[citation needed]

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:

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.

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

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.

This week’s issue of Science has a news article about National Lab Day, a White House-supported initiative to pair up teachers and scientists in an effort to improve STEM education nation-wide. As the article notes, National Lab Day is a bit of a misnomer, seeing as the goal is to encourage a range of educational activities over the next year or so. That’s a sentiment I can appreciate; why pick just one national lab day when you can have ALL OF THEM.

In any case, if you’re a scientist, you can sign up simply by giving away all of your deepest secrets and best research ideas providing your contact information and describing your academic background. I’m not really sure what happens after that, but in theory, at some point you’re supposed to wind up in a K-12 classroom demonstrating what you do and why it’s cool, which I guess could involve activities like pulling french fries out of burning oil with your bare hands, or applying TMS to 3rd graders’ foreheads, or other things of that nature. Of course, you can’t really bring an fMRI scanner into a classroom (though I suppose you could bring a classroom to an fMRI scanner), so I’m not really sure what I’ll do if anyone actually contacts me and asks me to come visit their classroom. I guess there’s always videos of lesion patients and the Muller-Lyer illusion, right?