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of these “unobservables” more directly than ever before. The tools and language of cognitive neuroscience make it easy to explain the Americans-in-London phenomenon through the concept of Stroop tasks and an understanding of which brain regions are active during those tasks, such as the cingulate cortex. The cingulate (Latin for “collar”) wraps around the corpus collosum in the center of the brain (a thick band of fibers that connect the left and right hemispheres). It is a large region and the anterior (front) part is known from hundreds of studies to be active during conflict, “executive function,” and the overriding of natural responses.

This approach has revealed some new predictions. One of these pertains to the cingulate cortex, which is activated not only by Stroop tasks but also by tasks that require “executive function,” attention, and resolving conflict. If the cingulate is busy doing another task, then less attention will be paid to correcting the Stroop mistake. This explanation is likely to predict that Americans talking on cell phones, or trying to remember the name of the spouse of an acquaintance they’ve just spotted, will have more accidents crossing London streets. Admittedly, one could get this prediction from an economic model, but only if scarce attention is built in, along with some facts about what kinds of tasks “spend” attention, whether attention can be consciously directed, and so forth. These details are best supplied by cognitive neuroscience rather than by casual intuition.

Behavioral economics is the use of psychological methods and constructs to introduce limits on computation, willpower, and self-interest into economic analysis. Neuroeconomics extends upon behavioral economics by including neural data for the purpose of creating a mathematical approach to the micro-foundation of economics that is neurally measurable (Rangel, Camerer, and Montague, 2008). The types of models that are likely to emerge from neuroeconomics will be computational models that are very much in keeping with famous American psychologist Herbert Simon’s idea of using algorithms to express cognitive processes. Below I will briefly describe the long-run goal of this “neuroeconomic” approach, give an extended example, and mention some caveats.

THE GOAL OF NEUROECONOMICS

Neuroscientists use a number of different tools, including animal studies, analysis of the behavior of patients with neurological lesions or psychiatric disorders, response times, tracking of eye movements to measure information acquisition, psychophysiological measures (e.g., skin conductance, pupil dilation), pharmacological interventions, and computational modeling as well as scans from functional magnetic resolution imaging (fMRI)1 and other devices providing brain images. Each tool has some limitations that can be overcome by the advantages of a complementary tool; thus, combinations of studies often have remarkable scientific power.

The case for using neuroscience to explain economic decisions (hence the notion of neuroeconomics) rests on three principles:

1. Economists are interested in individual choices.

2. The brain is the organ that makes choices.

3. More will eventually be known about the brain due to technological advances.

These principles, if accepted, logically imply that economists should be interested in what technological advances tell us about the brain.

Of course, there are some ways to wriggle out of this implication. You could say that economics is not the study of choices but is instead—and should only ever be—the use of a particular type of theory that is inherently agnostic about brain mechanisms (i.e., rational choice theory). I think that this claim is provably false because many economists have used other approaches than rational choice theory when it has proved interesting or useful to do so (e.g., learning and evolutionary theories applied to strategic choices). It is also reasonable to figure out where preferences come from by rooting them in processes like parental socialization of children and early