Okay, not everything. But a lot of what we know. The current issue of Current Opinion in Neurobiology, which features a special focus on cognitive neuroscience, contains are almost 20 short review papers, most of which focus on the neural mechanisms of cognitive control in one guise or another. As the Editors of the special issue (Earl Miller and Liz Phelps) explain in their introduction:
Our goal with this special issue was to highlight integrative approaches to brain function. To this end, we focused on the most integrative of brain functions, cognitive control. Cognitive, or executive, control is the ability to coordinate thought and action by directing them toward goals, often far removed goals.
I’ve only skimmed a couple of articles so far, but it’s a pretty impressive table of contents, and I’m looking forward to reading a lot of the reviews. The nice thing about the Current Opinion series, like the Trends series, is that the reviews are short and focused, so they’re well-suited to people who are very busy and don’t have enough hours in their day (like you), or people who just have a short attention span (like me).
Admittedly, I also have an ulterior motive for mentioning this issue: Todd Braver, Mike Cole and I contributed one of the articles, in which we review the neural bases of individual differences in executive control. I think it’s a really nice paper, the credit for which really goes to Todd and Mike–I mostly just contributed the section on methodological considerations (which is basically a precis of a much longer chapter I wrote with Todd a couple of years ago). Todd and Mike somehow managed to review work on everything from reward and motivation to emotion regulation to working memory capacity to dopamine genes, all in the space of eight pages. It’s a nice review highlighting the importance of modeling not only the central tendency of people’s behavior and brain activation in cognitive neuroscience studies, but also the variation between individuals. Aside from the fact that many people (including me!) find individual differences in cognitive abilities intrinsically interesting, an individual differences approach can provide insights that naturally complement those identified by more common within-subject analyses.
For instance, there’s a giant literature on the critical role the neurotransmitter dopamine plays in maintaining and updating goal representations. Most process models of dopamine function make either explicit or tacit predictions about how individual differences in dopamine function should manifest behaviorally, and recent studies have sought to test some of these predictions using both neuroimaging and molecular genetic techniques. A lot of work has focused on a common polymorphism in the COMT gene, variants of which dramatically alter the efficiency of dopamine degradation in the prefrontal cortex. An (admittedly simplistic) prediction that follows from one standard view of prefrontal dopamine function (that tonic dopamine serves to stabilize active representations) is that people who possess the low-activity met allele (and consequently have higher dopamine levels in PFC) should have a greater capacity to maintain goal representations and sustain attention, which may manifest as improved performance on many working memory tasks. Conversely, people with the val allele, which is associated with lower tonic dopamine levels in PFC, should do worse at tasks requiring sustained attention, but may have greater cognitive flexibility (due to the capacity to switch between goal representations more easily).
This prediction, which is borne out by a number of studies we review, is fundamentally about individual differences, since we typically can’t manipulate people’s COMT genes in the lab (though I know some people who probably really wish we could!). But the point is, even if you’re not intrinsically interested in what makes people different from one another, studying individual variation at a genetic, neural, or behavioral level can often tell you something useful about the models you’re developing. Particularly when it comes to the domain of executive control, where differences between individuals can be quite striking. Almost any mechanistic model of executive control is going to have ‘joints’ that could theoretically vary systematically across individuals, so it makes sense to capitalize on natural variability between people to test some of the predictions that fall out of the model, instead of just treating between-subject variability as the error term in your one-sample t-test.