Our research is primarily focused on how the brain makes decisions of preference. These decisions can range from simple binary food choice (e.g. do I want to eat an apple or an orange?) to very complex, multi-attribute choices (e.g. what apartment should I rent?). They can involve basic drives like food and shelter, or more evolved considerations like self-control and social concerns. We use every tool at our disposal to understand how people balance these different factors.
Attention and its effects on preferences
When people make decisions, they don’t look at each option once, close their eyes, and instantly pick their favorite. Instead, they look back and forth, often taking multiple seconds to make even the simplest decision. We have discovered that these shifts of attention actually have an important influence on the decision and that items that attract the eyes are more likely to be chosen. We are actively investigating the ways in which this effect can help us to understand behavioral phenomena and psychological biases. We are of course also interested in what attracts attention in the first place.
Dynamic modeling of decision making
Drift-diffusion (DDM) and other related evidence-accumulation models have been used for decades to capture how people (and animals) make perceptual decisions (e.g. which of these two apples is bigger?). In our lab, we are interested in using these models to capture how people make preferential decisions. We have developed a version of the DDM, that we call the attentional DDM (aDDM) that captures the effect of attention on choices (described above) and is also able to predict behavior in different environments. For example, using the aDDM fit to food choice, we have been able to predict behavior in several social-decision experiments where subjects chose how to divide money between themselves and others. We are working to show that this model captures a universal mechanism that our brains use to make decisions. The model is also useful because it provides a way to use response times to infer the strength of peoples’ preferences. We also use the model to study how people allocate their time, specifically addressing how people spend too much time on decisions that matter the least.
Valuation and choice in the brain
From our behavioral modeling work (described above) we have a candidate model for how preferential decisions are being made, but at the neural level we still know very little about the actual mechanisms behind this valuation and choice process. In our lab, we want to better understand which brain regions are responsible for the different computations in the DDM, and how external influences such as attention, cognitive load, etc. influence those computations. This research involves using correlational techniques such as fMRI and EEG to measure brain activity, or causal techniques such as brain stimulation (e.g. TMS & tDCS) to influence brain activity. One application of this work is in using neural signals of value to solve classic incentive problems in economics. In essence, we can compare what people tell us to what their brains tell us and tax them more when the two disagree.