom would the child speak? If Dawkins is correct and language is partially rooted in a specific gene, we could theorize that the “first” human with the gene would thereby mate with proto-humans lacking the gene. All of this is, of course, very sketchy and difficult to elucidate, as even the theory that language is rooted in a gene can be disputed. The problem remains an integral one, not only for understanding the evolutionary origins, but as the philosophers in my pragmatics class would point out, it would also have significant bearing on ontological and ethical questions regarding human origins.

I do not hope to solve this entire issue in my senior thesis; however, I do hope to show the development of language less as a suddenly produced trait and more as a gradual process from a less developed system of communication to a more developed one. From a pragmatics point of view, the question might be, how do we jump the gap, so to speak, between the lesser developed systems of communication (conventionally, these include animal communication, natural meaning, etc.) and the fully fledged unique system of human language? Paul Grice, as one might discover in my handy dandy Wikipedia link above, proposed a distinction between natural meaning, which he defined as being a cause/effect indication and considered in terms of its factivity, and non-natural meaning, as a communicative action that must be considered in terms of the speaker’s intentions. Yet, as stated above, the question remains: how do we (evolutionarily) progress from natural meaning to non-natural meaning?

Not to overly simplify, but my answer rests in the question of what it means to mean something. I hope to show, in my subsequent posts, that an investigation into semantics, and, more specifically, a natural progression through a hierarchy of types of meaning, might shed light on this problem. In short, taking a look at the development of meaning, intent, and the qualifications for a language proper can shed light on how language developed into the complex, unique phenomenon we study today.  (Oh, and to satisfy the philosophers in my class, I may ramble occasionally about the implications for a philosophical conception of our species!)

 

Free Online Machine Learning Course

Hello!

This is a quick post about a free online course on Machine Learning. The course is run by Andrew Ng at Stanford and I thought it would be of interest to those who read this blog as it covers learning algorithms which help us to understand how humans learn things as well as machines.

The course comes in structured chunks which are released a week at a time. It hasn’t started yet as it is in the pre-launch period but you can go on the site, sign up and watch the first week of videos and answer the review questions to get a head start.

It seems that this course is running as a beta version of what online courses could be in the future. If you’re even slightly interested in how machines, and indeed humans, learn I suggest you sign up and take part. You can set the difficulty as basic or advanced and it’s FREE!

Sign up and see the first week of videos here: http://www.ml-class.org/course/class/index

You can also see a lecture series by Andrew Ng on youtube here: http://www.youtube.com/watch?index=1&v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599

Cognitivism and the Critic 2: Symbol Processing

It has long been obvious to me that the so-called cognitive revolution is what happened when computation – both the idea and the digital technology – hit the human sciences. But I’ve seen little reflection of that in the literary cognitivism of the last decade and a half. And that, I fear, is a mistake.

Thus, when I set out to write a long programmatic essay, Literary Morphology: Nine Propositions in a Naturalist Theory of Form, I argued that we think of literary text as a computational form. I submitted the essay and found that both reviewers were puzzled about what I meant by computation. While publication was not conditioned on providing such satisfaction, I did make some efforts to satisfy them, though I’d be surprised if they were completely satisfied by those efforts.

That was a few years ago.

Ever since then I pondered the issue: how do I talk about computation to a literary audience? You see, some of my graduate training was in computational linguistics, so I find it natural to think about language processing as entailing computation. As literature is constituted by language it too must involve computation. But without some background in computational linguistics or artificial intelligence, I’m not sure the notion is much more than a buzzword that’s been trendy for the last few decades – and that’s an awful long time for being trendy.

I’ve already written one post specifically on this issue: Cognitivism for the Critic, in Four & a Parable, where I write abstracts of four texts which, taken together, give a good feel for the computational side of cognitive science. Here’s another crack at it, from a different angle: symbol processing.

Operations on Symbols

I take it that ordinary arithmetic is most people’s ‘default’ case for what computation is. Not only have we all learned it, it’s fundamental to our knowledge, like reading and writing. Whatever we know, think, or intuit about computation is built on our practical knowledge of arithmetic.

As far as I can tell, we think of arithmetic as being about numbers. Numbers are different from words. And they’re different from literary texts. And not merely different. Some of us – many of whom study literature professionally – have learned that numbers and literature are deeply and utterly different to the point of being fundamentally in opposition to one another. From that point of view the notion that literary texts be understood computationally is little short of blasphemy.

Not so. Not quite.

The question of just what numbers are – metaphysically, ontologically – is well beyond the scope of this post. But what they are in arithmetic, that’s simple; they’re symbols. Words too are symbols; and literary texts are constituted of words. In this sense, perhaps superficial, but nonetheless real, the reading of literary texts and making arithmetic calculations are the same thing, operations on symbols. Continue reading “Cognitivism and the Critic 2: Symbol Processing”

Animal Signalling Theory 101: Handicap, Index… or even a signal? The Case of Fluctuating Asymmetry

The differences between handicaps and indices are usually distinguishable in formal mathematical models or in unambiguous real-world cases. Often though, classifying a trait as a handicap, an index, or even a signal at all, can be quite a difficult task.

For the purposes of illustration I will use Fluctuating Asymmetry (FA for short) as an example.  Fluctuating asymmetry is the term used to refer to deviation from symmetry in paired morphological structures (ranging from birds’ tails to human faces) that should be, all being well, bilaterally symmetric. Deviations from the ideal symmetrical phenotype are caused by inherent genetic perturbations and exposure to environmental disturbances occurring in early development.

Is FA a signal?

In their 2005 book Animal Signals, Maynard-Smith and Harper define a signal as:

‘Any act or structure which alters the behaviour of other organisms, which evolved because of that effect, and which is effective because the receiver’s response has also evolved’

They then argue that FA is unlikely to function as a signal because it is difficult to discern whether receivers respond directly to FA and because there appear to be few examples of displays in which signallers actively advertise their symmetry to receivers.

 

Continue reading “Animal Signalling Theory 101: Handicap, Index… or even a signal? The Case of Fluctuating Asymmetry”