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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Image from https://goo.gl/HtfqLa

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.

Dealing with Failure

When the hits come, they really come hard.

I’m dealing with some significant personal/professional failures this month.

I put in for two federal operating grants this past year: one from NSERC to fund my basic cognitive science work on learning and memory and one from SSHRC to fund some relatively new research on mindfulness meditation. I worked pretty hard on these last fall.

And today I found out that neither were funded.

This means that for the first time in a long number of years, my lab does not have an active federal research grant. The renewal application from NSERC is particularly hard to swallow, since I’ve held multiple NSERC grants and they have a pretty high funding rate relative to other programs. I feel like the rug was pulled out from under me and worry about how to support the graduate students in my lab. I can still carry on doing good research this coming year, and I have some residual funds, but I won’t lie: this is very disappointing.

The cruelest month, the cruelest profession.

It’s often said that academic/scientific work loads heavily on dealing with failure. It’s true. I’ve had failed grants before. Rejected manuscripts. Experiments that I thought were interesting or good that fell apart with additional scrutiny. For every success, there are multiple failures. And that’s all just part of being a successful academic. Beyond that, many academics may work 6-8 years to get a PhD, do a post doc, and find themselves being rejected from one job after another. Other academics struggle with being on the tenure track and may fail to achieve that milestone.

And April really truly is the cruelest month in academics.  Students may have to deal with: rejection from grad school, med school, graduate scholarships, job applications, internships, residency programs. They worry about their final exams. Faculty worry about rejection from grants, looking for jobs, and a whole host of other things. (and at least here in Canada, we still have snow in the forecast…)

Why am I writing this?

Well, why not? I’m not going to hide these failures in shame. Or try to blame someone else. I have to look these failures in the eye, own them, take responsibility for them, and keep working. Part of that means taking the time to work through my emotions and feelings about this. That’s why I’m writing this.

I’m also writing, I guess, to say that it’s worth keeping in mind that we all deal with some kind of stress or anxiety or rejection. Even people who seem to have it together (like me, I probably seem like I have it together: recently promoted to Full Professor, respectable research output, I’ve won several teaching awards, written a textbook, and have been a kind and decent teacher and mentor to 100s of students)…we all get hits. But really, I’m doing fine. I’m still lucky. I’m still privileged. I know that others will be hurting more than I am. I have no intention to wallow in pity or fight with rage. I’m not going to stop working. Not going to stop writing, doing research or trying to improve as a teacher. Moving forward is the only way I can move.

Moving on

We all fail. The question is: What are you going to do about it?

From a personal standpoint, I’m not going to let this get me down. I’ve been in this boat before. I have several projects that are now beginning to bear fruit. I’ve had a terrific insights about some new collaborative work. I have a supportive department and I’m senior enough to weather quite a lot. (thought I’m not Job, so you don’t have to test me Lord!)

From a professional standpoint, though, I think I know what the problems were and I don’t even need to see the grant reviews or committee comments (though I will be looking at them soon). There’s only one of me and branching off into a new direction three years ago to pursue some new ideas took time away from my core program, and I think both suffered a bit as a result. That happens, and I can learn from that experience.

I’ll have to meet with my research team and students next week and give them the bad news. We’re going to need to probably have some difficult conversations about working through this, and I know this will hit some of them hard too.

It might also mean some scholarly pruning. It might mean turning off a few ideas to focus more on the basic cognitive science that’s most important to me.

Congratulations to everyone who got good news this month. Successful grants, acceptance into med school, hired, or published. Success was earned. And for those of us getting bad news: accept it, deal with it, and progress.

Now enjoy the weekend everyone.

 

River Water

A simple metaphor

I’ve been reading a lot about privilege, gender, and colonization. I will not even try to pretend to be an expert in this area. But I was thinking about how I am often unaware of my own life and its privilege and the role of luck and chance in all of our lives. The following metaphor / parable is what I came up with. It’s a bit of a clumsy analogy, but I thought it worked on a simple level for me.

We are like rivers

A river flows in the direction that it flows because of many things. Although some rivers are fast, or slow, or deep, or wide, they are all made of the same water. And really, a river is nothing more than water flowing along a course that was created by the water that came before it: the water that created the channel, the water that created the canyon, even the water that is downstream, pulling the river along its course.

The river doesn’t know this. It cannot know the struggles of the earlier river-water that moved the rocks. It cannot know the ease with which the earlier river-water flowed down an unobstructed path. It cannot know that the earlier river-water was obstructed and damned or if a melting glacier helped the earlier river-water to speed its course and deepen its channel. It cannot know that all rivers eventually stop flowing and that all river-water becomes part of the same sea.

All the river can know is it that it is flowing now: flowing quickly or flowing slowly; constrained or unconstrained, oblivious to its own history even as its present course and identity are shaped its history.

We are like rivers in this way. We flow along in our lives, making progress, confronting obstacles, and not always knowing the full context of our our life course.

We should try to understand

But we can try to know more that the river knows. Even as we try to live in the present, we can try to understand how the past shaped the channels and canyons of our life-course. We can see how our current circumstances might make it easier or more difficult depending on the obstacles that previous generations faced. We are the beneficiaries to the sometimes arbitrary circumstances that favoured or did not favour those who came before us. We may also carry the burden of the circumstances imposed on those who came before us. Those of us whose lives flow though clear cut channels may not always realize that we’re travelling a path with fewer obstacles, because those obstacles were removed long before us. We receive these benefits, earned or unearned, aware, or unaware.  But people whose paths are or were constrained or obstructed are often all too aware of the impedance. And like a river that was once blocked or dammed, the effects of the obstruction can be seen and felt long after the impedance was removed.

But we’re all the same river-water, flowing to the same sea. But we don’t all take the same course. We would do well to be aware of our privilege and to understand that we may not all have the same course to travel…but we still have to travel to the same place.

Be mindful of your own trajectory. Be mindful of others.

And help when you can.

 

A Curated Reading List

Fact: I do not read enough of the literature any more. I don’t really read anything. I read manuscripts that I am reviewing, but that’s not really sufficient to stay abreast of the field. I assign readings for classes, to grad students, and trainees and we may discuss current trends. This is great for lab, but for me the effect is something like me saying to my lab “read this and tell me what happened”. And I read twitter.

But I always have a list of things I want to read. What better way to work through these papers than to blog about them, right?

So this the first instalment of “Paul’s Curated Reading List”. I’m going to focus on cognitive science approaches to categorization and classification behaviour. That is my primary field, and the one I most want to stay abreast of. In each instalment, I’ll pick a paper that was published in the last few months, a preprint, or a classic. I’ll read it, summarize it, and critique. I’m not looking to go after anyone or promote anyone. I just want to stay up to date. I’ll have a new instalment on a regular basis (once every other week, once a month, etc.). I’m doing this for me.

So without further introduction, here is Reading List Item #1…

Smith, J. D., Jamani, S., Boomer, J., & Church, B. A. (2018). One-back reinforcement dissociates implicit-procedural and explicit-declarative category learning. Memory & Cognition,46(2), 261–273.

Background

This paper was published on line last fall but was just published officially in Feb of 2018. I came across it this morning and I was looking at the “Table of Contents” email from Memory & Cognition. Full disclosure, the first author was my grad advisor from 1995-2000, though we have’t collaborated since then (save for a chapter). He’s now at Georgia State and has done a lot of fascinating work on metacognition in non-human primates.

The article describes a single study on classification/category learning. The authors are working within a multiple systems approach of category learning. According to this framework, a verbally-mediated, explicit system learns categories by trying to abstract use a rule, and a procedurally-mediated, implicit system learns categories by Stimulus Response (S-R) association. Both systems have well-specified neural underpinnings. These two systems work together but sometimes they are in competition. I know this theory well and have published quite of few papers on the topic. So of course, I wanted to read this one.

A common paradigm in this field is to introduce a manipulation that is predicted to impair or enhance one of the systems and leave the other unharmed in order to create a behavioural dissociation. The interference in this paper was a 1-back feedback manipulation. In one condition, participants received feedback right after their decision and in another, they received feedback about their decision on the previous trial. Smith et al. reasoned that the feedback delay would disrupt the S-R learning mechanism of the procedural/implicit system, because it would interfere with the temporal congruity stimulus and response. It should have less of an effect on the explicit system, since learners can use working memory to verbalize the rule they used and the response they made.

Methods

In the experiment, Smith et al. taught people to classify a large set (480) of visual stimuli that varied along two perceptual dimensions into two categories. You get 480 trials, and on each trial you see shape, make a decision, get feedback, and see another shape, and so on. The stimuli themselves are rectangles that vary in terms of size (dimension 1) and pixel density (dimension two). The figure below shows examples of range. There was no fixed set of exemplars, but “each participant received his or her own sample of randomly selected category exemplars appropriate to the assigned task”.

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They used a 2 by 2 design with two between-subject factors. The first factor was category set. Participants learned either a rule based category (RB) in which a single dimension (size or density) creates an easily-verbalized rule, or an information integration category (II) in which both dimensions need to be integrated at a pre decisional stage. This II category can’t be learned very easily by a verbal rule and many studies have suggested it’s being learned by the procedural system. The figure below shows how many hundreds of individual exemplars would be divided into two categories for each of the each of the category sets (RB and II).

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The second factor was feedback. After each decision, you either received feedback right after you made a decisions (0Back) or you received feedback one trial later (1Back). This creates a heavier task demand, so it should make it harder to learn the RB categories at first because the task creates a heavier working memory load. But it should interfere with II learning by the procedural system because the 1-Back disturbs the S-R association.

Results

So what did they find? The learning data are plotted below and suggest that the 1Back feedback made it harder to learn the RB categories at first, and seemed to hurt the II categories at the end. The 3-way ANOVA (Category X Feedback X Block) provided evidence to that effect, but it’s not an overwhelming effect. Smith et al.’s decision to focus a follow up analysis on the final block was not very convincing. Essentially, they compared means and 95% CIs for the final block for each of the four cells and found that performance in the two RB conditions did not differ, but performance in the two II conditions did. Does that mean that the feedback was disrupting the feedback? I’m not sure. Maybe participants in that condition (II-1Back) were just getting weary of a very demanding task. A visual inspection of the data seems to support that alternative conclusion as well.  Exploring the linear trends might have been a stronger approach.

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The second analysis was a bit more convincing. They fit each subject’s data with a rule model and an II model. Each model tried to account for each subject’s final 100 trials. This is pretty easy to do and you are just looking to see which model provides the most likely account of the data. You can then plot the best fitting model. For subjects who learned the RB category, the optimal rule should be the vertical partition and for the II category, the optimal model is the diagonal partition.

As seen the figure below, the feedback did not change the strategy very much for subjects who learned the RB categories. Panel (a) and (b) show that the best-fitting model was usually a rule based one (the vertical partition). The story is different for subjects learning II categories. First, there is way more variation in best fitting model. Second, very few subjects in the 1-back condition (d) show evidence of the using the optimal rule (the diagonal partition).

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Conclusions

Smith et al concluded: “We predicted that 1-Back reinforcement would disable associative, reinforcement-driven learning and the II category-learning processes that depend on it. This disabling seems to have been complete” But that’s a strong conclusion. Too strong. Based on the modelling, the more measured conclusion seems to be that about 7-8 of the 30 subjects in the II-0Back condition learned the optimal rule (the diagonal) compared to about 1 subject in the II-1Back. Maybe a just handful of keener’s ended up in the II-0Back and learned the complex structure? It’s not easy to say. There is some evidence in favour of Smith et al’s conclusion but its not at all clear.

I still enjoyed reading the paper. The task design is clever, and the predictions flow logically from the theory (which is very important).It’s incremental work. It adds to the literature on the multiple systems theory but does not (in my opinion) rule out a single-system approach. But I wish they had done a second study as an internal replication to explore the stability of the result. Or maybe a second study with the same category structure but different stimuli. It’s incremental work. It adds to the literature on the multiple systems theory but does not (in my opinion) rule out a single-system approach.

Tune in a few weeks for the next instalment. Follow my blog if you like and check the tag to see the full list. As the list grows, I may create a better structure for these, too.