Monthly Archives: June 2018

How do you plan to use your PhD?

If you follow my blog or medium account, you’ve probably already read some of my thoughts and musings on the topic of running a research lab, training graduate students, and being a mentor. I think I wrote about that just a few weeks ago. But if you haven’t read any of my previous essays, let me provide some context. I’m professor of Psychology at a large research university in Canada, the University of Western Ontario. Although we’re seen as a top choice for undergraduates because of our excellent teaching and student life, we also train physicians, engineers, lawyers, and PhD students in dozens of field. My research group fits within the larger area of Cognitive Neuroscience which is one of our university’s strengths.

Within our large group (Psychology, the Brain and Mind institute, BrainsCAN, and other groups) we have some of the very best graduate students and postdocs in the world, not to mention some of my excellent faculty colleges. I’m not writing any of this to brag or boast but rather to give the context that we’re a good place to be studying cognition, psychology and neuroscience.

And I’m not sure any of our graduates will ever get jobs as university professors.

The Current State of Affairs

Gordon Pennycook, from Waterloo and soon from University of Regina wrote an excellent blog post and paper on the job market for cognitive psychology professors in Canada. You might think this is too specialized, but he makes the case that we can probably extrapolate to other fields and counties and find the same thing. But since this is my field (and Gordon’s also) it’s easy to see how this affects students in my lab and in my program.

One thing he noted is that the average Canadian tenure-track hire now has 15 publications on their CV when hired. That’s a long CV and as long as long as what I submitted in my tenure dossier in 2008. It’s certainly a longer CV than what I had when I was hired at Western in 2003. I was hired with 7 publications (two first author) after three years as a postdoc and three years of academic job applications. And it’s certainly longer than what the most eminent cognitive psychologists had when they were hired. Michael Posner, whose work I cite to this day, was hired straight from Wisconsin with one paper. John Anderson, who’s work I admire more than any other cognitive scientists, was hired with a PhD from Yale and 5 papers on his CV. Nancy Kanwisher was hired in 1987 with 3 papers from her PhD at UCLA.

Compare that to a recent hire in my own group, who was hired with 17 publications in great journals and was a postdoc for 5 years. Or compare that to most of our recent hires and short-listed applicants who have completed a second postdoc before they were hired.  Even our postdoctoral applicants, people applying for 2-3 year postdocs at my institution, are already postdocs and are looking to get a better postdoc to get more training and become more competitive.

So it’s really a different environment today.

The fact is, you will not get a job as a professor after finishing a PhD. Not in this field and not in most fields. Why do I say this? Well for one, it’s not possible to publish 15-17 papers during your PhD career. Not in my lab, at least. Even if added every student to every paper I published, they will not have a CV with that many papers, I simply can’t publish that many papers and keep everything straight. And I can’t really put every student on every paper anyway. If the PhD is not adequate for getting a job as a professor, what does that mean for our students, our program, and for PhD programs in general?

Expectation mismatch

Most students enter a PhD program with the idea of becoming a professor. I know this because I used to be the director of our program and that’s what nearly every student says, unless they are applying to our clinical program with the goal of being a clinician. If students are seeking a PhD to become a professor, but we can clearly see that the PhD is not sufficient, then students’ expectations are not being met by our program. We admit student to the PhD with most hoping to become university professors and then they slowly learn that it’s not possible. Our PhD is, in this scenario, merely an entry into the ever-lengthening postdoc stream which is where you prepare to be a professor. We don’t have well-thought out alternatives for any other stream.

But we can start.

Here’s my proposal

  1. We have to level with students and applicants right away that “tenure track university professor” is not going to be the end game for PhD. Even the very best students will be looking at 1-2 postdocs before they are ready for that. For academic careers, the PhD is training for the postdoc in the same way that med school is training for residency and fellowship.
  2. We need to encourage students to begin thinking about non-academic careers in their first year. This means encouraging students’ ownership of their career planning.  There are top-notch partnership programs like Mitacs and OCE (these are Canadian but programs like this exist in the US, EU and UK) that help students transition into corporate and industrial careers. We have university programs as well. And we can encourage students to look at certificate program store ensure that their skills match the market. But students won’t always know about these things if their advisors don’t know or care.
  3. We need to emphasize and cultivate a supportive atmosphere. Be open and honest with students about these things and encourage them to be open as well. Students should be encouraged to explore non-academic careers and not make to feel guilty for “quitting academia”.

I’m trying to manage these things in my own lab. It is not always easy because I was trained to all but expect that the PhD would lead into a job as a professor. That was not really true when I was a student but it’s even less true now. But I have to to adapt. Our students and trainees have to adapts and it’s incumbent upon us to guide and advice.

I’d be intersted in feedback on this topic.

  • Are you working on a PhD to become a professor?
  • Are you a professor wondering if you’d be able to actually get a job today?
  • Are you training students with an eye toward technical and industrial careers?


The Unintended Cruelty of America’s Immigration Policies


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It is well documented that the Trump administration is pursing a senselessly cruel policy of prosecuting migrants at the border, detaining families, and incarcerating them in large, improvised detention centres. This includes taking children away from their parents and siblings and housing them separately for an extended period.

Pointlessly Cruel

Jeff Sessions has pointed out that this policy is “simply enforcing the law” and that it’s a deterrent. He lays any negative conseqences on the migrant families themselves, asking why they would risk bringing their children on this long and dangerous trek. Other members of the administration have pointed out that families who claim asylum at ports of entry are not being detained or split apart. This too is disingenuous, as the Trump administration has narrowed the reasons for asylum, and as the border has become increasingly militarized, migrants and asylum-seekers are being forced away from busy ports of entry and often into dangerous crossings.

 How did we get to this point? How did a nation which once prided itself on welcoming immigrants become a nation increasingly looking to punish individuals even as they seek asylum? Although some aspects of this cruel policy have long been present in America’s history, I think that particular fixation on migration from Mexico stems from an unintended starting point.

Unintended Consequences

A recent podcast by Malcolm Gladwell explored the causes and effects of the militarized US-Mexico border. I found this podcast fascinating and I recommend listening to it. To summarize, for most of the 20th century, into the 1960s and 1970s, migration between the United States and Mexico was primarily cyclical. Migrants from rural areas near the border in Mexico would move to the United States for work, stay for a few months, and move back to Mexico with their families. This was an economic relationship and it worked because the cost of crossing the border was essentially zero. If you are apprehended, you’d be returned but otherwise it allowed for the flow of migrants into the United States and out of the United States.

In the early 1970s, however, the US-Mexico border began to be militarized. It happened almost by accident. An extremely skilled and dedicated retired Marine General took over immigration and naturalization services and began to tighten up the way in which border patrols operated. There was never any intent to cause suffering.  On the contrary, the original intent seem to be to harmonize border enforcement with existing law  in a way that benefited everyone. But what happened was that as the borders became less porous, migrants began seeking out for dangerous border crossings. Often these were in the high desert where risk of injury and death was higher, as the cost of crossing the border back and forth increased due to this danger, migrants were less likely to engage in cyclical migration but rather stayed in the United States and either send money home to Mexico or brought their families here.

This has profound implications for the current state of affairs. As each successive administration cracks down on illegal immigration, tightens the border, and militarizes the border patrol, it increases the risks and costs associated with crossing back and forth. Migrants still want to come to America, people are still claiming asylum, but illegal immigrants in the United States are persecuted and stay in hiding. Every indication is that the worst possible thing that could be done would be the actual construction of a wall.  In some ways, an analogy can be drawn to desire paths in public spaces. There is a natural flow to collective human behaviour. Civic planning and architecture does not always match, but human behaviour will always win out. People will continue to migrate and this will continue to be a problem.

Gladwell doesn’t say this, but it seems to me that the most rational and humane solution is a porous border. In a porous border, illegal immigrants are turned back when apprehended, but in a straightforward way. People are not apprehended and put into detention centers. Families are not charged with committing a misdemeanour offence and jailed prior to their hearings necessitating the removal of the children. In a porous border, there is still border security but the overall level of enforcement is lower.  In addition, a policy like this could benefit from increased access to green cards,  recognizing that many migrants wish to work in the United States for a few months. Unfortunately, no one in the Southwest (or anywhere else in America) is going to win an election with the promise of “Let’s make our border more porous and engage in lax border security.” That will not sell. But the evidence presented by the Mexican migration project and reviewed by Gladwell in his podcast suggests this would still be the most rational solution.

More Objective Research

This is one of those cases where we need more objective policy research, less political rhetoric. Has anyone asked an algorithm or computer model to determine the ideal level of border security? How much flow is tolerable? How does one balance economic detriment to having a relatively free flow of migrants with the costs associated with apprehension detention and deportation, and any associated criminal proceedings. The latter are expensive and human-resource intensive. Do to the risks of a porous border justify these expenses?

The thing is, these are computational problems. These are problems that demand rigorous computational analysis and not moralistic grandstanding about breaking the law for fears of drugs and criminals poring over the border.

The evidence seems to suggest that for decades, the relatively porous border had no ill effects on American society and was mutually beneficial to the US and to Mexican border regions. Though unintended, the slow militarization of the US-Mexico border restricted migration, made it more dangerous, which led to real costs illegal immigration thus necessitating a stronger more militaristic response, which creates a feedback loop. The harsher the enforcement the worse the problem gets.

The current administration has adopted the harshest enforcement yet, one that in my view is intentionally cruel, is a clear moral failing, and one that may be destined to fail anyway.

The fluidity of thought

Knowing something about the basic functional architecture of the brain is helpful in understanding the organization of the mind and in understanding how we think and behave. But when we talk about the brain, it’s nearly impossible to do so without using conceptual metaphors (when we talk about most things, it’s impossible to do so without metaphors). 

Conceptual metaphor theory is a broad theory of language and thinking from the extraordinary linguist George Lakoff. One of the basic ideas is that we think about things and organize the world into concepts in ways that correspond to how we talk about them. It’s not just that language directs thought (that’s Whorf’s idea), but that these two things are linked and our language also provides a window into how we think about things. 

Probably the most common metaphor for the brain is the “brain is a computer” metaphor, but there are other, older ideas.

The hydraulic brain

One interesting metaphor for brain and mind is the hydraulic metaphor. This probably goes back at least to Descartes (and probably earlier), who advocated a model of neural function whereby basic functions were governed by a series of tubes carrying “spirits” or vital fluids. In Descartes model, higher order thinking was handled by a separate mind that was not quite in the body. You might laugh at the ideas of brain tubes, but this idea seems quite reasonable as a theory from an era when bodily fluids were the most obvious indicators of health, sickness, and simply being alive: blood, discharge, urine, pus, bile, and other fluids are all indicators of things either working well or not working well. And when they stop, you stop. In Descartes time, these were the primary ways to understand the human body. So in the absence of other information about how thoughts and cognition occur it makes sense that early philosophers and physiologists would make an initial guess that thoughts in the brain are also a function of fluids.

Metaphors for thinking

This idea, no longer endorsed, lives on in our language in the conceptual metaphors we use to talk about the brain and mind. We often talk about cognition and thinking as information “flowing” as in the same way that fluid might flow. We have common expressions in English like the “stream of consciousness” or “waves of anxiety”, “deep thinking”, “shallow thinking”, ideas that “come to the surface”, and memories that come “flooding back” when you encounter an old friend. These all have their roots (“roots” is another conceptual metaphor of a different kind!) in the older idea that thinking and brain function are controlled by the flow of fluids through the tubes in the brain.

In the modern era, it sis still common to discuss neural activation as a “flow of information”. We might say that information “flows downstream”, or that there is a “cascade” of neural activity. Of course we don’t really mean that neural activation and cognition are flowing like water, but like so many metaphors it’s just impossible to describe things without using these expressions and in doing so, activating the common, conceptual metaphor that thinking is a fluid process.

There are other metaphors as well (like the electricity metaphor, behaviours being “hard wired”, getting “wires crossed”, an idea that “lights up”) but I think the hydraulic metaphor is my favourite because it captures the idea that cognition is fluid. We can dip our toes in the stream or hold back floods. And as you can seen from earlier posts, I have something of a soft spot for river metaphors.



The Professor, the PI, and the Manager

Here’s a question that I often ask myself: How much should I be managing my lab?

I was meeting with one of my trainees the other day and this grad student mentioned that they sometimes feel like they don’t know what to do during the work day and that they sometimes feel like they are wasting a lot of their time. As a result, this student will end up going home and maybe working on a coding class, or (more often) doing non grad school things. We talked about what this student is doing and I agreed: they are wasting a lot of time, and not really working very effectively.

Before I go on, some background…

There is no shortage of direction in my lab, or at least I don’t think so. I think I have a lot of things in place. Here’s a sample:

  • I have a detailed lab manual that all my trainees have access to. I’ve sent this document to my lab members a few times, and it covers a whole range of topics about how I’d like my lab group to work.
  • We meet as a lab 2 times a week. One day is to present literature (journal club) and the other day is to discuss the current research in the lab. There are readings to prepare, discussions to lead, and I expect everyone to contribute.
  • I meet with each trainee, one-on-one, at least every other week, and we go though what each student is working on.
  • We have an active lab Slack team, every project has a channel.
  • We have a project management Google sheet with deadlines and tasks that everyone can edit, add things to, see what’s been done and what hasn’t been done.

So there is always stuff to do but I also try not to be a micromanager of my trainees. I generally assume that students will want to be learning and developing their scientific skill set. This student is someone who has been pretty set of looking for work outside of academics, and I’m a big champion of that. I am a champion of helping any of my trainees find a good path. But despite all the project management and meetings this student was feeling lost and never sure what to work on. And so they were feeling like grad school has nothing to offer in the realm of skill development for this career direction. Are my other trainees also feeling the same way?

Too much or too little?

I was kind of surprised to hear one of my students say that they don’t know what to work on, because I have been working harder than ever to make sure my lab is well structured. We’ve even dedicated several lab meetings to the topic.

The student asked what I work on during the day, and it occurred to me that I don’t always discuss my daily routine. So we met for over an hour and I showed this student what I’d been working on for the past week: an R-notebook that will accompany a manuscript I’m writing that will allow for all the analysis of an experiment to be open and transparent. We talked about how much time that’s been taking, how I spent 1-2 days optimizing the R code for a computational model. How this code will then need clear documentation. How the OSF page will also need folders for the data files, stimuli, the experimenter instructions. And how those need to be uploaded. I have been spending dozens of hours on this one small part of one component of one project within one of the several research areas in my lab, and there’s so much more to do.

Why aren’t my trainees doing the same? Why aren’t they seeing this, despite all the project management I’ve been doing?

I want to be clear, I am not trying to be critical in any way of any of my trainees. I’m not singling anyone out. They are good students, and it’s literally my job to guide and advise them. So I’m left with the feeling that they are feeling unguided, with the perception that that there’s not much to do. If I’m supposed to be the guide and they are feeling unguided, this seems like a problem with my guidance.

What can I do to help motivate?

What can I do to help them organize, feel motivated, and productive?

I expect some independence for PhD students, but am I giving them too much? I wonder if my lab would be a better training experience if I were just a bit more of a manager.

  • Should I require students to be in the lab every day?
  • Should I expect daily summaries?
  • Should I require more daily evidence that they are making progress?
  • Am I sabotaging my efforts to cultivate independence by letting them be independent?
  • Would my students be better off if I assumed more of a top down, managerial role?

I don’t know the answers to these questions. But I know that there’s a problem. I don’t want to be a boss, expecting them to punch the clock, but I also don’t want them to float without purpose.

I’d appreciate input from other PIs. How much independence is too much? Do you find that your grad students are struggling to know what to do?

If you have something to say about this, let me know in the comments.