Daeyeol Lee, Department of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine
Research is revealing how neurons code the value of different options when people make decisions. These MRIs show brain areas whose activities increased according to how much human subjects valued the option they chose between two different alternatives researchers presented to them.
In these two different view of the monkey brain, three areas thought to play a key role in making decisions are highlighted (dorsal anterior cingulate cortex, lateral intraprietal cortex and dorsolateral prefrontal cortex). Researchers are trying to decipher how such far-flung regions of the brain work together in decision-making.
Researchers are beginning to decipher what exactly happens in our brains when we are making decisions. Three experts in the field describe the genesis of this cutting-edge field and potential practical applications of this research.
In an attempt to put matter over mind, researchers are beginning to decipher what exactly is happening in our brains when we are making decisions.
Our thoughts, though abstract and vaporous in form, are determined by the actions of specific neuronal circuits in our brains. The interdisciplinary field known as “decision neuroscience” is uncovering those circuits, thereby mapping thinking on a cellular level. Although still a young field, research in this area has exploded in the last decade, with findings suggesting it is possible to parse out the complexity of thinking into its individual components and decipher how they are integrated when we ponder. Eventually, such findings will lead to a better understanding of a wide range of mental disorders, from depression to schizophrenia, as well as explain how exactly we make the multitude of decisions that ultimately shape our destiny.
Recently, three experts in decision neuroscience discussed their work, describing the genesis of this cutting-edge field and why it incorporates several disciplines. They also identified the driving questions in the field and reflected on the potential practical applications of this research. The investigators who participated are:
- DAEYEOL LEE, PhD, Department of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine
- C. DANIEL SALZMAN, MD, PhD., Department of Psychiatry and Neuroscience and Kavli Institute for Brain Science, Columbia University School of Medicine
- XIAO-JING WANG, PhD., Department of Neurobiology, Physics and Psychology; Director, Swartz Program in Theoretical Neurobiology; Kavli Institute of Neuroscience, Yale University School of Medicine
The following is an edited transcript of the teleconference.
THE KAVLI FOUNDATION (TKF): Decision neuroscience is a young field. How did it take root and what is new?
C. DANIEL SALZMAN: In many ways, this area picked up steam a little over 20 years ago with research on perceptual decision-making. We trained monkeys to report whether dots were going up or down on a visual display they were seeing. During these experiments, we were aware the monkeys were doing this for one reason: to get a reward for a correct discrimination. Like these monkeys, we often assign values to different options related to what we are seeing. We are using our perceptual system, while at the same time kicking in parts of the brain that are more specialized for placing a value on what we are seeing. And that leads into the whole economic decision making that Daeyeol works on.
DAEYEOL LEE: For me, the essence of the thinking during decision making is mental simulation—you are trying to predict before you take an action what outcome may occur based on your previous experiences, or by observing and remembering the outcomes of other people’s behaviors. Having studied economics, I knew the mental simulations involved in decision-making could be studied using economic models and a rigorous quantitative approach. Economic theories show that you can assign numerical values to both real and mentally simulated outcomes, whether they are juice or money, so you have something concrete to work with that you can measure and connect to neuronal activity.
WANG: What is new is that, using experimental decision tasks, people like Daniel and Daeyeol and others are now recording neuronal activity from brain areas that are downstream from sensory areas, and these downstream brain areas are probably where decisions or choices are made. That is new research—it started about 10 years ago.
LEE: There definitely was a paradigm shift when people realized they could ask more complicated questions related to thinking and decision-making. People started asking questions like how is uncertainty about reward or risk represented in the brain and how does it influence decision-making, and how does the brain handle the tradeoff between the overall magnitude of the reward and how immediately you get the reward. Daniel is looking at how desirable and aversive information is integrated into decision-making. People weren’t asking these kinds of questions 10 or 20 years ago because we didn’t yet have the basic understanding of how perception and motor control worked—how simple movements and sensory information were coded in the brain. This knowledge was a prerequisite to understanding how the brain makes a decision because now, when we test animals in our experiments, we can distinguish changes in neuronal activity due to perception and motor control from those related to mental simulations and decisions.
SALZMAN: Researchers can now study how neurons represent rewards, and how information on rewards may be integrated over time in order to reach a decision.
WANG: It’s quite fascinating that what we are seeing now in single-neuron recordings is not coding for what we see or do—sensory and motor coding—but for the processes involved in how we value and make choices. That’s an important advance in neuroscience. I also think it’s fascinating that, when it comes to decision-making, behavior is very adaptive. You can really watch and see how your choice behavior adapts and changes from trial to trial, according to environment and task design, and such changes are reflected in the recorded activity of single neurons.
TKF: And are we starting to understand how that adaptability comes about at the neural level?
WANG: Yes. One of the important ingredients is reinforcement learning and its neural implementation. Reinforcement learning occurs when you are not explicitly taught what you are supposed to learn, but rather learn it by trial and error—by getting feedback about how well you predicted the outcomes of your behavioral choices. There is a substantial body of work showing that the neurotransmitter dopamine plays a central role in reward signaling, and that it can greatly affect changes in the synaptic connections between neurons in a way consistent with reinforcement learning.
LEE: Reinforcement learning theory, which has roots in many different disciplines, including psychology, artificial intelligence and machine learning, computer science, and economics, is actually playing a central role in neurobiological studies of decision-making. Such an economic framework draws a lot of people doing neuroscience studies at multiple levels, including people doing neuroimaging studies in humans and those doing single neuron recordings in animals.
SALZMAN: What are the computations performed in different brain areas, and how are they similar or different? Also how do the different brain areas communicate with each other, and how is information transformed as it moves around in the brain? How do these different representations about important variables for decision making come together and allow you to form a decision?
TKF: Because even when we are making the simplest decision, such as just choosing whether to go kayaking, swimming or hiking while on vacation, we have to place a value on each option, which in turn requires pulling up memories of prior experiences doing these activities, and factoring in which was more emotionally rewarding. So it must be especially challenging to figure out how the brain coordinates all these things.
WANG: Yes, that’s one of the biggest challenges today. Many of the questions so far have been addressed by focusing on local circuits. But decision-making involves so many processes carried out in different parts of the brain that you have to look at how these different parts do different computations in a coordinated way. There are no good theories about the operation of a larger-scale brain system with many interactive modules. That’s going to be one of the big questions people are going to try to address in the next 10 years or more.
LEE: We’re finding that very different functions, like memory and perception and motor control, are not handled separately in the brain. There’s a lot of overlap. That makes it more important for us to try to figure out how these anatomical structures in the brain coordinate their activity and work together.
TKF: In physics, there is a pursuit to identify simple, universal laws of nature. Do you think this is possible in neuroscience--to find some underlying simplicity to all the complexity?
WANG: It comes back to the notion of building blocks. The hope is that if we can understand, with our magnifying lens, the mechanisms for some very important basic computations, such as how neuronal circuits accumulate and value information about different choices, then this understanding will become the basic building blocks that we can put together to explain all kinds of possible complex thinking and behaviors. In my lab we are currently putting those things together in a larger-scale brain circuitry model to explain how your brain switches from task to task, which is rather complex and involves a number of different processes. We and other researchers in the field are testing the idea that when you put those building blocks together in a circuit, you may be able to explain more complex behavior. That’s the hope.
SALZMAN: Another one of the real challenges in terms of understanding how the brain makes decisions about new situations is figuring out how the brain represents them. Our brains are not big enough to represent every situation that we might possibly encounter. It’s a very fundamental question we are going to have to answer in order to put the whole puzzle together.
TKF: So what do you envision as the practical applications of this research?
LEE: The most immediate one is to understand the biological basis of mental disorders.
SALZMAN: We really don’t understand at a systemic level how psychiatric disorders result from dysfunction of neural circuits that affect decision-making. We also don’t understand how psychiatric treatments work -- how they change neural activity in the brain. The reason we know almost nothing about this is because we really don’t know in detail enough about how those neural circuits work normally. It’s like bringing your car to a car mechanic and the mechanic doesn’t know how the car works normally, so how can he fix your car? Some people interested in psychiatric disorders will do more clinically oriented research, and that’s because you can make contributions to understanding what is the better treatment for patients right now. But if you are asking what will be the better treatment in 20, 40 or 100 years, those treatments are only going to arise if we come up with a better understanding of about how cognition and emotion work and interact in the brain to produce things like decisions, because then we can begin to understand how they become dysfunctional. That kind of long-term investment in the basic science of the brain is going to be key to figuring out a wide range of psychiatric disorders.