Measuring Trust in Social Neuroeconomics: a Tutorial

Abstract

A brief outline of the fi eld of Neuroeconomics Neuroeconomics is a relatively recent research area that is based on the amalgamation of various disciplines, including experimental eco-nomics, experimental psychology and cognitive neuroscience. The combination of methods from these fi elds has allowed researchers to design experiments investigating brain function during decision-making. Recent experiments in neuroeconomics have signifi cantly furthered our understanding of the neural mechanisms involved in economic and social decisions. On the one hand, recent advances in neuroeconomics have lend support to the validity of economic models by demonstrating that parameters central to economic models, such as decision-utility (e.g. Knutson et al., 2005) and reinforcement learning (Montague et al., 2004) are represented in the brain. On the other hand, they have off ered refi nements of well-established views held by traditional economics, such as the rational-agent model. Against the assumption that economic behavior is purely rational, a wealth of con-verging evidence from behavioral and neuroimaging data underline the role of emotions in decision-making in both fi nancial (McClure et al., 2004), as well as social (Sanfey et al., 2003) settings. Since the current paper is directed at an audience outside of the fi elds that constitute neuroeconomics, we briefl y review some of the core methodologies employed within neuroeconomics and related disciplines to investigate the neural mechanisms of decision-making before we discuss recent advances on the neural correlates of social preferences using the example of trust. Methodologies commonly employed in Neuroeconomics One fundamental building block of experiments in neuroeconomics is the behavioral paradigm that is employed to investigate decision-making. Behavioral paradigms are typically taken from experimental Jan B. Engelmann Hermeneutische Blätter 2010 226 economics and psychology. Examples include the Trust Game (e.g. Berg et al., 1995; Kosfeld et al., 2005), in which participants are faced with the decision to entrust an anonymous person with their mo-ney, or the risky choice task (e.g. Engelmann and Tamir, 2009), in which participants choose between lotteries with diff erent levels of risk and real fi nancial consequences. Behavioral paradigms are then adapted for use with methods from cognitive neuroscience, such as non-invasive brain imaging and stimulation, which require specifi c experimental design considerations related to timing and randomi-zation of events in order to optimize statistical analyses. Common neuroscience methods employed to study the brain in neuroecono-mics include functional Magnetic Resonance Imaging (fMRI), which allows researchers to observe brain function during decision-processes, transcranial magnetic stimulation (TMS), through which a temporary lesion in a targeted brain region can be generated, and pharmaco-logical manipulations, which can temporarily alter the amount of a targeted neurotransmitter within the central nervous system. By far the most commonly used tool to investigate the neural basis of economic and social decision-making is fMRI. This procedure involves placing participants inside an MRI scanner, which provides a record of neuronal activity in form of the Blood Oxygen Level-Dependent (BOLD) response within the brain while participants make decisions in the context of an experimental paradigm. The BOLD response is refl ective of the amount of oxygen that is being delivered to neurons through the blood stream. Changes in blood oxygenation levels lead to localized changes in the ferromagnetic properties of blood that can be visualized by the MRI scanner. Specifi cally, an increased BOLD signal in a certain brain region is indicative of an increase in the amount of oxygen within the blood stream, which in turn is refl ective of an increased demand for energy made by active neurons (e.g. Raichle and Gusnard, 2002). Researchers can then couple the data provided by the fMRI scanner, the BOLD signal, with behavioral data obtained from participants' task performance that refl ects economic and social preferences to localize regions involved in producing behavior using statistical analyses based on the General Linear Model. To accomplish this feat, recent experiments have used model-based approaches to investigate brain function (O'Doherty et al., 2007). In such sophisticated analy-ses, quantitative computational models estimate parameters refl ective of participants' behavior that are relevant to the cognitive processes underlying economic decision-making. These variables can then be used to make predictions about what patterns of activation a given