How Anna Spurlock’s Brainy Idea Turned Into an Early Career in Neuroeconomics

With traffic congestion at a record high, some Bay Area officials would like to see more people take public transportation to work. Habits, however, are hard to break — and so the gridlock grinds on, with no solution in sight.

But for Anna Spurlock, an environmental and behavioral economist in the Electricity Markets and Policy Group of the Lab's Energy Technologies Area, the path to changing our minds about driving lies deep within — our brains.

With recent funding from her Early Career Laboratory-Directed Research and Development award announced last November, Spurlock and her team will employ research techniques rooted in neuroeconomics — a new multidisciplinary approach that combines neurology, economics, and psychology — to "gain a better understanding of how a person's brain engages in transportation decision-making, and capture insights that can help design mechanisms to influence more people to switch to public transportation or ridesharing," she said.

In the following Q&A, Spurlock discusses what she hopes to achieve under her Early Career LDRD award, why she wanted to work at Berkeley Lab, and why neuroeconomics may be key to turning people on to sustainable transportation.

Q: What do you hope to achieve under this Early Career LDRD award?
A: I'm interested in interpreting behavior, and methodologically pushing the envelope by contributing to a relatively new field known as neuroeconomics that uses fMRI — or functional magnetic resonance imaging — scans of a subject's brain to study how someone neurologically responds to a stimulus or a task. This information could help us to understand what goes on at a neurological level when someone is faced with making a choice, and figure out what biases they might have in their decision-making processes that might create added barriers to their adopting other modes of sustainable transportation.

Q: Why is it important to study neuroeconomics?
A: It's pretty common for people to behave in a way that isn't anticipated by a policymaker or a product designer. The better we can understand what matters to people and how the framing of information can influence how people behave, the better we can inform technological innovation and policies that are good for the environment and are easily incorporated into people's lives.

Q: Why did you want to work at Berkeley Lab?
A: After graduate school, I knew I wanted to continue doing research, but I wasn't sure if I wanted to pursue a tenure-track position in academia. I was drawn to the Lab because it offers a great middle ground between research and policy-relevant applications for the real world. What I continue to appreciate about Berkeley Lab is that, from my experience, if you are driven and persistent, you can build the job you want here. You can actually pursue something new that you think is interesting if you can find a way to get it funded and develop it further.

Q: What does winning this LDRD award mean to you?
A: This LDRD award allows us to build this experiment and run it, and build out the capacity for us to do this kind of work here at the Lab. Most money going into neuroeconomics research comes from marketing firms that want to study how people make decisions relevant for their own product. There have been extremely few — maybe three to five papers — that have used the tools of neuroeconomics to touch on energy questions or study things like how people respond to Energy Star labels. With this LDRD award, my collaborators, including Nik Sawe of Stanford University, who was recently hired part-time here at the Lab, and Ming Hsu, an associate professor at UC Berkeley's Haas Marketing Group, and I can do research in an applied area such as energy that's not for marketing but for the purpose of understanding behaviors that matter for the environment, and how we think about energy-relevant outcomes in the economy.

Q: Where do you see yourself 10 years from now?
A: I see myself still at Berkeley Lab heading up a group focused on decision science and using data-driven techniques like machine learning, big data, or neuroeconomics to expand the energy research toolbox, and applying it to interesting energy-relevant behaviors and policy analysis.

Q: What advice do you have for other people who are interested in a career in science?
A: Be stubborn and persistent. You may not succeed the first time, whether it's getting into the school you want, or getting the job you want, or getting the grant you want. It's amazing what can be accomplished if you don't take setbacks as failures.