Search
  • Feature
  • Azrieli Program in Brain, Mind & Consciousness

Q&A with Melvyn Goodale

by Alona Fyshe
Aug 1 / 18
This Q&A is part of CIFAR’s series on building a research lab.

Alona Fyshe is a CIFAR Azrieli Global Scholar in CIFAR’s Azrieli program in Brain, Mind & Consciousness and an Assistant Professor in the in the Department of Computing Science and Department of Psychology at the University of Alberta. Melvyn Goodale is Program Co-Director of CIFAR’s Azrieli program in Brain, Mind & Consciousness and Distinguished University Professor, Canada Research Chair in Visual Neuroscience, and Director of the Brain and Mind Institute at Western.

Alona Fyshe (AF): Could you tell me about your first faculty position and what it was like?

Melvyn Goodale (MG): I finished a post-doc at Oxford. I had come from Western where I did my PhD, and was trying to get back to Canada, but there were no jobs available. I ended up at St. Andrew’s in Scotland, which turned into a wonderful opportunity.

I went there in 1971. At that time I worked on rats and hamsters. And there was no animal lab there. So at the tender age of 27, I was charged with helping plan an animal facility. I had to work with the architects, and with the Home Office inspector, because in Britain you need a government dispensation to work on animals.

Melvyn Goodale
Brain, Mind & Consciousness program Co-Director, Melvyn Goodale

AF: Could you tell me about your first graduate student?

MG: I had only one graduate student there, who was a friend, and slightly older than me! We did several published experiments together, looking at interocular transfer in the brains of pigeons.

AF: When did you return to Canada?

MG: I arrived back in ‘77. And, again, I was still working on animals but there were full facilities set up. I applied to NSERC and got some support.

AF: How did you begin to work on human patients?

So at the tender age of 27, I was charged with helping plan an animal facility.

MG: I took a year in which I eventually became a registered clinical neuropsychologist and worked on human patients.

And that was a big learning experience for me because I was an animal guy. I had been working on rats and gerbils and so on and they were easy. Working with patients was a totally different thing. I had a kind of broom closet up in the hospital where I did my work, doing eye movements and pointing movements, looking at the visual control of reaching. It was a very different experience and it was a pretty steep learning curve. In those days, it was all neuropsychology and behaviour, and now, of course, there’s fMRI and other kinds of neuroimaging as well.

AF: Can you tell me more about your current role?

MG: I’m now the Director of the BMI Institute. The whole culture of doing research has changed. Before, it was very much an individual effort. In the late '80s and the early '90s, it became more and more “big science”, with a number of authors on papers. Today, you can’t have your own fMRI or, in some cases, even your own EEG.

But even more important is sharing your lab. You do have your own space now with workstations, and you might have one set up where you do eye movement recording or something of that kind, but more and more the equipment is being shared.

I think the reason we've been successful is that the Brain and Mind Institute is regarded as a core facility, so that anyone can use the equipment. That has its challenges where, for example, people buy a piece of equipment from a major grant, and feel a certain proprietary interest in it. We try to have people collaborate so they will learn how to use all the different pieces of equipment, because it's silly to have a very expensive piece of gear which is used four or five times a week for an hour or so. It’s better if other people use it as well.

I think that psychologists, and cognitive neuroscientists in particular, have to learn to do that if they are going to succeed. The character of science now is very interdisciplinary.

BMC_AlonaFyshe
CIFAR Azrieli Global Scholar, Alona Fyshe

AF: That makes me think of astronomy, how teams have to share the gigantic telescope.

MG: One of the things we've started is a post-doc research fund where, if a couple of post-docs from different labs propose a collaboration, they can apply for support. We have two or three awards a year, and they get maybe two or three thousand dollars. But that's enough for quite a few imaging sessions.

It's been quite successful in that the post-docs run the whole thing. A committee reviews the applications and gives feedback to the applicants, who are also post-docs. So, it's kind of like a baby NSERC.

AF: I had never reviewed a grant until after I wrote my first grant. But it would have been really instructive to have had the opportunity to review.

MG: Exactly. It gives post-docs some experience in reviewing grants and in making hard decisions about their peers.

AF: What do you think of the new trend towards data sharing in brain imaging?

MG: Oh, it's great. It should be a lot more transparent. It's difficult because people use different protocols and machines. But there is clearly a movement to do that within Canada.

I think it should be the case for behavioural data, as well, which is even harder because paradigms are so unique. But certainly, the journals now require that you log your data in a way in which people can analyze your data or put them together with other data.

AF: As a young person starting out, you might feel like it's hard to let other people at your data. Do you know what I mean?

MG: I don't know, do you feel that way? I mean, why would you say that?

AF: Computer science is pretty open with data, but it's a different scenario. It takes less time to collect data.

MG: Right. But I think the payoff is that people can look at it a different way and discover something that you hadn't thought of. It means that fewer animals have to be used if you've got all this data stored in a place where people can access it.

So, I'm all for it and I suppose the big fear is that someone will find a flaw in my study, but that's good. It's better than having some flawed conclusion out there.

AF: And what about the feeling that you want accolades for the data you collect?

MG: I would be very clear about who collected it. You are resting your conclusions on data collected by someone else.

AF: I think it's interesting that you switched fields -- and pretty early in your career.

MG: I did. There was a period of overlap where I was doing both work on animals and humans. But when you demonstrate something about the way the brain works in humans, the public gets much more engaged with it than they do when you have demonstrated it in a rat -- or even a monkey.

What we're trying to do as psychologists is to talk about the human condition. If you can do it in humans, then that's better. You don't want to sacrifice animals unnecessarily. We still need animals in a lot of things that we do. But at the same time, we're getting better and better at imaging and even recording from single cells in patients who are undergoing epilepsy surgery. We're learning a lot that we could never have done even a decade ago.

AF: Would you say you are inspired when people are inspired by your work?

MG: I think when newspapers and the general public and other sciences take notice of our work, it's definitely encouraging. I'm not making any claims that people should direct their research towards the utility of society, however. I'm a great proponent of fundamental research.

I had been working on rats and gerbils and so on and they were easy. Working with patients was a totally different thing.


AF:
So, can you think of any mistakes you made early on that you want to share?

MG: I think my great regret is that I did not do enough math when I was young. My advice is to get as much math and physics and computing science under your belt as you can, because you can always learn biology. Biology is just a bunch of facts. Whereas learning math and computational science is like learning French; you have to learn it early.

AF: I like that advice since I do a lot of math.

MG: But I'm actually pretty satisfied with the way things have turned out. I was lucky to be there at the beginning of fMRI and so on, and we were encouraged by the university to set up all of this stuff.

AF: And you think a lot of that came about because the institute or the university was encouraging you to go in this direction?

MG: They were encouraging us to do our science. I don't think they said cognitive neuroscience is the answer. They recognized that we were doing well and that it was clearly getting attention outside the university.

I have never regretted not going elsewhere. I think people sometimes feel that it's important to be at Harvard or Yale or McGill or Cambridge. I don't think that's as important as having a critical mass at any respectable university where you are making some impact on science in the world.

AF: It’s nice to hear that it's possible to build something even if you're not somewhere known for whatever you're doing.

MG: You can't do it yourself, you've got to have a group of people. For us it took off in the late '90s. We were one of only four centres in Canada that had high field imaging at the time so we were ahead of the game.

But then we got a CIHR group grant in the days when they had group grants. We had collaborators at York and Wilfred Laurier. Suddenly we were able to hire post-docs and it just took off.

It was called GAP, the Group on Action and Perception. You know, we were doing something that other people weren't, which was looking at in a sense what the brain is for, which is the control of movements.

When the group grant program ended at CIHR, there was a very successful group at Queens. We decided that we would not compete for the small amount of remaining funds, that instead we would combine forces. We formed the Canadian Action Perception Network, CAPnet. At first it was just Queens, York, and Western. We were kind of like an amoeba. We put out a pseudopod every so often where there was some money, but we wouldn't compete. We would just form ourselves in different ways in order to chase the frugal funds. CAPnet is now Canada-wide.

AF: It's sort of like a meta example of collaboration, the sum being greater than the parts?

MG: Absolutely. We are always competing somewhat when we apply for individual grants, but when there is big money on the table, it's better to try and see if you can chase it together rather than all competing for the same buck.

It also means that we've exchanged our post-docs and grad students, and there has been a lot of cross-fertilization. We share protocols and ideas for imaging and behavioural testing. We've been doing it now for more than seven years.

AF: You can start a new network? With your academic children?

MG: Exactly. Actually, I have academic great-grandchildren now, which is kind of scary.

AF: When you think about running a group, do you think there is a, sort of a better way to structure things, thinking hierarchically about post-docs, grad students, undergrads?

MG: As the BMI Director, that's been my pattern. I'm busy so I have three and a half really good post-docs. Typically, I involve them with supervision of incoming graduate students. When they first get here and they don't know what's going on, I give them to a post-doc who is more at the front line of the research than I am.

My graduate students and post-docs supervise honour students. I meet with them once every couple of weeks, but they've been doing most of the heavy lifting in terms of showing them the ropes.

With big science these days, particularly imaging, the whole approach to supervision of post-docs and grad students has changed. In the old days I could say, can I see the spreadsheet; can we do this, can we do that? I can't do that now. There is such a distance between the data collection and what they show me. It means that enormous trust has to develop, because I can't check their work.

AF: Exactly. You can check it on a sanity check, but to actually run the analysis again, you can't.

MG: It's not just that I have no time, it's because I haven't been there in the trenches. There is a big distance between the data and me.


We are always competing somewhat when we apply for individual grants, but when there is big money on the table, it's better to try and see if you can chase it together rather than all competing for the same buck. 

AF: Do you think there is anything we are missing in the training of our upcoming researchers that would help them build their new labs?

MG: The more opportunities you can give your grad students and post-docs to learn the soft skills they need to function as an independent researcher, the better. In particular how to give a good job talk, how to be interviewed, how to write a good resume, how to write a good research or teaching plan. We give people as much practice as we can giving talks all the time.

AF: And also, the supervising that you let them do.

MG: They learn that not everyone can supervise the same way. Some people won't take a step unless you direct them. Others want to just be cut loose. You have to find some way of making sure that the person who wants to be completely independent doesn't fall off a cliff. And you have got to encourage independence in those that need their hands held. You also have to realize that you don't have to like everybody. There are some people I don't get on with. Nevertheless, I've found that if I work at it and we are collaborating or I'm supervising them, I've learned how to manage that.

AF: What do you do if you end up in a collaboration that you feel is not working?

MG: Most of the time when that's happened it's evaporated through inaction on my part. And that's happened even with people I want to collaborate with.

AF: So, we're running out of time but I wanted to give you one last chance to tell me anything you think you would like new faculty to know or think about as they start their labs.

MG: I know that there is a lot of hard negotiating that goes on now, particularly in big universities like ours, where they try to get out of teaching as long as possible. I think to avoid teaching is a huge mistake. I know that some of my best ideas come from teaching. You think you know something until you have to teach it, and then you discover huge leaps of logic that you made when you try to explain it to a bunch of undergraduates.

I think we're in danger of creating a two-tiered system where there are successful researchers who don't teach and get all this protected research time, and then people who are slogging in the trenches and can never recover, because they are teaching so heavily that even if they get a great research idea and some tentative funding, it's hard for them to find time.

At Harvard and Yale, they don't let you buy out of teaching. I think that's important.

AF: That's definitely something I have struggled with. Teaching takes just so much time.

MG: But see, but you get better with more teaching. And as you develop a module of stuff you teach, then you just have to update it every year. Then every three or four years you do a big spring cleaning, as it were, and really jazz up the stuff and go in slightly different directions. I don't teach now because I am so busy, but I taught for 30 years and I enjoyed it immensely.

Read more articles in the Building a Research Lab Series