Working at the BE.Center over the last eight months has taught me a lot about what behavioral science looks like outside of the lab. It has been both fascinating and exciting, but I have also had to grapple with a widespread and (what I find) troubling perspective: the field of behavioral science often sees my own area of research, neuroscience, as of little use to behavior change practitioners.
As a behavioral neuroscientist, I find that there is nothing more revealing about human behavior than understanding how our relatively limited brain has evolved ways of navigating our many choices. I see neuroscience as a crucial and integral part of the way the behavioral sciences attempt to understand and change behavior. So, what does neuroscience offer to practitioners beyond what psychology and behavioral economics already do?
Let’s start by imagining two different scenarios:
Scenario A: You expect nothing from me tomorrow, but when tomorrow arrives, I give you $5.
Scenario B: You expect I will give you $10 tomorrow, but when tomorrow arrives, I only give you $5.
Psychology tells us that though the material outcome of both situations is the same, most people are happy to receive $5 in Scenario A and quite unhappy at getting $5 in Scenario B. Why? Because the way we frame things matters. In Scenario A, you expected nothing, so you feel like you gained $5. In Scenario B, you expected $10, so you feel as if you lost $5. This example is simple. But as choices and situations get more complex, so do psychological theories and explanations.
The behavioral sciences now rely on an ever-growing lexicon of theories and cognitive biases to explain people’s choices. Yet, we have no quick way of assessing the reliability and biological validity of these many theories. Put simply, the field still lacks a grounded framework to bring all of its ideas together.
This is where neuroscience can help. Economics mostly focuses on what people’s choices are, and psychology looks at why these choices are likely to happen; the neurosciences, instead, are best positioned to investigate how the brain actually goes about making choices. This focus on the biological underpinnings and limitations of our human brain could help us examine our ever-growing cognitive bias lexicon with greater scrutiny — centering our understanding of decision-making around those explanations that make sense neurobiologically. Behavioral scientists already have many useful guiding principles, and neuroscience offers the chance to go one step further.
Take the scenarios I gave above: we know that the satisfaction we get from that $5 depends on what we expect in a given context. This works because framing is an integral part of the way in which our brain’s neocortex processes information. Rather than making absolute judgements, neurons process information in a relative and comparative way. We call this relative coding, a fast and efficient way of representing reality. Neurons are limited in their capacity to represent information, so they focus their processing power to communicate the information that is most relevant to the context at hand.
To see this working in action, consider the optical illusion below. Though the middle, narrow rectangle is a single shade of grey, it appears as a light-dark gradient because it is ‘processed’ by the brain in the context of the larger rectangle in the background. This property of neurons makes visual processing flexible, and it helps to emphasize those differences that the visual system finds most relevant.
Neuroscientists have found that we attribute value to our choices in much the same way. The value of one option depends on the value of all the other options in a given context. As a result, we can now pinpoint the underlying physical mechanism behind the framing effect, and we can observe and model its properties to predict or validate other cognitive biases that might arise because of it. Relative coding, for example, helps us explain the various ways in which decoy effects can occur. That is, scenarios where we can be swayed to purchase a specific item if we find similarly priced but lower-quality items in the same context. And it can explain why the more options we have, the lousier we are at differentiating between them. Our neurons struggle to meaningfully represent small differences when their encoding range is spread thin.
Neuroscience allows us to look at decision-making from the ground up. The field shows us that our choices aren’t always ‘economically’ rational, but that our brain is rational in how it has evolved to use its limited resources. Our decision systems are simple enough to be efficient, but flexible enough to be useful in a wide array of situations (e.g., from vision all the way to decision-making). These biological realities can therefore help us ground and filter our current list of cognitive biases and, as neuroscience becomes ever-more precise in its understanding of the brain, discover ones that have yet to manifest themselves in academic research. In both cases, neuroscience can help us develop and justify a more unified theory of decision-making.
To their credit, more behavioral scientists have begun incorporating these insights into their theories of decision-making. However, like the field of economics’ incorporation of psychology, the meaningful integration of neuroscience will take time. The evolutionary biologist Theodosius Dobzhansky said it best: “Nothing in biology makes sense except in the light of evolution.” We should strive to remember the form and function of our brains if we hope to make better sense of the decisions we make.
This past spring, Dr. Philipe Bujold stepped out of his academic lab to join Rare’s Center for Behavior & the Environment as our Senior Behavioral Research Associate. Philipe earned his Ph.D. from the University of Cambridge, where he researched the neurobiological underpinnings of decision-making and the role that evolution plays in biasing our choices. At the BE.Center, he helps us translate insights from the behavioral sciences into field-based applications, such as sustainable agriculture and technology adoption