Peter N. C. Mohr

Neural Processing of Risk

In our everyday life, we often have to make decisions with risky consequences, such as choosing a restaurant for dinner or choosing a form of retirement saving. To date, however, little is known about how the brain processes risk. Recent conceptualizations of risky decision making highlight that it is generally associated with emotions but do not specify how emotions are implicated in risk processing. Moreover, little is known about risk processing in non-choice situations and how potential losses influence risk processing. Here we used quantitative meta-analyses of functional magnetic resonance imaging experiments on risk processing in the brain to investigate (1) how risk processing is influenced by emotions, (2) how it differs between choice and non-choice situations, and (3) how it changes when losses are possible. By showing that, over a range of experiments and paradigms, risk is consistently represented in the anterior insula, a brain region known to process aversive emotions such as anxiety, disappointment, or regret, we provide evidence that risk processing is influenced by emotions. Furthermore, our results show risk-related activity in the dorsolateral prefrontal cortex and the parietal cortex in choice situations but not in situations in which no choice is involved or a choice has already been made. The anterior insula was predominantly active in the presence of potential losses, indicating that potential losses modulate risk processing.

Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions

The ability to rapidly and flexibly adapt decisions to available rewards is crucial for survival in dynamic environments. Reward-based decisions are guided by reward expectations that are updated based on prediction errors, and processing of these errors involves dopaminergic neuromodulation in the striatum. To test the hypothesis that the COMT gene Val158Met polymorphism leads to interindividual differences in reward-based learning, we used the neuromodulatory role of dopamine in signaling prediction errors. We show a behavioral advantage for the phylogenetically ancestral Val/Val genotype in an instrumental reversal learning task that requires rapid and flexible adaptation of decisions to changing reward contingencies in a dynamic environment. Implementing a reinforcement learning model with a dynamic learning rate to estimate prediction error and learning rate for each trial, we discovered that a higher and more flexible learning rate underlies the advantage of the Val/Val genotype. Model-based fMRI analysis revealed that greater and more differentiated striatal fMRI responses to prediction errors reflect this advantage on the neurobiological level. Learning rate-dependent changes in effective connectivity between the striatum and prefrontal cortex were greater in the Val/Val than Met/Met genotype, suggesting that the advantage results from a downstream effect of the prefrontal cortex that is presumably mediated by differences in dopamine metabolism. These results show a critical role of dopamine in processing the weight a particular prediction error has on the expectation updating for the next decision, thereby providing important insights into neurobiological mechanisms underlying the ability to rapidly and flexibly adapt decisions to changing reward contingencies.

Neuroeconomics and aging: Neuromodulation of economic decision making in old age

Economic decision making is a complex process of integrating and comparing various aspects of economically relevant choice options. Neuroeconomics has made important progress in grounding these aspects of decision making in neural systems and the neurotransmitters therein. The dopaminergic and serotoninergic brain systems have been identified as key neurotransmitter systems involved in economic behavior. Both are known to be prone to significant changes during the adult lifespan. Similarly, economic behavior undergoes significant age-related changes over the course of the adult lifespan. Here we propose a triadic relationship between (a) economic decision making, (b) dopaminergic and serotonergic neuromodulation, and (c) aging. In this review, we describe the different relationships around this triad in detail and summarize current evidence that supports them. Based on the reviewed evidence, we propose new research agendas that take the entire triad into account.

Variability in Brain Activity as an Individual Difference Measure in Neuroscience

In neurobiological research it is common to identify effects for a certain population of individuals, thereby averaging across participants. Thus, the goal of a functional magnetic resonance imaging (fMRI) data analysis typically is to test whether an average task-induced change in the blood oxygen

Neural foundations of risk-return trade-off in investment decisions.

Many decisions people make can be described as decisions under risk. Understanding the mechanisms that drive these decisions is an important goal in decision neuroscience. Two competing classes of risky decision making models have been proposed to describe human behavior, namely utility-based models and risk-return models. Here we used a novel investment decision task that uses streams of (past) returns as stimuli to investigate how consistent the two classes of models are with the neurobiological processes underlying investment decisions (where outcomes usually follow continuous distributions). By showing (a) that risk-return models can explain choices behaviorally and (b) that the components of risk-return models (value, risk, and risk attitude) are represented in the brain during choices, we provide evidence that risk-return models describe the neural processes underlying investment decisions well. Most importantly, the observed correlation between risk and brain activity in the anterior insula during choices supports risk-return models more than utility-based models because risk is an explicit component of risk-return models but not of the utility-based models.

Risk Patterns and Correlated Brain Activities. Multidimensional Statistical Analysis of fMRI Data in Economic Decision Making Study

Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556–2563, 2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284–298, 2009) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects’ decision behavior.

Aging and neuroeconomics: Insights from research on neuromodulation of reward-based decision making

Abstract ‘Neuroeconomics’ can be broadly defined as the research of how the brain interacts with the environment to make decisions that are functional given individual and contextual constraints. Deciphering such brain-environment transactions requires mechanistic understandings of the neurobiological processes that implement value-dependent decision making. To this end, a common empirical approach is to investigate neural mechanisms of reward-based decision making. Flexible updating of choices and associated expected outcomes in ways that are adaptive for a given task (or a given set of tasks) at hand relies on dynamic neurochemical tuning of the brain’s functional circuitries involved in the representation of tasks, goals and reward prediction. Empirical evidence as well as computational theories indicate that various neurotransmitter systems (e.g., dopamine, norepinephrine, and serotonin) play important roles in reward-based decision making. In light of the apparent aging-related decline in various aspects of the dopaminergic system as well as the effects of neuromodulation on reward-related processes, this article focuses selectively on the literature that highlights the triadic relations between dopaminergic modulation, reward-based decision making, and aging. Directions for future research on aging and neuroeconomoics are discussed.

Portfolio Decisions and Brain Reactions via the CEAD method

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Corrigendum to "Neuroeconomics and aging : Neuromodulation of economic decision making in old age" [Neuroscience & Biobehavioral Reviews, 34 (5) (2010), 678-688. doi:10.1016/j.neubiorev.2009.05.010]

The authors regret the following error in their recent publication. On page 684 they described an experiment on temporal discounting onducted by Green et al. (1994). Here, they incorrectly stated that Green et al. (1994) observed an age-related increase in discount rates, hen in fact Green et al. (1994) found lower discount rates in older adults compared to younger adults and pre-teen. This result is, however, nly partly in line with later findings by Harrison et al. (2002) and Read and Read (2004). As correctly described in this paper, both studies eport decreasing discount rates from young adulthood to middle adulthood, but increasing discount rates from middle adulthood to old dulthood, suggesting a potential U-shaped relationship between age and delay discounting across wider age ranges.

Internet users' valuation of enhanced data protection on social media: which aspects of privacy are worth the most?

As the development of the Internet and social media has led to pervasive data collection and usage practices, consumers’ privacy concerns have increasingly grown stronger. While previous research has investigated consumer valuation of personal data and privacy, only few studies have investigated valuation of different privacy aspects (e.g., third party sharing). Addressing this research gap in the literature, the present study explores Internet users’ valuations of three different privacy aspects on a social networking service (i.e., Facebook), which are commonly captured in privacy policies (i.e., data collection, data control, and third party sharing). A total of 350 participants will be recruited for an experimental online study. The experimental design will consecutively contrast a conventional, free-of-charge version of Facebook with four hypothetical, privacy-enhanced premium versions of the same service. The privacy-enhanced premium versions will offer (1) restricted data collection on side of the company; (2) enhanced data control for users; and (3) no third party sharing, respectively. A fourth premium version offers full protection of all three privacy aspects. Participants’ valuation of the privacy aspects captured in the premium versions will be quantified measuring willingness-to-pay. Additionally, a psychological test battery will be employed to examine the psychological mechanisms (e.g., privacy concerns, trust, and risk perceptions) underlying the valuation of privacy. Overall, this study will offer insights into valuation of different privacy aspects, thus providing valuable suggestions for economically sustainable privacy enhancements and alternative business models that are beneficial to consumers, businesses, practitioners, and policymakers, alike.