Fluffy Friday – Internet Roundup

Fluffy Fridays have fallen by the wayside a bit as I keep up with the MOOC. This week has been a really interesting experience and in some ways, a lot of the discussions I was expecting, haven’t happened in the forums. The questions that spring to my mind when I think about measuring animal welfare clearly aren’t the questions that spring to my students’ mind.

For me this is one of the really valuable personal experiences I’m taking from the MOOC, being exposed to so many different students. I was never one of the panicking students, but I’ve had plenty of experience with them in my lectures – they’re usually  doing absolutely fine anyway, but because it’s important to them they doubt themselves very quickly. Take the undergraduates who email at midnight to tell you they just realised they used the wrong word in an essay.

It’s not a problem for lecturers (until the student starts to expect that lecturers will answer emails at midnight!) but I wonder about how the panickers feel about their education – if the stress of it detracts from the experience at all? I expect this is something I should be looking up and investigating, particularly as I’ve put in to supervise some Masters students this year.

But I always assumed that it was to do with the university experience, and yet I have panicky MOOC students too – it’s a free (or, at most, $40 course), and yet people still get very worked up if they’re worried about something. I think it just goes to show that the pastoral care of students is something that all lecturers need to be involved in.

 

Anyway I would like to introduce you to two fellow bloggers:

Sam Hardman of Ecologica Blog blogs about animal behaviour and has been commenting over here for the last week with some really interesting resources and insights. I’m hoping he expands on one of his comments in a future blog post.

And second is ComparativelyPsyched who I met a few months ago at a science communication event. He works on some really interesting psychology research and also an excellent science communicator.

 

I’ll be adding both these blogs to the sideroll so I thought I should introduce them.

 

Salvador Dumbo

I’ve spoken before about how YouTube and the explosion of camera phones has given animal behaviour researchers a a way of quantifying behaviour that is rarely seen, or would once have been thought of as anecdotal. Well here’s a short example of (what looks to be) a very strange behaviour that is prolifegate on YouTube and the interwebs.

Animal art!

Hey, don’t leave. This is a science blog. Sit down and watch these videos of elephants painting with sticks.

 

In that second video, at around 09:20, I wonder if that’s a bit of stereotypic behaviour going on.

By my thinking, as animal behaviour and welfare scientists, we’re interested in two or three main questions here:

  1. Are these animals creating art?
  2. Does the animal know what it is depicting?
  3. Is the process rewarding for the animal?

 

Firstly, we’ll define ‘art’ in a somewhat simplistic manner for the sake of this blog post – it should be a piece designed to provoke feelings in the viewer. This would require the elephants to have a theory of mind and to understand that someone ‘other’ than them perceives things and feels emotions. This is a pretty complex concept to grasp. There’s some evidence (Edgar et al 2012) to suggest that some species are capable of empathy (or proto-empathy), i.e. understanding that another individual has an emotional response comparable to your own, and yet different from yours. Strictly speaking empathy doesn’t mean you understand you can influence the emotional state of others, just that you understand they have it.

So are the elephants trying to manipulate our emotional state through their actions? Probably not. Could the elephants be doing this because they get rewarded afterwards – most likely.

Now both these elephants paint what looks like another elephant. Do they know this is what they’re painting? Are they deliberately trying to paint themselves? (Or their mothers, sisters, etc.) Well there’s two aspects to this question – yes animals can recognise other members of their own species, but they don’t see in the same way we do. For example, you have to take very high definition photographs of a chicken before it will recognise it (D’Eath, 1998). In that case, unless something looks ‘realistic’ to a chicken, they don’t recognise it as a representation of their species.

You can train dogs and parrots to recognise that the phrase ‘blue’ refers to the colour ‘blue’ and various shapes (Pepperberg et al, 2000) but I question the difference between being able to identify the concepts and knowing the sound-object-colour associations. You could train an elephant to associate that particular shape with other elephants, but that doesn’t mean that it conceptually indicates elephants.

However, it is considerably simpler to imagine that these elephants have been taught to paint this shape (considering they all seem to paint the same thing), which is pretty cognitively impressive regardless.

Lastly – is it rewarding for the animal? I already pointed out what looks like a bit of a stereotypy and by all my interpretations above these are captive wild animals performing for their supper. From my point of view, I decry Blackfish for this exact thing. This is just marketed as earthy and vaguely ‘ethnic’, and not at all corporate like SeaWorld. Here we have a very intelligent animal being given a series of instructions that it has learned the appropriate responses to. I don’t see it as anything more.

 

However cats painting looks hilarious.

Fluffy Friday – Personality and Trait Theory

Oh, hey, Crash Course have done a video about measuring personality

 

That’s kind of my thing.  Although the video talks more about trait theory and self than concepts behind how we measure it, which is what I’m interested in, it’s pretty cool. It notes that traits are used to ‘predict behaviour and attitude’ but it doesn’t really get into the idea that we’re only using models and hence the models are infinitely variable. The simpler your model (e.g. the big five personality traits) you have less power to predict specific behaviours, but it’s general enough to apply to most humans and even some other animal species. That’s why we tend to go for the two-trait model for animals (also known as active/passive coping).

Anyway, I’m just bitter because my massive paper on the subject hasn’t been published yet.

And it would be really cool if we could try floating imaginary scenarios past animals . . .

I Lectured to 27000 Students This Week

Please do forgive the bragging in this post’s title. But I have. I have lectured to 27000 students this week.

Our MOOC went live on Monday and people are still signing up, which is all kinds of mindblowing. From the moment that button was pressed on Monday morning, people have been meeting in the forums, watching our videos, working through our interactive sessions and this has been one of the most gratifying experiences of my career.

The diversity of people on the course has amazed me, from the school kids who help out in animal shelters at the weekend, to people who have real power and influence on a global scale, and what’s more – these people are talking to each other in the same thread.

I have a confession to make. Before this week began, this post was going to be full of summary numbers, bragging, essentially, about our reach. Because I’m really proud of that. But I had always seen the MOOC as a bit of a ‘flash in the pan’. I was pleased it was on my CV, and pleased that it was running, and I was sure our students would enjoy it and would learn something, but I thought MOOCs as a concept were going to fizzle out.

I’ve changed my mind. While advertising the MOOC a little while ago I said it would ‘democratise education’. I was using buzzwords, but I don’t think I was far off. The discussions we’ve been seeing on the forums has shown me that people are genuinely interested in learning science, and will be passionate as they engage with that science. There are people logging on from areas that are threatened with terrible violence. Little girls in countries that don’t have equal rights for women. Yes, it’s ‘just’ an introductory course, but its real strength lies in its community, in the learners who are taking it and using it to build their support networks. MOOCs have a huge amount of power, not because they allow universities to share their research, but because they invite universities into peoples homes.

As somebody who has lived with universities for all of my adult life, I had underestimated this. We might complain about student fees and the business like nature of the modern university, but they are still places of tremendous innovation and power. And I am so, so proud of what our students are doing.

Fluffy Friday – Peer Review Rings and MOOCs

You’ll have to forgive the lack of original content in this week’s Fluffy Friday (and lack of content entirely in last week’s). The MOOC launches on Monday at 11 AM and this week has been spent polishing the course and obsessing over comma placements and going a little bit hysterical after watching ourselves present over and over. One of our hysterical moments was remembering filming this introductory video – you’re never more aware of your face than when you’re being filmed in the background!

 

But in other science news there has been yet another peer review scandal, this one reported by the Washington Post. The Journal of Vibration and Control (I will not make a joke, I will not make a joke) was apparently victim to a peer review ring, where a scientist made up false aliases to give himself and colleagues favourable reviews. Publishers SAGE have released a statement where they say:

While investigating the JVC papers submitted and reviewed by Peter Chen, it was discovered that the author had created various aliases on SAGE Track, providing different email addresses to set up more than one account. Consequently, SAGE scrutinised further the co-authors of and reviewers selected for Peter Chen’s papers, these names appeared to form part of a peer review ring. The investigation also revealed that on at least one occasion, the author Peter Chen reviewed his own paper under one of the aliases he had created.

 

What I would give to have been a fly on the wall when they came up with that idea. I imagine it happened in the pub as it was closing, a group of scientists huddled around their pints, and as they get hustled from their barstools one of them comes up with the inevitable words “Why don’t we just review our own papers?

I think Kevin Spacey should play that scientist in the movie.

Statistics Continued

Interestingly enough after last week’s post there is a brilliant article in the BBC magazine about doctors and their understanding of statistics.

Gerd Gigerenzer is one of those names in statistics I trust. His discussion of risk is fascinating. Take the example mentioned in the article:

As a doctor, you know the following facts to be true:

 

  1. The probability that a woman has breast cancer is 1% (“prevalence”)
  2. If a woman has breast cancer, the probability that she tests positive is 90% (“sensitivity”)
  3. If a woman does not have breast cancer, the probability that she nevertheless tests positive is 9% (“false alarm rate”)

 

 

When a 50 year old female patient, who has no other symptoms of breast cancer, has a routine mammogram, she tests positive. Alarmed, she asks you what her risk is? Which of the following is the best answer?

  • nine in 10
  • eight in 10
  • one in 10
  • one in 100

 

If, like me, you read this at lunch with a box of strawberries with one eye on your MOOC numbers, you probably said ‘nine in ten’. In fact the answer is ‘one in ten’. Why is this the case?

Well first remember that if there are a hundred random women in a room, the prevalence of the disease in the population suggests that one of them will have breast cancer. Second, remember that if we test the same hundred women, we will have nine women testing positive who don’t have the disease, and the woman who does have the disease has a 90% chance of testing positive (meaning that it’s possible she won’t test positive).

So with no other symptoms to go on, and remembering that it’s likely that 10 of our hundred random women would test positive (one because she does have cancer and the other nine because they get false positives), the best estimate of whether this patient has cancer is actually one in ten. She might be the true positive. But nine times out of ten she’s the false positive.

 

It’s an excellent teaching opportunity and the maths make sense when you think about it, but it’s keeping the populations separate in your head that makes it difficult.

In other news, I picked up Andy Field’s ‘Discovering Statistics Through R’ and I’m really enjoying it so far.

How To Teach Me Statistics

A few weeks ago I was swearing at my computer and had to go buy a Twix bar from the canteen to calm myself. There was some frantic chocolate scoffing that afternoon.

The source of my irritation? Statistics. I am not a great wielder of statistical power, but I am very interested in their dark arts. This leads to the common situation where I know I’m doing something wrong, such as using stepwise regressions to build a model, the fact I use frequentist over Bayesian probabilities, and even my over reliance on P Values to communicate scientific results, but I just don’t know how to do it better.

I’m expecting there are three reactions to that sentence. The first is “I don’t have a clue what any of that means”. Don’t worry, my grasp of it is very shaky, and it’s not something I’ve ever been taught. It’s something I’ve discovered through hanging out with statisticians.

The second is “Man, I have that exact same problem, but every time I try and learn how to do it, I can’t figure it out.” My friends we are in the same boat. I do not feel I have enough statistical training to tackle these problems.

And lastly the third kind of person is reading that and thinking “Well obviously the answer is *string of gibberish*”

I have had good stats teachers, but they are sadly few and far between, and there are a lot of poor stats teachers who get in there in the mean time and deeply confuse me. I have a lot of good friends who try to teach me and I end up glazing over. What I mean to say is that the following is not personal – and it’s as much a criticism of myself as those who have tried to teach me . . .

Loads of statistically savvy people are willing to teach, they just don’t seem to get it through to me. So seeing as I’m supposed to be quite good at this education malarky, here’s my guide to teaching me statistics.

 

Make Sure We’re Speaking a Common Language

Yes, we really have to start with the basics here. Statistical language is incomprehensible to me. And that’s because we’re all taught differently.

As an example, I refer to response variables as ‘y’ and explanatory variables as ‘x’. A good friend of mine refers to explanatory variables as ‘y’ and response variables as ‘a’ or ‘b’. This causes huge confusion whenever we ask one another stats questions off the cuff.

And the common language refers to more than just making sure I understand what your big formulas are saying. This is what the homepage of R looks like. R is a sophisticated and free statistical tool that we should all be using. I’ve seen more intuitive GeoCities layouts. This is written by and for coders and I have to explain how to extract a zip file to some of my colleagues.

Why are you writing your R manual or your page about your fancy new statistical technique? Are you trying to share it with others who think like you? Fine, carry on. Are you trying to improve the statistical techniques used by frustrated, busy scientists who haven’t had more than a few week stats CPD a year?

Use your words.

Now the R Book is a good start for people wanting to learn R but I still wish it was written by Andy Field, who’s Discovering Statistics book is still my favourite bible, even though I don’t use SPSS anymore. If you’ve read both, you’ll see the difference in style is extreme, and I think it’s because, as a social scientist, Field has a better grasp of how people think. (Although speaking of GeoCities sites . . . I still love the book!)

Edited to Add: I lie! Andy Field has written an R textbook, which I have just bought! Thanks to Comparatively Psyched for the heads up! 

 

Teach Me Something I Can Use

This may seem counterintuitive to what I said further up, but if you’re trying to teach me, say, an alternative method to a stepwise regression, don’t just give me a dataset and tell me the code to run.

Tell me how to arrange my dataset in the way its needed. Ask me questions about my data – get me thinking about the complexities of the experiment I designed. And then tell me the code to run. Don’t forget to walk me through the output. For example, the documentation for the lars package in R explains how I can run a least angle regression on a sample dataset. Great. I can copy and paste that code ad libitum. Can I get it to work on my data? Even though to the best of my knowledge I’ve arranged it in the same way? Nope.

Get me to work through the whole process and you show me where your new method fits into my life.

 

What’s the Application?

I recently sat through a stats seminar where someone was showing off a new method. In the same presentation they briefly glossed over ternary plots as a way of showing off new data.

Applied scientists work in a world that judges us on the number of papers we produce and the impacts our papers have. That is literally how we get our baseline funding.

I don’t disagree that there are lots of problems with publishing but you’re asking me to relearn how I think about statistics, and then to communicate all this in a real-world paper with real-world data (that doesn’t always play nicely). If you’re asking somebody to use an amazing new technique, you’re asking them to get that past reviewers (who more often than not will not know your new stats).

If you have a great technique but it won’t actually give me a conclusion that I can use to improve animal welfare, then it’s not going to help me. And related to this . . .

 

What does it Mean?

The truth of the matter is that the statistical tests we commonly use are ‘plug and play’. We get into the habit of checking the things we want to look at noting the laundry list of caveats in a footnote.

Walk me through an example of what my results mean. If you’ve got me using my own data, tell me if this result confirms or denies my hypothesis, show me why, give me some indication of the next step.

I’m amazed at how many people don’t do this when trying to explain stats to me. You’re interested in the method, I get that. I’m fascinated by recording aggression in groups, but there’s a time and a place to discuss this, or just to tell you what aggression means.

 

Don’t Assume I’m Stupid

I see this all the time when statisticians are trying to teach something to scientists. They spend a very long time on the basics because our fundamentals are so scattered. This is not the most helpful approach. The other method I often see, when I say I don’t understand or even hesitate, the statistician repeats what they’ve said, more slowly and slightly louder.

We’re not stupid. Try teaching us a complex problem in an environment we’re familiar with (i.e. with our own data) and you’ll be surprised how many fundamental skills we’ll pick up because of it. To use a simple analogy, if you wanted to teach me how to maintain a car, wouldn’t you be be better off showing me how to take an engine apart rather than build one from scratch?

Don’t spend half our time explaining the problem to me – I get that there is a problem with the statistics I already use, it’s why I’ve sought you out. Is a finer understanding of the theory really going to help me use this test in future?

 

Finally – Why Are You Teaching Me?

This blog post sounds very whiny. Trust me, I know.

I know I should have learned all this earlier in my career. I know I should use R every day until I’m fluent. I know I shouldn’t using all these out of date stats. But the sad truth is that I haven’t, I don’t and I can’t.

I want to change, and I need the great community of statisticians to help me. So if you’re a statistician who wants to help me and people like me, this is how I’d suggest doing it.

Good luck!

Fluffy Friday – MOOC Countdown

In preparation for our MOOC, we’ve become a little obsessive. Every time I check the student count the numbers go up – we’re currently sitting at a staggering 19,129 students and roughly 6.7% of you have taken part in our little data gathering exercise we’ve sent out on the emails – so a big thank you for that.

At the moment you come from 153 different countries, and you span the age ranges of 13-70+.

We are so excited to meet all of you, and I have a little clip from the Jeanne Marchig YouTube channel of our third VLog.

Fifty Thoughts You’ll Have Writing a Grant Proposal

As an early stage researcher, there are a number of thoughts that will cross your mind as you try to write a grant. Allow me to demonstrate.

  1. That grant is totally relevant to me!
  2. Oh, but the deadline is next month.
  3. Still, that’s so relevant. Damn did they write this call for me?
  4. OH MY GOD LOOK HOW MUCH MONEY IS IN THAT POT
  5. I could get a mortgage if I got this grant.
  6. That’s Han Solo levels of wealth right there.
  7. That’s it. I’m applying. What’s there to lose, right?
  8. Well I might lose my early stage researcher status.
  9. Is there a thing about that?
  10. Why are all these guidelines so long? Jesus.
  11. Ok, I’m probably still eligible for early stage researcher status if I apply.
  12. Is this idea going to work? I will have to bend it a bit.
  13. What are the buzzwords of the month?
  14. Can I link this to climate change?
  15. I can probably link this to climate change.
  16. I’m really not sure if this idea is going to work.
  17. Hell, I’m going to email Betsy about it, Betsy’s pretty cool, she’ll tell me more.
  18. Unless she decides she wants to apply for it.
  19. Maybe I could email Colin, Colin’s always really helpful.
  20. I should probably bring this up at a team meeting.
  21. But I’ve only got a month till the deadline.
  22. Fuck it. Write a grant and take it to the boss. What can go wrong?
  23. Hmm. Will they accept that the aim of this project is to keep me in booze for the foreseeable future?
  24. Damn, only 700 words to write my outline, that’ll be tough.
  25. Um.
  26. What is my aim again?
  27. Why is this so hard? I wrote a bloody thesis. I write for a living. God. Maybe a cup of tea will help.
  28. 600 words to go.
  29. I’ll do a literature search! I’m amazing at literature searches.
  30. Wow, quite a few people have done this.
  31. Okay, that is in fact my idea.
  32. They did it in the eighties.
  33. Well I’ll update the research.
  34. I should have emailed Betsy.
  35. Okay I can write this.
  36. I survived the PhD, I can survive being a real academic.
  37. Mendeley, why are you so awesome?
  38. Okay it’s done! It only took me . . . oh my god the deadline is tomorrow.
  39. Quick! What does the boss think?
  40. What do you mean, the grant doesn’t fund overheads?
  41. DAMN IT.
  42. Maybe we can repurpose it for a Fellowship?
  43. Yeah, that experiment is way too expensive, I should take it out.
  44. And Colin says this other experiment won’t work because he tried it twenty five years ago and never published the non-results.
  45. I can see that mortgage slipping through my fingers.
  46. Oh yeahhhhhh, technician time.
  47. No I did not budget technician time.
  48. Yes the project would pretty much demand a whole three technicians.
  49. What do you mean the uni already put a proposal in to this grant?!?!?!
  50. Fuck it. Write it up as a M.Sc project for next year and hope you get a good student.

Badger Friday – Part Five

Whoops. The eagle eyed viewer might have noticed we didn’t have a Fluffy Friday last week, but now you can finally find out what happens to Fluffy and her babies:

 

 

And we’ve also got a Jeanne Marchig International Centre for Animal Welfare Education YouTube channel now! (Incidentally, saying the name of the channel might be my new test of sobriety). So you can watch all our VLogs in one place. If you haven’t signed up for the MOOC you totally should because we are ready to go on the 14th July!