Executive Summary
In this commentary, the authors discuss Hedgcock and Rao’s (2009) novel and integrative neuroeconomic study of the attraction effect (i.e., that preferences change following of a new but dominated option). The authors view Hedgcock and Rao’s core conclusion—that the attraction effect results from a strategy that avoids emotional trade-offs—as reasonable. Moreover, they laud this work for going beyond identifying “neural correlates.” Instead, it tests well-formed hypotheses about brain function that are based on prior behavioral theories. Under this approach, neuroscience data effectively become an operational proxy for the cognitive processes postulated in prior studies of decision behavior.
Yet, using the techniques of neuroscience to understand judgment and choice behavior poses many challenges. Neuroscience experiments cannot simply replace behavioral research, nor should behavioral scientists dismiss neuroscience data as irrelevant to their goals. Well-grounded neuroscience data can lead to specific and novel inferences about behavior. For example, Hedgcock and Rao (2009) hypothesize that the pattern of activation they observe in the amygdala supports a simple conclusion: Changes in negative emotion underlie the attraction. This hypothesis, which reflects a “reverse inference” from brain to cognition, is justifiable because of the relatively specific role of that brain structure in negative emotion. Conversely, Hedgcock and Rao’s other hypotheses are less compelling because they rely on reverse inference from prefrontal control regions that contribute to many cognitive processes: maintaining and manipulating information in working memory, shifting attention, and inhibiting irrelevant stimuli, among others. This lack of selectivity argues for caution in interpreting their activation.
Despite these caveats, the authors argue that decision scientists, consumer behavior researchers, and neuroscientists should collaborate in projects along the lines of Hedgcock and Rao’s (2009) study. In particular, the authors suggest that one area in which converging neuroscience and behavioral data will be particularly useful is understanding variability in decision making. Every functional magnetic resonance imaging experiment provides a welter of data specific to each individual—the activation within and across regions of interest—and these data may predict and explain variability in participants’ responses. In Hedgcock and Rao’s study, for example, amygdala response is not only reduced when a decoy is present but is more reduced in participants who exhibit a greater attraction effect.
The authors conclude with four simple guidelines for integrating neuroscience and behavioral data. First, neuroscience experiments should test precise hypotheses about brain function to minimize concerns about reverse inference. Second, neuroscience data should be used to identify behavioral models that are biologically implausible (and thus are candidates for revision). Third, researchers should adopt an iterative approach—moving from behavior to brain and back to behavior—that recognizes the converging value of both forms of data without privileging either. Fourth, neuroscience results should not lead to overgeneralizations about function. Robust decision phenomena, such as context-dependent preferences, are likely to be robust precisely because they are multiply determined. Thus, when using an exciting new method, such as neuroimaging, researchers should seek to build and refine models, not to draw conclusions about the sole causes of phenomena.
Biography
Scott A. Huettel is Associate Professor of Psychology and Neuroscience at Duke University. He is also Associate Professor of Psychology and of Neurobiology and is Codirector of the Center for Neuroeconomic Studies. He has a BA in Psychology/Plan II from the University of Texas at Austin and a PhD in Psychology from Duke University. His research investigates the mechanisms underlying decision making, particularly in economic and social contexts, using a combination of behavioral and functional neuroimaging methods. Collectively, these studies fall under the emerging field of “neuroeconomics.” He is also lead author of a textbook on functional magnetic resonance imaging. His laboratory is funded by grants from the National Institutes of Health. He is a member of the Society for Neuroscience, the American Psychological Society, the Society for Neuroeconomics, and the Cognitive Neuroscience Society.
John W. Payne is Joseph J. Ruvane Jr. Professor of Business Administration at Duke University’s Fuqua School of Business. He is also a Professor of Psychology, Professor of Law, and Research Professor in the Institute of Statistics and Decision Sciences at Duke University. He has a BA in Mathematical and Computer Models in the Social Sciences, and an MA and PhD in Psychology, all from the University of California, Irvine. His research deals with how people make decisions and how decision making might be improved. He has authored or edited four books and numerous journal articles and book chapters. Research awards include the Leo Melamed Prize, University of Chicago (2000) and the first (2002) Journal of Consumer Research award for long-term contribution to consumer research. He is a fellow of both the American Psychological Association (Division 3) and the American Psychological Society and is the past president of the Judgment and Decision Making Society.
J Marketing Research, Volume 46, Number 1, February 2009
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