Timothy R. Brick, PhD

My overall goal is to improve the way that we do science, so we can help people thrive.

Industry has already made a lot of jumps towards improving the way that research happens. They run A/B tests in live environments, update lists of subgroups as they discover them, and generally monitor a whole lot of crazy data on a whole lot of people. The ethical constraints that they follow are a lot less rigid than what we do in science. But we have the technology and the methods to do this right; we just need to make it easy and set up the rewards so that they promote good work. There are a bunch of things that are needed to make this happen.

Let's do it live.

If we want to intervene in the processes that make up human experience, there's no better time to do it than exactly when a human is experiencing it. So let's monitor and intervene in conversation during the conversation. When better to adapt an educational tool than while someone is using it to learn? And how will we ever learn about the experience of an emotion unless we ask people about it while they're feeling it?

Drug addiction recovery is a great example for this. The science of addiction says that self-reported craving isn't really related to risk of relapse. But the actual people who are in recovery often say otherwise. And a lot of that difference is context. That is, craving levels when you're in a lab surrounded by people in labcoats isn't particularly predictive. But craving when you're out on the town with your still-using friends is a different story. That's when the intervention needs to happen.

Also, I care about integration.

Human sensorimotor systems are amazingly complex. We use them for all kinds of crazy things, from deciding whether or not we can sit on something to extracting meaning from shapes on a page to alerting ourselves to incoming texts. And they're flexible in ways people rarely imagine. If there's useful information in something, our brains and sensorimotor systems extract it. And when we have tools, we do it even faster. You stop thinking about fingers hitting keys on the keyboard, and you make words with your brain. You even detect typos that way sometimes. I think there's a good deal of fun to be had hacking the sensorimotor system. Mostly, I target this in context, but there's a bunch of work that could be done better.

Not too high-level, not too low-level

I like mid-level research. While it's very cool to think about super high-level constructs like how concept X maps to concept Y in people's conceptions, I'm not really interested in studying that, except where it starts to influence behavior. Similarly, while the cellular and subcellular makeups of neurons are pretty cool, I'm not so much into that on its own, either. I'm a fan of the level right below what everybody knows is happening: where things like the perception-action loop and embodied cognition reside. Right around the level where you read about a finding, and then spend the next four days watching yourself and your friends and going, "Holy crap, that's totally true!" (Try it with the example above: People synchronize their poses and movements in conversation. Honest. Watch yourself talking to people, and you'll see it. Better still, next time you talk to someone, look them straight in the eye and start nodding. See if they can resist the urge to nod back.)

We Have the Technology.

There's a disconnect between psychological science and technological development that aught not to be there. Using technology like videoconference, wearables, generative AI, we can add data-heavy objective (often implicit) measures to modify and add to traditional self-report, response-time, or survey measures. Once we're using data-heavy measures, of course, we need more advanced statistical methods to analyze them--classical ANOVA, for example, just can't get at what we want. Instead, we have to turn to multi-level, hierarchical, and dynamical models to describe the data and test our hypotheses. And sometimes, we need to develop those methods before we can use them. Of course, that's just another part of the fun.

The numbers tell the story, but they don't sell the book

I prefer to focus on research that has a clear implementation, preferably one that's useful. Even if it is ten-years-down-the-road useful. So I try to work from two sides towards the middle: there's science, and there's engineering. Science starts with a question, and works towards an answer. For example, we might ask, "What kinds of things trigger drug cravings in recovery?" The result might be a mathematical model related to cue saliency, stress reactivity, social environment, and a bunch of other things. Most of the time these will be person-specific. So our final model may be highly predictive, but it's probably too complicated to really get our heads around easily. We need additional tools, including theory, simulation, and visualization to figure out how to understand it all. We could build a simulator and run some tests to see if the predictions are good. And that's great, too. But the point where it really tells us if it's working is the point where we can nudge into someone's actual life and tamp their cravings down. How better to test a model of craving and relapse than by helping somebody not relapse?

People are awesome.

The behavioral sciences are where it's at in my book. I'm interested in everything about people and the way they think and interact with themselves, each other, and the technology they use. After a time as a teacher, I'm starting to get interested in how they learn new things, as well. And how culture and tradition get mixed up in it all. Of course, I haven't done much studying in that regard yet. But there's still time.

Welcome to the future. Here's your robot.

C'mon. It's the 21st century. While I'm still waiting for my personal jetpack, two of the best technologies the future has brought us is affordable home and hobbyist robots, and personalized monitors and predictors like wearables. They're both tools that are easy to use and fun to hack. Why wouldn't you want to toy with one? Actually, I'm only just getting back into the robotics game, and only starting out in the wearables world. But if I get some free time [Ha, ha! That's hilarious. Free time. Good luck on that one. --ed.] I'll post some details on my robotics tinkerings.

And I like monsters.

Y'know, folklore, stories, tall tales, and legends. They tell us a whole lot about ourselves. And they're fun. Plus, the fact that even fear can be interpreted positively is one of those things that just makes you go... huh.