The field of neuroeconomics incorporates many disciplines of learning, including economics, cognitive and social psychology, neuroscience, and biology. Techniques from each discipline are used to try and further understand the decisions we make. Look below to learn more about the specific techniques we use to try and shed light on why we, as humans, make the choices we do.
Functional Neuroimaging Methods
Neuroimaging research in our laboratory uses functional magnetic resonance imaging (fMRI) to study the mechanisms underlying decision making. To better understand this neuroimaging technique and its limitations, we have published studies on the basic properties of the hemodynamic response measured by fMRI. Properties investigated by our laboratory have included refractory effects associated with repeated stimulus presentation, stimulus-specific adaptation effects, differences in the hemodynamic response across individuals and subject groups, effects of signal-to-noise upon the reproducibility of activation, and the relation between fMRI activation and intracranially recorded activity. Some studies use pattern classification algorithms derived from machine learning (e.g., support vector machines, SVM) to identify local information carried across voxels within a brain region. We also apply functional connectivity techniques to understand large-scale networks in the brain -- and how those networks contribute to behavior.
Finally, we explore major issues in neuroimaging research through review articles, meta-analyses, and our textbook Functional Magnetic Resonance Imaging (3nd edition published in 2014).
Economic Decision Making
To understand how people make decisions, our laboratory adopts many of the practices of behavioral economics (e.g, incentive-compatible choices, full information). This provides an important advantage for cognitive neuroscience research: it allows us to specify the factors underlying choice, both within and across individuals, with great precision. Moreover, many (but not all!) import real-world choices can be described using economic terms: cost-benefit tradeoffs, utility functions, risk and ambiguity, and temporal delay. Our core goal is to use knowledge of the functional properties of the brain to develop better understanding of economic phenomena. In essence, we seek models of brain function that provide new insights for models of behavior.
We explore these and other aspects of decision making using functional magnetic resonance imaging, eye-tracking, measures of behavior, and computational modeling. We collaborate with other laboratories at Duke, at Duke-NUS Medical School, and at other institutions to extend this work via different techniques (e.g., EEG, TMS) and new populations (e.g., sleep deprivation, younger children and older adults, addiction).
Social Decision Making
Many decisions lead to consequences for others. Our laboratory explores several sorts of social decisions: those that involve personal sacrifice for another’s benefit (e.g., choices in a competitive game). We adopt the working hypothesis that, throughout evolutionary time, many of the most critical decisions were social, not economic. Thus, neural systems for the adaptive control of behavior often acted to achieve social goals, in concert with systems for understanding the cognitions and desires of others. We believe that this is an important and understudied area of research — one that will merge with neuroeconomics over the coming years.
We also conduct traditional cognitive neuroscience research on the executive control of behavior, with an emphasis on the prefrontal cortex (PFC). These studies use tasks derived from cognitive psychology -- often involving response selection, behavioral conflict, or memory -- and seek to elucidate the functional properties of specific regions within PFC by converging evidence across multiple studies. This work often connects to our lab's interest in neuroeconomics. For example, we have used sets of different decision making tasks to map out a functional topography within medial prefrontal cortex. We have also investigated parallels between executive control processes (e.g., emotion regulation) and decision making processes (e.g., valuation). In recent years, we have explored techniques of meta-analysis that provide insight about large-scale regularities in the cognitive neuroscience literature.