Mapping the Neural Basis of Neuroeconomics with Functional Magnetic Resonance Imaging: A Narrative Literature Review

Neuroeconomics merges neuroscience, economics, and psychology to investigate the neural basis of decision making. Decision making involves assessing outcomes with subjective value, shaped by emotions and experiences, which are crucial in economic decisions. Functional MRI (fMRI) reveals key areas of the brain, including the ventro-medial prefrontal cortex, that are involved in subjective value representation. Collaborative interdisciplinary efforts are essential for advancing the field of neuroeconomics, with implications for clinical interventions and policy design. This review explores subjective value in neuroeconomics, highlighting brain regions identified through fMRI studies.

The neural correlates of value representation: From single items to bundles

One of the core questions in Neuro-economics is to determine where value is represented. To date, most studies have focused on simple options and identified the ventromedial prefrontal cortex (VMPFC) as the common value region. We report the findings of an fMRI study in which we asked participants to make pairwise comparisons involving options of varying complexity: single items (Control condition), bundles made of the same two single items (Scaling condition) and bundles made of two different single items (Bundling condition). We construct a measure of choice consistency to capture how coherent the choices of a participant are with one another. We also record brain activity while participants make these choices. We find that a common core of regions involving the left VMPFC, the left dorsolateral prefrontal cortex (DLPFC), regions associated with complex visual processing and the left cerebellum track value across all conditions. Also, regions in the DLPFC, the ventrolateral prefrontal cortex (VLPFC) and the cerebellum are differentially recruited across conditions. Last, variations in activity in VMPFC and DLPFC value-tracking regions are associated with variations in choice consistency. This suggests that value based decision-making recruits a core set of regions as well as specific regions based on task demands. Further, correlations between consistency and the magnitude of signal change with lateral portions of the PFC suggest the possibility that activity in these regions may play a causal role in decision quality.

Information acquisition and cognitive processes during strategic decision-making: Combining a policy-capturing study with eye-tracking data

Policy-capturing (PC) methodologies have been employed to study decision-making, and to assess how decision-makers use available information when asked to evaluate hypothetical situations. An important assumption of the PC techniques is that respondents develop cognitive models to help them efficiently process the many information cues provided while reviewing a large number of decision scenarios. With this study, we seek to analyze the process of answering a PC study. We do this by investigating the information acquisition and the cognitive processes behind policy-capturing, building on cognitive and attention research and exploiting the tools of eye-tracking. Additionally, we investigate the role of experience in mediating the relationship between the information processed and judgments in order to determine how the cognitive models of student samples differ from those of professionals. We find evidence of increasing efficiency as a function of practice when respondents undergo the PC experiment. We also detect a selective process on information acquisition; such selection is consistent with the respondents' evaluation. While some differences are found in the information processing among the split sample of students and professionals, remarkable similarities are detected. Our study adds confidence to the assumption that respondents build cognitive models to handle the large amounts of information presented in PC experiments, and the defection of such models is not substantially affected by the applied sample.

Electrophysiological Markers of Fairness and Selfishness Revealed by a Combination of Dictator and Ultimatum Games

Individual behavior during financial decision making is motivated by fairness, but an unanswered question from previous studies is whether particular patterns of brain activity correspond to different profiles of fairness. Event Related Potentials (ERPs) were recorded from 39 participants who played the role of allocators in a Dictator Game (DG) and responders in an Ultimatum Game (UG). Two very homogeneous groups were formed by fair and selfish individuals. At fronto-central cortical sites, the latency of ERP early negativity (N1) was 10 ms shorter in selfish participants than in fair participants. In fair DG players, the subsequent positive wave P2 suggested that more cognitive resources were required when they allocated the least gains to the other party. P2 latency and amplitude in the selfish group supported the hypothesis that these participants tended to maximize their profit. During UG, we observed that medial frontal negativity (MFN) occurred earlier and with greater amplitude when selfish participants rejected less favorable endowment shares. In this case, all players received zero payoffs, which showed that MFN in selfish participants was associated with a spiteful punishment. At posterior-parietal sites, we found that the greater the selfishness, the greater the amplitude of the late positive component (LPC). Our results bring new evidence to the existence of specific somatic markers associated with the activation of distinct cerebral circuits by the evaluation of fair and unfair proposals in participants characterized by different expressions of perceived fairness, thus suggesting that a particular brain dynamics could be associated with moral decisions.

Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions

This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms-pertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.

Focal stimulation of the temporoparietal junction improves rationality in prosocial decision-making

We tested the hypothesis that modulation of neurocomputational inputs to value-based decision-making affects the rationality of economic choices. The brain's right temporoparietal junction (rTPJ) has been functionally associated with both social behavior and with domain-general information processing and attention. To identify the causal function of rTPJ in prosocial decisions, we administered focal high definition transcranial direct current stimulation (HD-tDCS) while participants allocated money between themselves and a charity in a modified dictator game. Anodal stimulation led to improved rationality as well as increased charitable giving and egalitarianism, resulting in more consistent and efficient choices and increased sensitivity to the price of giving. These results are consistent with the theory that anodal stimulation of the rTPJ increases the precision of value computations in social decision-making. Our results demonstrate that theories of rTPJ function should account for the multifaceted role of the rTPJ in the representation of social inputs into value-based decisions.

Dual Process Theories in Behavioral Economics and Neuroeconomics: a Critical Review

Despite their popularity, dual process accounts of human reasoning and decision-making have come under intense scrutiny in recent years. Cognitive scientists and philosophers alike have come to question the theoretical foundations of the ‘standard view’ of dual process theory and have challenged the validity and relevance of evidence in support of it. Moreover, attempts to modify and refine dual process theory in light of these challenges have generated additional concerns about its applicability and refutability as a scientific theory. With these concerns in mind, this paper provides a critical review of dual process theory in economics, focusing on its role as a psychological framework for decision modeling in behavioral economics and neuroeconomics. I argue that the influx of criticisms against dual process theory challenge the descriptive accuracy of dualistic decision models in economics. In fact, the case can be made that the popularity of dual process theory in economics has less to do with the empirical success of dualistic decision models, and more to do with the convenience that the dual process narrative provides economists looking to explain-away decision anomalies. This leaves behavioral economists and neuroeconomists with something of a dilemma: either they stick to their purported ambitions to give a realistic description of human decision-making and give up the narrative, or they revise and restate their scientific ambitions.

A Neuroeconomic Framework for Creative Cognition

Neuroeconomics is the study of the neurobiological bases of subjective preferences and choices. We present a novel framework that synthesizes findings from the literatures on neuroeconomics and creativity to provide a neurobiological description of creative cognition. We propose that value-based decision-making processes and activity in the locus ceruleus-norepinephrine (LC-NE) neuromodulatory system underlie creative cognition, as well as the large-scale brain network dynamics shown to be associated with creativity. This reconceptualization leads to several falsifiable hypotheses that can further understanding of creativity, decision making, and brain network dynamics.

Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics

In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA) technique, denoted as DSSA, to filter out the noise (nonstationary sources) in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively.

Pitfalls in Organizational Neuroscience: A Critical Review and Suggestions for Future Research

The potential of neuroscience to be a viable framework for studying human behavior in organizations depends on scholars’ ability to evaluate, design, analyze, and accurately interpret neuroscientific research. Prior to the publishing of this special issue, relatively little guidance has been available in the management literature for scholars seeking to integrate neuroscience and organization science in a balanced, informative and methodologically rigorous manner. In response to this need, we address design logic and inferential issues involved in evaluating and conducting neuroscience research capable of informing organizational science. Specifically, neuroscience methods of functional magnetic resonance imaging, electroencephalography, lesion studies, transcranial magnetic stimulation, and transcranial direct current stimulation are reviewed, with attention to how these methods might be combined to achieve convergent evidence. We then discuss strengths and limitations of various designs, highlighting the issue of reverse inference as precarious yet necessary for organizational neuroscience. We offer solutions for addressing limitations related to reverse inference, and propose features that allow stronger inferences to be made. The article concludes with a review of selected empirical work in organizational neuroscience in light of these critical design features.