Tal Yarkoni

University of Colorado at Boulder

http://talyarkoni.org

What does your research focus on?

Most of my current research focuses on what you might call psychoinformatics: the application of information technology to psychology, with the aim of advancing our ability to study the human mind and brain. I’m interested in developing new ways to acquire, synthesize, and share data in psychology and cognitive neuroscience. Some of the projects I’ve worked on include developing new ways to measure personality more efficiently, adapting computer science metrics of string similarity to visual word recognition, modeling fMRI data on extremely short timescales, and conducting large-scale automated synthesis of published neuroimaging findings. The common theme that binds these disparate projects together is the desire to develop new ways of conceptualizing and addressing psychological problems; I believe very strongly in the transformative power of good methods.

What drew you to this line of research? Why is it exciting to you?

Technology enriches and improves our lives in every domain, and science is no exception. In the biomedical sciences in particular, many revolutionary discoveries would have been impossible without substantial advances in information technology. Entire subfields of research in molecular biology and genetics are now synonymous with bioinformatics, and neuroscience is currently also experiencing something of a neuroinformatics revolution. The same trend is only just beginning to emerge in psychology, but we’re already able to do amazing things that would have been unthinkable 10 or 20 years ago. For instance, we can now collect data from thousands of people all over the world online, sample people’s inner thoughts and feelings in real time via their phones, harness enormous datasets released by governments and corporations to study everything from how people navigate their spatial world to how they interact with their friends, and use high-performance computing platforms to solve previously intractable problems through large-scale simulation. Over the next few years, I think we’re going to see transformative changes in the way we study the human mind and brain, and I find that a tremendously exciting thing to be involved in.

Who were/are your mentors or psychological influences?

I’ve been fortunate to have outstanding teachers and mentors at every stage of my training. I actually started my academic career quite disinterested in science and owe my career trajectory in no small part to two stellar philosophy professors (Rob Stainton and Chris Viger) who convinced me as an undergraduate that engaging with empirical data was a surprisingly good way to discover how the world really works. I can’t possibly do justice to all the valuable lessons my graduate and postdoctoral mentors have taught me, so let me just pick a few out of a hat. Among many other things, Todd Braver taught me how to talk through problems collaboratively and keep recursively questioning the answers to problems until a clear understanding materializes. Randy Larsen taught me that patience really is a virtue, despite my frequent misgivings. Tor Wager has taught me to think more programmatically about my research and to challenge myself to learn new skills. All of these people are living proof that you can be an ambitious, hard-working, and productive scientist and still be extraordinarily kind and generous with your time. I don’t think I embody those qualities myself right now, but at least I know what to shoot for.

To what do you attribute your success in the science?

Mostly to blind luck. So far I’ve managed to stumble from one great research and mentoring situation to another. I’ve been fortunate to have exceptional advisors who’ve provided me with the perfect balance of freedom and guidance and amazing colleagues and friends who’ve been happy to help me out with ideas and resources whenever I’m completely out of my depth — which is most of the time.

To the extent that I can take personal credit for anything, I think I’ve been good about pursuing ideas I’m passionate about and believe in, even when they seem unlikely to pay off at first. I’m also a big proponent of exploratory research; I think pure exploration is tremendously undervalued in psychology. Many of my projects have developed serendipitously, as a result of asking, “What happens if we try doing it this way?”

What’s your future research agenda?

I’d like to develop technology-based research platforms that improve psychologists’ ability to answer existing questions while simultaneously opening up entirely new avenues of research. That includes things like developing ways to collect large amounts of data more efficiently, tracking research participants over time, automatically synthesizing the results of published studies, building online data repositories and collaboration tools, and more. I know that all sounds incredibly vague, and if you have some ideas about how to go about any of it, I’d love to collaborate! And by collaborate, I mean that I’ll brew the coffee and you’ll do the work.

Any advice for even younger psychological scientists? What would you tell someone just now entering graduate school or getting their PhD?

The responsible thing would probably be to say “Don’t go to graduate school.” But if it’s too late for that, I’d recommend finding brilliant mentors and colleagues and serving them coffee exactly the way they like it. Failing that, find projects you’re passionate about, work with people you enjoy being around, develop good technical skills, and don’t be afraid to try out crazy ideas. Leave your office door open, and talk to everyone you can about the research they’re doing, even if it doesn’t seem immediately relevant. Good ideas can come from anywhere and often do.

What publication you are most proud of or feel has been most important to your career?

Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. [Manuscript submitted for publication]

In this paper, we introduce a highly automated platform for synthesizing data from thousands of published functional neuroimaging studies. We used a combination of text mining, meta-analysis, and machine learning to automatically generate maps of brain activity for hundreds of different psychological concepts, and we showed that these results could be used to “decode” cognitive states from brain activity in individual human subjects in a relatively open-ended way. I’m very proud of this work, and I’m quite glad that my co-authors agreed to make me first author in return for getting their coffee just right. Unfortunately, the paper isn’t published yet, so you’ll just have to take my word for it that it’s really neat stuff. And if you’re thinking, “Isn’t it awfully convenient that his best paper is unpublished?”… why, yes. Yes it is.