Tag: training

No, it’s not The Incentives—it’s you

There’s a narrative I find kind of troubling, but that unfortunately seems to be growing more common in science. The core idea is that the mere existence of perverse incentives is a valid and sufficient reason to knowingly behave in an antisocial way, just as long as one first acknowledges the existence of those perverse incentives. The way this dynamic usually unfolds is that someone points out some fairly serious problem with the way many scientists behave—say, our collective propensity to p-hack as if it’s going out of style, or the fact that we insist on submitting our manuscripts to publishers that are actively trying to undermine our interests—and then someone else will say, “I know, right—but what are you going to do, those are the incentives.”

As best I can tell, the words “it’s the incentives” are magic. Once they’re uttered by someone, natural law demands that everyone else involved in the conversation immediately stop whatever else they were doing, solemnly nod, and mumble something to the effect that, yes, the incentives are very bad, very bad indeed, and it’s a real tragedy that so many smart, hard-working people are being crushed under the merciless, gigantic boot of The System. Then there’s usually a brief pause, and after that, everyone goes back to discussing whatever they were talking about a moment earlier.

Perhaps I’m getting senile in my early middle age, but my anecdotal perception is that it used to be that, when somebody pointed out to a researcher that they might be doing something questionable, that researcher would typically either (a) argue that they weren’t doing anything questionable (often incorrectly, because there used to be much less appreciation for some of the statistical issues involved), or (b) look uncomfortable for a little while, allow an awkward silence to bloom, and then change the subject. In the last few years, I’ve noticed that uncomfortable discussions about questionable practices disproportionately seem to end with a chuckle or shrug, followed by a comment to the effect that we are all extremely sophisticated human beings who recognize the complexity of the world we live in, and sure it would be great if we lived in a world where one didn’t have to occasionally engage in shenanigans, but that would be extremely naive, and after all, we are not naive, are we?

There is, of course,  an element of truth to this kind of response. I’m not denying that perverse incentives exist; they obviously do. There’s no question that many aspects of modern scientific culture systematically incentivize antisocial behavior, and I don’t think we can or should pretend otherwise. What I do object to quite strongly is the narrative that scientists are somehow helpless in the face of all these awful incentives—that we can’t possibly be expected to take any course of action that has any potential, however small, to impede our own career development.

“I would publish in open access journals,” your friendly neighborhood scientist will say. “But those have a lower impact factor, and I’m up for tenure in three years.”

Or: “if I corrected for multiple comparisons in this situation, my effect would go away, and then the reviewers would reject the paper.”

Or: “I can’t ask my graduate students to collect an adequately-powered replication sample; they need to publish papers as quickly as they can so that they can get a job.”

There are innumerable examples of this kind, and they’ve become so routine that it appears many scientists have stopped thinking about what the words they’re saying actually mean, and instead simply glaze over and nod sagely whenever the dreaded Incentives are invoked.

A random bystander who happened to eavesdrop on a conversation between a group of scientists kvetching about The Incentives could be forgiven for thinking that maybe, just maybe, a bunch of very industrious people who generally pride themselves on their creativity, persistence, and intelligence could find some way to work around, or through, the problem. And I think they would be right. The fact that we collectively don’t see it as a colossal moral failing that we haven’t figured out a way to get our work done without having to routinely cut corners in the rush for fame and fortune is deeply troubling.

It’s also aggravating on an intellectual level, because the argument that we’re all being egregiously and continuously screwed over by The Incentives is just not that good. I think there are a lot of reasons why researchers should be very hesitant to invoke The Incentives as a justification for why any of us behave the way we do. I’ll give nine of them here, but I imagine there are probably others.

1. You can excuse anything by appealing to The Incentives

No, seriously—anything. Once you start crying that The System is Broken in order to excuse your actions (or inactions), you can absolve yourself of responsibility for all kinds of behaviors that, on paper, should raise red flags. Consider just a few behaviors that few scientists would condone:

  • Fabricating data or results
  • Regulary threatening to fire trainees in order to scare them into working harder
  • Deliberately sabotaging competitors’ papers or grants by reviewing them negatively

I think it’s safe to say most of us consider such practices to be thoroughly immoral, yet there are obviously people who engage in each of them. And when those people are caught or confronted, one of the most common justifications they fall back on is… you guessed it: The Incentives! When Diederik Stapel confessed to fabricating the data used in over 50 publications, he didn’t explain his actions by saying “oh, you know, I’m probably a bit of a psychopath”; instead, he placed much of the blame squarely on The Incentives:

I did not withstand the pressure to score, to publish, the pressure to get better in time. I wanted too much, too fast. In a system where there are few checks and balances, where people work alone, I took the wrong turn. I want to emphasize that the mistakes that I made were not born out of selfish ends.

Stapel wasn’t acting selfishly, you see… he was just subject to intense pressures. Or, you know, Incentives.

Or consider these quotes from a New York Times article describing Stapel’s unraveling:

In his early years of research — when he supposedly collected real experimental data — Stapel wrote papers laying out complicated and messy relationships between multiple variables. He soon realized that journal editors preferred simplicity. “They are actually telling you: ‘Leave out this stuff. Make it simpler,'” Stapel told me. Before long, he was striving to write elegant articles.

…

The experiment — and others like it — didn’t give Stapel the desired results, he said. He had the choice of abandoning the work or redoing the experiment. But he had already spent a lot of time on the research and was convinced his hypothesis was valid. “I said — you know what, I am going to create the data set,” he told me.

Reading through such accounts, it’s hard to avoid the conclusion that Stapel’s self-narrative is strikingly similar to the one that gets tossed out all the time on social media, or in conference bar conversations: here I am, a good scientist trying to do an honest job, and yet all around me is a system that incentivizes deception and corner-cutting. What do you expect me to do?.

Curiously, I’ve never heard any of my peers—including many of the same people who are quick to invoke The Incentives to excuse their own imperfections—seriously endorse The Incentives as an acceptable justification for Stapel’s behavior. In Stapel’s case, the inference we overwhelmingly jump to is that there must be something deeply wrong with Stapel, seeing as the rest of us also face the same perverse incentives on a daily basis, yet we somehow manage to get by without fabricating data. But this conclusion should make us a bit uneasy, I think, because if it’s correct (and I think it is), it implies that we aren’t really such slaves to The Incentives after all. When our morals get in the way, we appear to be perfectly capable of resisting temptation. And I mean, it’s not even like it’s particularly difficult; I doubt many researchers actively have to fight the impulse to manipulate their data, despite the enormous incentives to do so. I submit that the reason many of us feel okay doing things like reporting exploratory results as confirmatory results, or failing to mention that we ran six other studies we didn’t report, is not really that The Incentives are forcing us to do things we don’t like, but that it’s easier to attribute our unsavory behaviors to unstoppable external forces than to take responsibility for them and accept the consequences.

Needless to say, I think this kind of attitude is fundamentally hypocritical. If we’re not comfortable with pariahs like Stapel blaming The Incentives for causing them to fabricate data, we shouldn’t use The Incentives as an excuse for doing things that are on the same spectrum, albeit less severe. If you think that what the words “I did not withstand the pressure to score” really mean when they fall out of Stapel’s mouth is something like “I’m basically a weak person who finds the thought of not being important so intolerable I’m willing to cheat to get ahead”, then you shouldn’t give yourself a free pass just because when you use that excuse, you’re talking about much smaller infractions. Consider the possibility that maybe, just like Stapel, you’re actually appealing to The Incentives as a crutch to avoid having to make your life very slightly more difficult.

2. It would break the world if everyone did it

When people start routinely accepting that The System is Broken and The Incentives Are Fucking Us Over, bad things tend to happen. It’s very hard to have a stable, smoothly functioning society once everyone believes (rightly or wrongly) that gaming the system is the only way to get by. Imagine if every time you went to your doctor—and I’m aware that this analogy won’t work well for people living outside the United States—she sent you to get a dozen expensive and completely unnecessary medical tests, and then, when prompted for an explanation, simply shrugged and said “I know I’m not an angel—but hey, them’s The Incentives.” You would be livid—even though it’s entirely true (at least in the United States; other developed countries seem to have figured this particular problem out) that many doctors have financial incentives to order unnecessary tests.

To be clear, I’m not saying perverse incentives never induce bad behavior in medicine or other fields. Of course they do. My point is that practitioners in other fields at least appear to have enough sense not to loudly trumpet The Incentives as a reasonable justification for their antisocial behavior—or to pat themselves on the back for being the kind of people who are clever enough to see the fiendish Incentives for exactly what they are. My sense is that when doctors, lawyers, journalists, etc. fall prey to The Incentives, they generally consider that to be a source of shame. I won’t go so far as to suggest that we scientists take pride in behaving badly—we obviously don’t—but we do seem to have collectively developed a rather powerful form of learned helplessness that doesn’t seem to be matched by other communities. Which is a fortunate thing, because if every other community also developed the same attitude, we would be in a world of trouble.

3. You are not special

Individual success in science is, to a first approximation, a zero-sum game—at least in the short term. many scientists who appeal to The Incentives seem to genuinely believe that opting out of doing the right thing is a victimless crime. I mean, sure, it might make the system a bit less efficient overall… but that’s just life, right? It’s not like anybody’s actually suffering.

Well yeah, people actually do suffer. There are many scientists who are willing to do the right things—to preregister their analysis plans, to work hard to falsify rather than confirm their hypotheses, to diligently draw attention to potential confounds that complicate their preferred story, and so on. When you assert your right to opt out of these things because apparently your publications, your promotions, and your students are so much more important than everyone else’s, you’re cheating those people.

No, really, you are. If you don’t like to think of yourself as someone who cheats other people, don’t reflexively collapse on a crutch made out of stainless steel Incentives any time someone questions your process. You are not special. Your publications, job, and tenure are not more important than other people’s. The fact that there are other people in your position engaging in the same behaviors doesn’t mean you and your co-authors are all very sophisticated, and that the people who refuse to cut corners are naive simpletons. What it actually demonstrates is that, somewhere along the way, you developed the reflexive ability to rationalize away behavior that you would disapprove of in others and that, viewed dispassionately, is clearly damaging to science.

4. You (probably) have no data

It’s telling that appeals to The Incentives are rarely supported by any actual data. It’s simply taken for granted that engaging in the practice in question would be detrimental to one’s career. The next time you’re tempted to blame The System for making you do bad things, you might want to ask yourself this: Do you actually know that, say, publishing in PLOS ONE rather than [insert closed society journal of your choice] would hurt your career? If so, how do you know that? Do you have any good evidence for it, or have you simply accepted it as stylized fact?

Coming by the kind of data you’d need to answer this question is actually not that easy: it’s not enough to reflexively point to, say, the fact that some journals have higher impact factors than others, To identify the utility-maximizing course of action, you’d need to integrate over both benefits and costs, and the costs are not always so obvious. For example, the opportunity cost of submitting your paper to a “good” journal will be offset to some extent by the likelihood of faster publication (no need to spend two years racking up rejections at high-impact venues), by the positive image you send to at least some of your peers that you support open scientific practices, and so on.

I’m not saying that a careful consideration of the pros and cons of doing the right thing would usually lead people to change their minds. It often won’t. What I’m saying is that people who blame The Incentives for forcing them to submit their papers to certain journals, to tell post-hoc stories about their work, or to use suboptimal analytical methods don’t generally support their decisions with data, or even with well-reasoned argument. The defense is usually completely reflexive—which should raise our suspicion that it’s also just a self-serving excuse.

5. It (probably) won’t matter anyway

This one might hurt a bit, but I think it’s important to consider—particularly for early-career researchers. Let’s suppose you’re right that doing the right thing in some particular case would hurt your career. Maybe it really is true that if you comprehensively report in your paper on all the studies you ran, and not just the ones that “worked”, your colleagues will receive your work less favorably. In such cases it may seem natural to think that there has to be a tight relationship between the current decision and the global outcome—i.e., that if you don’t drop the failed studies, you won’t get a tenure-track position three years down the road. After all, you’re focusing on that causal relationship right now, and it seems so clear in your head!

Unfortunately (or perhaps fortunately?), reality doesn’t operate that way. Outcomes in academia are multiply determined and enormously complex. You can tell yourself that getting more papers out faster will get you a job if it makes you feel better, but that doesn’t make it true. If you’re a graduate student on the job market these days, I have sad news for you: you’re probably not getting a tenure-track job no matter what you do. It doesn’t matter how many p-hacked papers you publish, or how thinly you slice your dissertation into different “studies”; there are not nearly enough jobs to go around for everyone who wants one.

Suppose you’re right, and your sustained pattern of corner-cutting is in fact helping you get ahead. How far ahead do you think it’s helping you get? Is it taking you from a 3% chance of getting a tenure-track position at an R1 university to an 80% chance? Almost certainly not. Maybe it’s increasing that probability from 7% to 11%; that would still be a non-trivial relative increase, but it doesn’t change the fact that, for the average grad student, there is no full-time faculty position waiting at the end of the road. Despite what the environment around you may make you think, the choice most graduate students and postdocs face is not actually between (a) maintaining your integrity and “failing” out of science or (b) cutting a few corners and achieving great fame and fortune as a tenured professor. The Incentives are just not that powerful. The vastly more common choice you face as a trainee is between (a) maintaining your integrity and having a pretty low chance of landing a permanent research position, or (b) cutting a bunch of corners that threaten the validity of your work and having a slightly higher (but still low in absolute terms) chance of landing a permanent research position. And even that’s hardly guaranteed, because you never know when there’s someone on a hiring committee who’s going to be turned off by the obvious p-hacking in your work.

The point is, the world is complicated, and as a general rule, very few things—including the number of publications you produce—are as important as they seem to be when you’re focusing on them in the moment. If you’re an early-career researcher and you regularly find yourself strugging between doing what’s right and doing what isn’t right but (you think) benefits your career, you may want to take a step back and dispassionately ask yourself whether this integrity versus expediency conflict is actually a productive way to frame things. Instead, consider the alternative framing I suggested above: you are most likely going to leave academia eventually, no matter what you do, so why not at least try to see the process through with some intellectual integrity? And I mean, if you’re really so convinced that The System is Broken, why would you want to stay in it anyway? Do you think standards are going to change dramatically in the next few years? Are you laboring under the impression that you, of all people, are going to somehow save science?

This brings us directly to the next point…

6. You’re (probably) not going to “change things from the inside”

Over the years, I’ve talked to quite a few early-career researchers who have told me that while they can’t really stop engaging in questionable research practices right now without hurting their career, they’re definitely going to do better once they’re in a more established position. These are almost invariably nice, well-intentioned people, and I don’t doubt that they genuinely believe what they say. Unfortunately, what they say is slippery, and has a habit of adapting to changing circumstances. As a grad student or postdoc, it’s easy to think that once you get a faculty position, you’ll be able to start doing research the “right” way. But once you get a faculty position, it then turns out you need to get papers and grants in order to get tenure (I mean, who knew?), so you decide to let the dreaded Incentives win for just a few more years. And then, once you secure tenure, well, now the problem is that your graduate students also need jobs, just like you once did, so you can’t exactly stop publishing at the same rate, can you? Plus, what would all your colleagues think if you effectively said, “oh, you should all treat the last 15 years of my work with skepticism—that was just for tenure”?

I’m not saying there aren’t exceptions. I’m sure there are. But I can think of at least a half-dozen people off-hand who’ve regaled me with me some flavor of “once I’m in a better position” story, and none of them, to my knowledge, have carried through on their stated intentions in a meaningful way. And I don’t find this surprising: in most walks of life, course correction generally becomes harder, not easier, the longer you’ve been traveling on the wrong bearing. So if part of your unhealthy respect for The Incentives is rooted in an expectation that those Incentives will surely weaken their grip on you just as soon as you reach the next stage of your career, you may want to rethink your strategy. The Incentives are not going to dissipate as you move up the career ladder; if anything, you’re probably going to have an increasingly difficult time shrugging them off.

7. You’re not thinking long-term

One of the most frustrating aspects of appeals to The Incentives is that they almost invariably seem to focus exclusively on the short-to-medium term. But the long term also matters. And there, I would argue that The Incentives very much favor a radically different—and more honest—approach to scientific research. To see this, we need only consider the ongoing “replication crisis” in many fields of science. One thing that I think has been largely overlooked in discussions about the current incentive structure of science is what impact the replication crisis will have on the legacies of a huge number of presently famous scientists.

I’ll tell you what impact it will have: many of those legacies will be completely zeroed out. And this isn’t just hypothetical scaremongering. It’s happening right now to many former stars of psychology (and, I imagine, other fields I’m less familiar with). There are many researchers we can point to right now who used to be really famous (like, major-chunks-of-the-textbook famous), are currently famous-with-an-asterisk, and will in all likelihood, be completely unknown again within a couple of decades. The unlucky ones are probably even fated to become infamous—their entire scientific legacies eventually reduced to footnotes in cautionary histories illustrating how easily entire areas of scientific research can lose their footing when practitioners allow themselves to be swept away by concerns about The Incentives.

You probably don’t want this kind of thing to happen to you. I’m guessing you would like to retire with at least some level of confidence that your work, while maybe not Earth-shattering in its implications, isn’t going to be tossed on the scrap heap of history one day by a new generation of researchers amazed at how cavalier you and your colleagues once were about silly little things like “inferential statistics” and “accurate reporting”. So if your justification for cutting corners is that you can’t otherwise survive or thrive in the present environment, you should consider the prospect—and I mean, really take some time to think about it—that any success you earn within the next 10 years by playing along with The Incentives could ultimately make your work a professional joke within the 20 years after that.

8. It achieves nothing and probably makes things worse

Hey, are you a scientist? Yes? Great, here’s a quick question for you: do you think there’s any working scientist on Planet Earth who doesn’t already know that The Incentives are fucked up? No? I didn’t think so. Which means you really don’t need to keep bemoaning The Incentives; I promise you that you’re not helping to draw much-needed attention to an important new problem nobody’s recognized before. You’re not expressing any deep insight by pointing out that hiring committees prefer applicants with lots of publications in high-impact journals to applicants with a few publications in journals no one’s ever heard of. If your complaints are achieving anything at all, they’re probably actually making things worse by constantly (and incorrectly) reminding everyone around you about just how powerful The Incentives are.

Here’s a suggestion: maybe try not talking about The Incentives for a while. You could even try, I don’t know, working against The Incentives for a change. Or, if you can’t do that, just don’t say anything at all. Probably nobody will miss anything, and the early-career researchers among us might even be grateful for a respite from their senior colleagues’ constant reminder that The System—the very same system those senior colleagues are responsible for creating!—is so fucked up.

9. It’s your job

This last one seems so obvious it should go without saying, but it does need saying, so I’ll say it: a good reason why you should avoid hanging bad behavior on The Incentives is that you’re a scientist, and trying to get closer to the truth, and not just to tenure, is in your fucking job description. Taxpayers don’t fund you because they care about your career; they fund you to learn shit, cure shit, and build shit. If you can’t do your job without having to regularly excuse sloppiness on the grounds that you have no incentive to be less sloppy, at least have the decency not to say that out loud in a crowded room or Twitter feed full of people who indirectly pay your salary. Complaining that you would surely do the right thing if only these terrible Incentives didn’t exist doesn’t make you the noble martyr you think it does; to almost anybody outside your field who has a modicum of integrity, it just makes you sound like you’re looking for an easy out. It’s not sophisticated or worldly or politically astute, it’s just dishonest and lazy. If you find yourself unable to do your job without regularly engaging in practices that clearly devalue the very science you claim to care about, and this doesn’t bother you deeply, then maybe the problem is not actually The Incentives—or at least, not The Incentives alone. Maybe the problem is You.

“Open Source, Open Science” Meeting Report – March 2015

[The report below was collectively authored by participants at the Open Source, Open Science meeting, and has been cross-posted in other places.]

On March 19th and 20th, the Center for Open Science hosted a small meeting in Charlottesville, VA, convened by COS and co-organized by Kaitlin Thaney (Mozilla Science Lab) and Titus Brown (UC Davis). People working across the open science ecosystem attended, including publishers, infrastructure non-profits, public policy experts, community builders, and academics.
Open Science has emerged into the mainstream, primarily due to concerted efforts from various individuals, institutions, and initiatives. This small, focused gathering brought together several of those community leaders. The purpose of the meeting was to define common goals, discuss common challenges, and coordinate on common efforts.

We had good discussions about several issues at the intersection of technology and social hacking including badging, improving standards for scientific APIs, and developing shared infrastructure. We also talked about coordination challenges due to the rapid growth of the open science community. At least three collaborative projects emerged from the meeting as concrete outcomes to combat the coordination challenges.

A repeated theme was how to make the value proposition of open science more explicit. Why should scientists become more open, and why should institutions and funders support open science? We agreed that incentives in science are misaligned with practices, and we identified particular pain points and opportunities to nudge incentives. We focused on providing information about the benefits of open science to researchers, funders, and administrators, and emphasized reasons aligned with each stakeholders’ interests. We also discussed industry interest in “open”, both in making good use of open data, and also in participating in the open ecosystem. One of the collaborative projects emerging from the meeting is a paper or papers to answer the question “Why go open?“ for researchers.

Many groups are providing training for tools, statistics, or workflows that could improve openness and reproducibility. We discussed methods of coordinating training activities, such as a training “decision tree” defining potential entry points and next steps for researchers. For example, Center for Open Science offers statistics consulting, rOpenSci offers training on tools, and Software Carpentry, Data Carpentry, and Mozilla Science Lab offer training on workflows. A federation of training services could be mutually reinforcing and bolster collective effectiveness, and facilitate sustainable funding models.

The challenge of supporting training efforts was linked to the larger challenge of funding the so-called “glue” – the technical infrastructure that is only noticed when it fails to function. One such collaboration is the SHARE project, a partnership between the Association of Research Libraries, its academic association partners, and the Center for Open Science. There is little glory in training and infrastructure, but both are essential elements for providing knowledge to enable change, and tools to enact change.

Another repeated theme was the “open science bubble”. Many participants felt that they were failing to reach people outside of the open science community. Training in data science and software development was recognized as one way to introduce people to open science. For example, data integration and techniques for reproducible computational analysis naturally connect to discussions of data availability and open source. Re-branding was also discussed as a solution – rather than “post preprints!”, say “get more citations!” Another important realization was that researchers who engage with open practices need not, and indeed may not want to, self-identify as “open scientists” per se. The identity and behavior need not be the same.

A number of concrete actions and collaborative activities emerged at the end, including a more coordinated effort around badging, collaboration on API connections between services and producing an article on best practices for scientific APIs, and the writing of an opinion paper outlining the value proposition of open science for researchers. While several proposals were advanced for “next meetings” such as hackathons, no decision has yet been reached. But, a more important decision was clear – the open science community is emerging, strong, and ready to work in concert to help the daily scientific practice live up to core scientific values.

Authors
[Authors are listed in reverse alphabetical order; order does not denote relative contribution.]

  1. Tal Yarkoni, University of Texas at Austin
  2. Kara Woo, NCEAS
  3. Andrew Updegrove, Gesmer Updegrove and ConsortiumInfo.org
  4. Kaitlin Thaney, Mozilla Science Lab
  5. Jeffrey Spies, Center for Open Science
  6. Courtney Soderberg, Center for Open Science
  7. Elliott Shore, Association of Research Libraries
  8. Andrew Sallans, Center for Open Science
  9. Karthik Ram, rOpenSci and Berkeley Institute for Data Science
  10. Min Ragan-Kelley, IPython and UC Berkeley
  11. Brian Nosek, Center for Open Science and University of Virginia
  12. Erin C, McKiernan, Wilfrid Laurier University
  13. Jennifer Lin, PLOS
  14. Amye Kenall, BioMed Central
  15. Mark Hahnel, figshare
  16. C. Titus Brown, UC Davis
  17. Sara D. Bowman, Center for Open Science

not really a pyramid scheme; maybe a giant cesspool of little white lies?

There’s a long tradition in the academic blogosphere (and the offlinesphere too, I presume) of complaining that academia is a pyramid scheme. In a strict sense, I guess you could liken academia to a pyramid scheme, inasmuch as there are fewer open positions at each ascending level, and supply generally exceeds demand. But as The Prodigal Academic points out in a post today, this phenomenon is hardly exclusive to academia:

I guess I don’t really see much difference between academic job hunting, and job hunting in general. Starting out with undergrad admissions, there are many more qualified people for desirable positions than available slots. Who gets those slots is a matter of hard work (to get qualified) and luck (to be one of the qualified people who is “chosen”). So how is the TT any different from grad school admissions (in ANY prestige program), law firm partnership, company CEO, professional artist/athlete/performer, attending physician, investment banking, etc? The pool of qualified applicants is many times larger than the number of slots, and there are desirable perks to success (money/prestige/fame/security/intellectual freedom) making the supply of those willing to try for the goal pretty much infinite.

Maybe I have rose colored glasses on because I have always been lucky enough to find a position in research, but there are no guarantees in life. When I was interviewing in industry, I saw many really interesting jobs available to science PhD holders that were not in research. If I hadn’t gone to National Lab, I would have been happy to take on one of those instead. Sure, my life would be different, but it wouldn’t make my PhD a waste of time or a failed opportunity.

For the most part, I agree with this sentiment. I love doing research, and can’t imagine ever voluntarily leaving academia. But If I do end up having to leave–meaning, if I can’t find a faculty position when I go on the job market in the next year or two–I don’t think it’ll be the end of the world. I see job ads in industry all the time that looks really interesting, and on some level, I think I’d find almost any job that involves creative analysis of very large datasets (which there are plenty of these days!) pretty gratifying. And no matter what happens, I don’t think I’d ever view the time I’ve spent on my PhD and postdoc training as a waste of time, for the simple reason that I’ve really enjoyed most of it (there are, of course, the nasty bits, like writing the Nth chapter of a dissertation–but those are transient, fortunately). So in that sense, I think all the talk about academia being a pyramid scheme is kind of silly.

That said, there is one sticking point to the standard pyramid scheme argument I do agree with, which is that, when you’re starting out as a graduate student, no one really goes out of their way to tell you what the odds of getting a tenure-track faculty position actually are (and they’re not good). The problem being that most of the professors that prospective graduate students have interacted with, either as undergraduates, or in the context of applying to grad school, are precisely those lucky souls who’ve managed to secure faculty positions. So the difficulty of obtaining the same type of position isn’t always very salient to them.

I’m not saying faculty members lie outright to prospective graduate students, of course; I don’t doubt that if you asked most faculty point blank “what proportion of students in your department have managed to find tenure-track positions,” they’d give you an honest answer. But when you’re 22 or 23 years old (and yes, I recognize some graduate students are much older, but this is the mode) and you’re thinking of a career in research, it doesn’t always occur to you to ask that question. And naturally, departments that are trying to recruit your services are unlikely to begin their pitch by saying, “in the past 10 years, only about 12% of our graduates have gone on to tenure-track faculty positions”. So in that sense, I don’t think new graduate students are always aware of just how difficult it is to obtain an independent research position, statistically speaking. That’s not a problem for the (many) graduate students who don’t really have any intention of going into academia anyway, but I do think a large part of the disillusionment graduate students often experience is about the realization that you can bust your ass for five or six years working sixty hours a week, and still have no guarantee of finding a research job when you’re done. And that could be avoided to some extent by making a concerted effort to inform students up front of the odds they face if they’re planning on going down that path. So long as that information is made readily available, I don’t really see a problem.

Having said that, I’m now going to blatantly contradict myself (so what if I do? I am large! I contain multitudes!). You could, I think, reasonably argue that this type of deception isn’t really a problem, and that it’s actually necessary. For one thing, the white lies cut both ways. It isn’t just faculty who conveniently forget to mention that relatively few students will successfully obtain tenure-track positions; many graduate students nod and smile when asked if they’re planning a career in research, despite having no intention of continuing down that path past the PhD. I’ve occasionally heard faculty members complain that they need to do a better job filtering out those applicants who really truly are interested in a career in research, because they’re losing a lot of students to industry at the tail end. But I think this kind of magical mind-reading filter is a pipe dream, for precisely the reasons outlined above: if faculty aren’t willing to begin their recruitment speeches by saying “most of you probably won’t get research positions even if you want them,” they shouldn’t really complain when most students don’t come right out and say “actually, I just want a PhD because I think it’ll be something interesting to do for a few years and then I’ll be able to find a decent job with better hours later”.

The reality is that the whole enterprise may actually require subtle misdirection about people’s intentions. If every student applying to grad school knew exactly what the odds of getting a research position were, I imagine many fewer people who were serious about research would bother applying; you’d then get predominantly people who don’t really want to do research anyway. And if you could magically weed out the students who don’t want to do research, then (a) there probably wouldn’t be enough highly qualified students left to keep research programs afloat, and/or (b) there would be even more candidates applying for research positions, making things even harder for those students who do want careers in research. There’s probably no magical allocation of resources that optimizes everyone’s needs simultaneously; it could be that we’re more or less at a stable equilibrium point built on little white lies.

tl;dr : I don’t think academia is really a pyramid scheme; more like a giant cesspool of little white lies and subtle misinformation that indirectly serves most people’s interests. So, basically, it’s kind of like most other domains of life that involve interactions between many groups of people.

cognitive training doesn’t work (much, if at all)

There’s a beautiful paper in Nature this week by Adrian Owen and colleagues that provides what’s probably as close to definitive evidence as you can get in any single study that “brain training” programs don’t work. Or at least, to the extent that they do work, the effects are so weak they’re probably not worth caring about.

Owen et al used a very clever approach to demonstrate their point. Rather than spending their time running small-sample studies that require people to come into the lab over multiple sessions (an expensive and very time-intensive effort that’s ultimately still usually underpowered), they teamed up with the BBC program ‘Bang Goes The Theory‘. Participants were recruited via the tv show, and were directed to an experimental website where they created accounts, engaged in “pre-training” cognitive testing, and then could repeatedly log on over the course of six weeks to perform a series of cognitive tasks supposedly capable of training executive abilities. After the training period, participants again performed the same battery of cognitive tests, enabling the researchers to compare performance pre- and post-training.

Of course, you expect robust practice effects with this kind of thing (i.e., participants would almost certainly do better on the post-training battery than on the pre-training battery solely because they’d been exposed to the tasks and had some practice). So Owen et al randomly assigned participants logging on to the website to two different training programs (involving different types of training tasks) or to a control condition in which participants answered obscure trivia questions rather than doing any sort of intensive cognitive training per se. The beauty of doing this all online was that the authors were able to obtain gargantuan sample sizes (several thousand in each condition), ensuring that statistical power wasn’t going to be an issue. Indeed, Owen et al focus almost explicitly on effect sizes rather than p values, because, as they point out, once you have several thousand participants in each group, almost everything is going to be statistically significant, so it’s really the effect sizes that matter.

The critical comparison was whether the experimental groups showed greater improvements in performance post-training than the control group did. And the answer, generally speaking, was no. Across four different tasks, the differences in training-related gains in the experimental group relative to the control group were always either very small (no larger than about a fifth of a standard deviation), or even nonexistent (to the extent that for some comparisons, the control group improved more than the experimental groups!). So the upshot is that if there is any benefit of cognitive training (and it’s not at all clear that there is, based on the data), it’s so small that it’s probably not worth caring about. Here’s the key figure:

owen_et_al

You could argue that the fact the y-axis spans the full range of possible values (rather than fitting the range of observed variation) is a bit misleading, since it’s only going to make any effects seem even smaller. But even so, it’s pretty clear these are not exactly large effects (and note that the key comparison is not the difference between light and dark bars, but the relative change from light to dark across the different groups).

Now, people who are invested (either intellectually or financially) in the efficacy of cognitive training programs might disagree, arguing that an effect of one-fifth of a standard deviation isn’t actually a tiny effect, and that there are arguably many situations in which that would be a meaningful boost in performance. But that’s the best possible estimate, and probably overstates the actual benefit. And there’s also the opportunity cost to consider: the average participant completed 20 – 30 training sessions, which, even at just 20 minutes a session (an estimate based on the description of the length of each of the training tasks), would take about 8 – 10 hours to complete (and some participants no doubt spent many more hours in training).  That’s a lot of time that could have been invested in other much more pleasant things, some of which might also conceivably improve cognitive ability (e.g., doing Sudoku puzzles, which many people actually seem to enjoy). Owen et al put it nicely:

To illustrate the size of the transfer effects observed in this study, consider the following representative example from the data. The increase in the number of digits that could be remembered following training on tests designed, at least in part, to improve memory (for example, in experimental group 2) was three-hundredth of a digit. Assuming a linear relationship between time spent training and improvement, it would take almost four years of training to remember one extra digit. Moreover, the control group improved by two-tenths of a digit, with no formal memory training at all.

If someone asked you if you wanted to spend six weeks doing a “brain training” program that would provide those kinds of returns, you’d probably politely (or impolitely) refuse. Especially since it’s not like most of us spend much of our time doing digit span tasks anyway; odds are that the kinds of real-world problems we’d like to perform a little better at (say, something trivial like figuring out what to buy or not to buy at the grocery store) are even further removed from the tasks Owen et al (and other groups) have used to test for transfer, so any observable benefits in the real world would presumably be even smaller.

Of course, no study is perfect, and there are three potential concerns I can see. The first is that it’s possible that there are subgroups within the tested population who do benefit much more from the cognitive training. That is, the miniscule overall effect could be masking heterogeneity within the sample, such that some people (say, maybe men above 60 with poor diets who don’t like intellectual activities) benefit much more. The trouble with this line of reasoning, though, is that the overall effects in the entire sample are so small that you’re pretty much forced to conclude that either (a) any group that benefits substantially from the training is a very small proportion of the total sample, or (b) that there are actually some people who suffer as a result of cognitive training, effectively balancing out the gains seen by other people. Neither of these possibilities seem particularly attractive.

The second concern is that it’s conceivable that the control group isn’t perfectly matched to the experimental group, because, by the authors’ own admission, the retention rate was much lower in the control group. Participants were randomly assigned to the three groups, but only about two-thirds as many control participants completed the study. The higher drop-out rate was apparently due to the fact that the obscure trivia questions used as a control task were pretty boring. The reason that’s a potential problem is that attrition wasn’t random, so there may be a systematic difference between participants in the experimental conditions and those in the control conditions. In particular, it’s possible that the remaining control participants had a higher tolerance for boredom and/or were somewhat smarter or more intellectual on average (answering obscure trivia questions clearly isn’t everyone’s cup of tea). If that were true, the lack of any difference between experimental and control conditions might be due to participant differences rather than an absence of a true training effect. Unfortunately, it’s hard to determine whether this might be true, because (as far as I can tell) Owen et al don’t provide the raw mean performance scores on the pre- and post-training testing for each group, but only report the changes in performance. What you’d want to know is that the control participants didn’t do substantially better or worse on the pre-training testing than the experimental participants (due to selective attrition of low-performing subjects), which might make changes in performance difficult to interpret. But at face value, it doesn’t seem very plausible that this would be a serious issue.

Lastly, Owen et al do report a small positive correlation between number of training sessions performed (which was under participants’ control) and gains in performance on the post-training test. Now, this effect was, as the authors note, very small (a maximal Spearman’s rho of .06), so that it’s also not really likely to have practical implications. Still, it does suggest that performance increases as a function of practice. So if we’re being pedantic, we should say that intensive cognitive training may improve cognitive performance in a generalized way, but that the effect is really minuscule and probably not worth the time and effort required to do the training in the first place. Which isn’t exactly the type of careful and measured claim that the people who sell brain training programs are generally interested in making.

At any rate, setting aside the debate over whether cognitive training works or not, one thing that’s perplexed me for a long time about the training literature is why people focus to such an extent on cognitive training rather than other training regimens that produce demonstrably larger transfer effects. I’m thinking in particular of aerobic exercise, which produces much more robust and replicable effects on cognitive performance. There’s a nice meta-analysis by Colcombe and colleagues that found effect sizes on the order of half a standard deviation and up for physical exercise in older adults–and effects were particularly large for the most heavily g-loaded tasks. Now, even if you allow for publication bias and other manifestations of the fudge factor, it’s almost certain that the true effect of physical exercise on cognitive performance is substantially larger than the (very small) effects of cognitive training as reported by Owen et al and others.

The bottom line is that, based on everything we know at the moment, the evidence seems to pretty strongly suggest that if your goal is to improve cognitive function, you’re more likely to see meaningful results by jogging or swimming regularly than by doing crossword puzzles or N-back tasks–particularly if you’re older. And of course, a pleasant side effect is that exercise also improves your health and (for at least some people) mood, which I don’t think N-back tasks do. Actually, many of the participants I’ve tested will tell you that doing the N-back is a distinctly dysphoric experience.

On a completely unrelated note, it’s kind of neat to see a journal like Nature publish what is essentially a null result. It goes to show that people do care about replication failures in some cases–namely, in those cases when the replication failure contradicts a relatively large existing literature, and is sufficiently highly powered to actually say something interesting about the likely effect sizes in question.

ResearchBlogging.org
Owen AM, Hampshire A, Grahn JA, Stenton R, Dajani S, Burns AS, Howard RJ, & Ballard CG (2010). Putting brain training to the test. Nature PMID: 20407435

in praise of (lab) rotation

I did my PhD in psychology, but in a department that had close ties and collaborations with neuroscience. One of the interesting things about psychology and neuroscience programs is that they seem to have quite different graduate training models, even in cases where the area of research substantively overlaps (e.g., in cognitive neuroscience). In psychology, there seem two be two general models (at least, at American and Canadian universities; I’m not really familiar with other systems). One is that graduate students are accepted into a specific lab and have ties to a specific advisor (or advisors); the other, more common at large state schools, is that graduate students are accepted into the program (or an area within the program) as a whole, and are then given the (relative) freedom to find an advisor they want to work with. There are pros and cons to either model: the former ensures that every student has a place in someone’s lab from the very beginning of training, so that no one falls through the cracks; but the downside is that beginning students often aren’t sure exactly what they want to work on, and there are occasional (and sometimes acrimonious) mentor-mentee divorces. The latter gives students more freedom to explore their research interests, but can make it more difficult for students to secure funding, and has more of a sink-or-swim flavor (i.e., there’s less institutional support for students).

Both of these models differ quite a bit from what I take to be the most common neuroscience model, which is that students spend all or part of their first year doing a series of rotations through various labs–usually for about 2 months at a time. The idea is to expose students to a variety of different lines of research so that they get a better sense of what people in different areas are doing, and can make a more informed judgment about what research they’d like to pursue. And there are obviously other benefits too: faculty get to evaluate students on a trial basis before making a long-term commitment, and conversely, students get to see the internal workings of the lab and have more contact with the lab head before signing on.

I’ve always thought the rotation model makes a lot of sense, and wonder why more psychology programs don’t try to implement one. I can’t complain about my own training, in that I had a really great experience on both personal and professional levels in the labs I worked in; but I recognize that this was almost entirely due to dumb luck. I didn’t really do my homework very well before entering graduate school, and I could easily have landed in a department or lab I didn’t mesh well with, and spent the next few years miserable and unproductive. I’ll freely admit that I was unusually clueless going into grad school (that’s a post for another time), but I think no matter how much research you do, there’s just no way to know for sure how well you’ll do in a particular lab until you’ve spent some time in it. And most first-year graduate students have kind of fickle interests anyway; it’s hard to know when you’re 22 or 23 exactly what problem you want to spend the rest of your life (or at least the next 4 – 7 years) working on. Having people do rotations in multiple labs seems like an ideal way to maximize the odds of students (and faculty) ending up in happy, productive working relationships.

A question, then, for people who’ve had experience on the administrative side of psychology (or neuroscience) departments: what keeps us from applying a rotation model in psychology too? Are there major disadvantages I’m missing? Is the problem one of financial support? Do we think that psychology students come into graduate programs with more focused interests? Or is it just a matter of convention? Inquiring minds (or at least one of them) want to know…

what’s the point of intro psych?

Sanjay Srivastava comments on an article in Inside Higher Ed about the limitations of traditional introductory science courses, which (according to the IHE article) focus too much on rote memorization of facts and too little on the big questions central to scientific understanding. The IHE article is somewhat predictable in its suggestion that students should be engaged with key scientific concepts at an earlier stage:

One approach to breaking out of this pattern, [Shirley Tilghman] said, is to create seminars in which first-year students dive right into science — without spending years memorizing facts. She described a seminar — “The Role of Asymmetry in Development” — that she led for Princeton freshmen in her pre-presidential days.

Beyond the idea of seminars, Tilghman also outlined a more transformative approach to teaching introductory science material. David Botstein, a professor at the university, has developed the Integrated Science Curriculum, a two-year course that exposes students to the ideas they need to take advanced courses in several science disciplines. Botstein created the course with other faculty members and they found that they value many of the same scientific ideas, so an integrated approach could work.

Sanjay points out an interesting issue in translating this type of approach to psychology:

Would this work in psychology? I honestly don’t know. One of the big challenges in learning psychology — which generally isn’t an issue for biology or physics or chemistry — is the curse of prior knowledge. Students come to the class with an entire lifetime’s worth of naive theories about human behavior. Intro students wouldn’t invent hypotheses out of nowhere — they’d almost certainly recapitulate cultural wisdom, introspective projections, stereotypes, etc. Maybe that would be a problem. Or maybe it would be a tremendous benefit — what better way to start off learning psychology than to have some of your preconceptions shattered by data that you’ve collected yourself?

Prior knowledge certainly does seem to play a huge role in the study of psychology; there are some worldviews that are flatly incompatible with certain areas of psychological inquiry. So when some students encounter certain ideas in psychology classes–even introductory ones–they’re forced to either change their views about the way the world works, or (perhaps more commonly?) to discount those areas of psychology and/or the discipline as a whole.

One example of this is the aversion many people have to a reductionist, materialist worldview. If you really can’t abide by the idea that all of human experience ultimately derives from the machinations of dumb cells, with no ghost to be found anywhere in the machine, you’re probably not going to want to study the neural bases of romantic love. Similarly, if you can’t swallow the notion that our personalities appear to be shaped largely by our genes and random environmental influences–and show virtually no discernible influence of parental environment–you’re unlikely to want to be a behavioral geneticist when you grow up. More so than most other fields, psychology is full of ideas that turn our intuitions on our head. For many Intro Psych students who go on to study the mind professionally, that’s one of the things that makes the field so fascinating. But other students are probably turned off for the very same reason.

Taking a step back though, I think before you can evaluate how introductory classes ought to be taught, it’s important to ask what goal introductory courses are ultimately supposed to serve. Implicit in the views discussed in the IHE article is the idea that introductory science classes should basically serve as a jumping-off point for young scientists. The idea is that if you’re immersed in deep scientific ideas in your first year of university rather than your third or fourth, you’ll be that much better prepared for a career in science by the time you graduate. That’s certainly a valid view, but it’s far from the only one. Another perfectly legitimate view is that the primary purpose of an introductory science class isn’t really to serve the eventual practitioners of that science, who, after all, form a very small fraction of students in the class. Rather, it’s to provide a very large number of students with varying degrees of interest in science with a very cursory survey of the field. After all, the vast majority of students who sit through Intro Psych classes would never go onto careers in psychology no matter how the course was taught. You could mount a reasonable argument that exposing most students to “the ideas they need to take advanced courses in several science disciplines” would be a kind of academic malpractice,  because most students who take intro science classes (or at least, intro psychology) probably have no  real interest in taking advanced courses in the topic, and simply want to fill a distribution requirement or get a cursory overview of what the field is about.

The question of who intro classes should be designed for isn’t the only one that needs to be answered. Even if you feel quite certain that introductory science classes should always be taught with an eye to producing scientists, and you don’t care at all for the more populist idea of catering to the non-major masses, you still have to make other hard choices. For example, you need to decide whether you value breadth over depth, or information retention over enthusiasm for the course material. Say you’re determined to teach Intro Psych in such a way as to maximize the production of good psychologists. Do you pick just a few core topics that you think students will find most interesting, or most conducive to understanding key research concepts, and abandon those topics that turn people off? Such an approach might well encourage more students to take further psychology classes; but it does so at the risk of providing an unrepresentative view of the field, and failing to expose some students to ideas they might have benefited more from. Many Intro Psych students seem to really resent the lone week or two of the course when the lecturer covers neurons, action potentials and very basic neuroanatomy. For reasons that are quite inscrutable to me, many people just don’t like brainzzz. But I don’t think that common sentiment is sufficient grounds for cutting biology out of intro psychology entirely; you simply wouldn’t be getting an accurate picture of our current understanding of the mind without knowing at least something about the way the brain operates.

Of course, the trouble is that the way that people like me feel about the brain-related parts of intro psych is exactly the way other people feel about the social parts of intro psych, or the developmental parts, or the clown parts, and so on. Cut social psych out of intro psych so that you can focus on deep methodological issues in studying the brain, and you may well create students more likely to go on to a career in cognitive neuroscience. But you’re probably reducing the number of students who’ll end up going into social psychology. More generally, you’re turning Intro Psychology into Intro to Cognitive Neuroscience, which sort of defeats the point of it being an introductory course in the first place; after all, they’re called survey courses for a reason!

In an ideal world, we wouldn’t have to make these choices; we’d just make sure that all of our intro courses were always engaging and educational and promoted a deeper understanding of how to do science. But in the real world, it’s rarely possible to pull that off, and we’re typically forced to make trade-offs. You could probably promote student interest in psychology pretty easily by showing videos of agnosic patients all day long, but you’d be sacrificing breadth and depth of understanding. Conversely, you could maximize the amount of knowledge students retain from a class by hitting them over the head with information and testing them in every class, but then you shouldn’t be surprised if some students find the course unpleasant and lose interest in the subject matter. The balance between the depth, breadth, and entertainment value of introductory science classes is a delicate one, but it’s one that’s essential to consider before we can fairly evaluate different proposals as to how such classes ought to be structured.