Value normalization in decision making: theory and evidence

A sizable body of evidence has shown that the brain computes several types of value-related signals to guide decision making, such as stimulus values, outcome values, and prediction errors. A critical question for understanding decision-making mechanisms is whether these value signals are computed using an absolute or a normalized code. Under an absolute code, the neural response used to represent the value of a given stimulus does not depend on what other values might have been encountered. By contrast, under a normalized code, the neural response associated with a given value depends on its relative position in the distribution of values. This review provides a simple framework for thinking about value normalization, and uses it to evaluate the existing experimental evidence

Nucleus Accumbens Mediates Relative Motivation for Rewards in the Absence of Choice

To dissociate a choice from its antecedent neural states, motivation associated with the expected outcome must be captured in the absence of choice. Yet, the neural mechanisms that mediate behavioral idiosyncrasies in motivation, particularly with regard to complex economic preferences, are rarely examined in situations without overt decisions. We employed functional magnetic resonance imaging in a large sample of participants while they anticipated earning rewards from two different modalities: monetary and candy rewards. An index for relative motivation toward different reward types was constructed using reaction times to the target for earning rewards. Activation in the nucleus accumbens (NAcc) and anterior insula (aINS) predicted individual variation in relative motivation between our reward modalities. NAcc activation, however, mediated the effects of aINS, indicating the NAcc is the likely source of this relative weighting. These results demonstrate that neural idiosyncrasies in reward efficacy exist even in the absence of explicit choices, and extend the role of NAcc as a critical brain region for such choice-free motivation

iPhone 3G student developer competition

Project Partners: Reynolds Journalism Institute/Missouri School of Journalism, College of Engineering, Trulaske College of Business, College of Education, College of Agriculture, Food & Natural Resources, Apple Inc and AT&T Inc.Proposal for the 2008 project: "iPhone 3G Student Developer Competition." Partners: Reynolds Journalism Institute/Missouri School of Journalism, College of Engineering, Trulaske College of Business, College of Education, College of Agriculture, Food & Natural Resources, Apple Inc and AT&T Inc. Project Description: The above partners propose to sponsor a student contest, modeled on last year's successful RJI/Adobe AIR Student Competition, to develop, test and market native iPhone applications designed to improve the practice and the business of American journalism. Finalist teams will receive private consulting at Apple Inc.'s world headquarters in Cupertino, CA. The project's sponsors will host a symposium in Spring 2009 on the MU campus to showcase the competition as a model for interdisciplinary education and entrepreneurship. The winning student team will attend Apple's 2009 Worldwide Developer Conference in San Francisco.MU Interdisciplinary Innovations Fun

Loss Aversion Reflects Information Accumulation, Not Bias: A Drift-Diffusion Model Study

Defined as increased sensitivity to losses, loss aversion is often conceptualized as a cognitive bias. However, findings that loss aversion has an attentional or emotional regulation component suggest that it may instead reflect differences in information processing. To distinguish these alternatives, we applied the drift-diffusion model (DDM) to choice and response time (RT) data in a card gambling task with unknown risk distributions. Loss aversion was measured separately for each participant. Dividing the participants into terciles based on loss aversion estimates, we found that the most loss-averse group showed a significantly lower drift rate than the other two groups, indicating overall slower uptake of information. In contrast, neither the starting bias nor the threshold separation (barrier) varied by group, suggesting that decision thresholds are not affected by loss aversion. These results shed new light on the cognitive mechanisms underlying loss aversion, consistent with an account based on information accumulation

Correspondence: Are Cognitive Functions Localizable? Colin Camerer et al. versus Marieke van Rooij and John G. Holden

The Fall 2011 issue of this journal published a two-paper section on “Neuroeconomics.” One paper, by Ernst Fehr and Antonio Rangel, clearly and concisely summarized a small part of the fast-growing literature. The second paper, “It’s about Space, It’s about Time, Neuroeconomics, and the Brain Sublime,” by Marieke van Rooij and Guy Van Orden, is beautifully written and enjoyable to read, but misleading in many critical ways. A number of economists and neuroscientists working at the intersection of the two fields shared our reaction and have signed this letter, as shown below. Some of the paper’s descriptions of empirical findings and methods in neuroeconomics are incomplete, badly out of date, or flatly wrong. In studies the authors describe in detail, their skeptical interpretations have often been refuted by published data, old and new, that they overlook

Translating upwards: linking the neural and social sciences via neuroeconomics

The social and neural sciences share a common interest in understanding the mechanisms that underlie human behaviour. However, interactions between neuroscience and social science disciplines remain strikingly narrow and tenuous. We illustrate the scope and challenges for such interactions using the paradigmatic example of neuroeconomics. Using quantitative analyses of both its scientific literature and the social networks in its intellectual community, we show that neuroeconomics now reflects a true disciplinary integration, such that research topics and scientific communities with interdisciplinary span exert greater influence on the field. However, our analyses also reveal key structural and intellectual challenges in balancing the goals of neuroscience with those of the social sciences. To address these challenges, we offer a set of prescriptive recommendations for directing future research in neuroeconomics

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal–parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity