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Humans discount rewards as a function of the delay to their receipt. This tendency is referred to as delay discounting and has been extensively researched in the last decades. The magnitude effect (i.e., smaller rewards are discounted more steeply than larger rewards) and the trait effect (i.e., delay discounting of one reward type is predictive of delay discounting of other reward types) are two phenomena which have been consistently observed for a variety of reward types. Here, we wanted to investigate if these effects also occur in the context of the novel but widespread reward types of Instagram followers and likes and if delay discounting of these outcomes is related to self-control and Instagram screen time. In a within-subject online experiment, 214 Instagram users chose between smaller, immediate and larger, delayed amounts of hypothetical money, Instagram followers and likes. First, we found that the magnitude effect also applies to Instagram followers and likes. Second, delay discounting of all three reward types was correlated, providing further evidence for a trait influence of delay discounting. Third, no relationships were found between delay discounting and self-control as well as Instagram screen time, respectively. However, a user’s average like count was related to delay discounting of Instagram likes.

Recent empirical findings have indicated that gaze allocation plays a crucial role in simple decision behaviour. Many of these findings point towards an influence of gaze allocation onto the speed of evidence accumulation in an accumulation-to-bound decision process (resulting in generally higher choice probabilities for items that have been looked at longer). Further, researchers have shown that the strength of the association between gaze and choice behaviour is highly variable between individuals, encouraging future work to study this association on the individual level. However, only few decision models exist that easily allow studying the gaze-choice association on the individual level, due to the high cost of developing and implementing them. The model space is particularly scarce for choice sets with more than two choice alternatives. Here, we present GLAMbox, a Python-based toolbox that is built upon PyMC3 and allows the easy application of the gaze-weighted linear accumulator model (GLAM) to experimental choice data. The GLAM assumes gaze-dependent evidence accumulation in a linear stochastic race that extends to decision scenarios with many choice alternatives. GLAMbox enables Bayesian parameter estimation of the GLAM for individual, pooled or hierarchical models, provides an easy-to-use interface to predict choice behaviour and visualize choice data, and benefits from all of PyMC3’s Bayesian statistical modeling functionality. Further documentation, resources and the toolbox itself are available athttps://github.com/glamlab/glambox.

Emotion regulation impacts the expected emotional responses to the outcomes of risky decisions via activation of cognitive control strategies. However, whether the regulation of emotional responses to preceding, incidental stimuli also impacts risk-taking in subsequent decisions is still poorly understood. In this study, we investigated the interplay between the regulation of incidentally induced emotional responses and subsequent choice behavior using a risky decision-making task in two independent samples (behavioral and functional magnetic resonance imaging experiment). We found that overall, emotion regulation was followed by less risky decisions, which was further reflected in an increase in activation in brain regions in dorsolateral and ventrolateral prefrontal cortex and cingulate cortex. These findings suggest that altering incidental emotions using reappraisal strategies impacts on subsequent risk-taking in decision-making.

Emotions have a strong impact on our everyday life, including our mental health, sleep pattern, overall well-being, and judgment and decision making. Our paper is the first study to show that incidental emotions, i.e., emotions not related to the actual choice problem, influence the compliance behavior of individuals. In particular, we provide evidence that individuals have a lower willingness to comply with social norms after being primed with positive incidental emotions compared with aversive emotions. This result is replicated in a second study. As an extension to our first study, we add a neutral condition as a control. Willingness to comply in this condition ranges between the other two conditions. Importantly, this finding indicates that the valence of an emotion but not its arousal drives the influence on compliance behavior. Furthermore, we show that priming with incidental emotions is only effective if individuals are - at least to some extent - emotionally sensitive.

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 (ID) study for ID-related effects. We propose a new technique for identifying activated brain regions: Cluster, Estimation, Activation and Decision (CEAD) 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 NCUT spectral clustering. The information within each cluster can then be extracted by the flexible 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 BOLD 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 (aINS) and DMPFC. 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.

Individuals in most industrialized countries have to make investment decisions throughout their adult life span to save for their retirement. These decisions substantially affect their living standards in old age. Research on cognitive aging has already demonstrated several changes in cognitive functions (e.g., processing speed) that likely influence investment decisions. This review brings together research on behavioral and neural aspects of financial decision making and aging to advance knowledge on age-related changes in financial decision making. The dopaminergic system plays a key role in financial decision making, both in financial decisions from description and financial decisions from experience. Importantly, both dopaminergic neuromodulation and financial decision making change during healthy aging. Especially when the parameters of the return distribution have to be learned from experience, older adults show a different and suboptimal choice behavior compared to younger adults. Based on these observations we suggest ways to circumvent the age-related bias in financial decision making to improve older adults' wealth.

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 reanalyze functional magnetic resonance imaging (fMRI) data on 17 subjects which were exposed to an investment decision task from Mohr et al. (2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. Our goal is to capture the dynamic behavior of specific brain regions of all subjects in this high-dimensional time series data, by a flexible factor approach resulting in a low dimensional representation. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park et al. (2009) and identify task-related activations in space and dynamics in time. Further, we classify the risk attitudes of all subjects based on the estimated lowdimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects' decision behavior. Keywords: risk, risk attitude, fMRI, decision making, medial orbifrontal cortex, semiparametric model, factor structure, SVMDecision 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 reanalyze functional magnetic resonance imaging (fMRI) data on 17 subjects which were exposed to an investment decision task from Mohr et al. (2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. Our goal is to capture the dynamic behavior of specific brain regions of all subjects in this high-dimensional time series data, by a exible factor approach resulting in a low dimensional representation. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park et al. (2009) and identify task-related activations in space and dynamics in time. Further, we classify the risk attitudes of all subjects based on the estimated lowdimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects' decision behavior.

In our everyday life we often have to make decisions with uncertain consequences, for instance in the context of retirement savings. Although these decisions can have important consequences (i.e., they can have an effect on our living standard in old age) it is still not fully understood how individuals process risk and how they make decisions under risk. In the context of this dissertation I have investigated three main questions all related to the topic risky decision making, namely (a) how neural processing of risk is influenced by emotions, context, and potential losses, (b) how individuals value choice options with continuous outcome distributions, and (c) how age-related changes in economic decision making are related to neurobiological changes during the adult life span. By showing in Project I that risk is consistently represented in the anterior insula, a brain region known to process aversive emotions like anxiety, disappointment, or regret, I provide evidence that risk processing is influenced by emotions. Further, the results show risk-related activity in the dorsolateral prefrontal cortex and the parietal cortex in choice situations but not in situations were 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. The results thus provide evidence that neural processing of risk is influenced by emotions, context, and potential losses. In Project II I can show that risk-return models can explain choices behaviorally and that the components of risk-return models (value, risk, and risk attitude) are represented in the brain during choices. 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 in the case of continuous outcome distributions because risk is an explicit component of risk-return models but not of the utility-based models. In Project III I identify the dopaminergic and serotoninergic brain systems as key neurotransmitter systems involved in economic behavior. Both are known to be prone to significant changes during the adult life span. Similarly, economic behavior undergoes significant age-related changes over the course of the adult life span. Consequently, I propose a triadic relationship between (a) economic decision making, (b) dopaminergic and serotoninergic neuromodulation, and (c) aging., In vielen Situationen des Alltags, wie beispielsweise bei der Wahl einer privaten Altersvorsorge, müssen Entscheidungen unter Risiko getroffen werden. Diese können, wie im Falle von Altersvorsorgeentscheidungen, die den Lebensstandard im Alter beeinflussen, weitreichende Konsequenzen für das weitere Leben haben. Zum heutigen Zeitpunkt ist allerdings weitestgehend unklar wie Menschen Risiko verarbeiten und wie sie Entscheidungen unter Risiko treffen. Das Ziel der vorliegenden Arbeit war es daher, die Mechanismen von Risikoverarbeitung und Entscheidungen unter Risiko näher zu untersuchen. Hierzu wurde zunächst eine Meta-Analyse von fMRT-Studien zur Risikoverarbeitung (Projekt I) durchgeführt. Ziel der Studie war es herauszufinden, wie die neurale Verarbeitung von Risiko durch Emotionen, potentielle Verluste und Kontext beeinflusst wird. Die gefundenen Aktivationen in der anterioren Insula, einer Hirnregion, die mit der Verarbeitung aversiver Emotionen wie Furcht, Enttäuschung oder Bedauern assoziiert wird, deuten darauf hin, dass Risikoverarbeitung von Emotionen beeinflusst wird. Die anteriore Insula war allerdings vorwiegend aktiv, wenn Verluste möglich waren. Dies deutet darauf hin, dass Verluste einen Einfluss auf die Art der Risikoverarbeitung haben. Weiterhin waren der dorsolaterale präfrontale Kortex und der parietale Kortex nur im Kontext von in Entscheidungssituationen aktiv, nicht aber in Situationen, in denen keine Entscheidung zu treffen war, was nahe legt, dass Risikoverarbeitung kontextabhängig ist. In den meisten Entscheidungen unter Risiko, wie beispielweise beim Roulette, sind alle möglichen Konsequenzen und zugehörigen Wahrscheinlichkeiten bekannt. Bei Anlageentscheidungen unterliegen die möglichen Renditen von Wertpapieren allerdings in der Regel stetigen Verteilungen. Es ist daher nahezu unmöglich alle möglichen Renditen und Wahrscheinlichkeiten in die Bewertung des Wertpapiers einzubeziehen. Unklar ist daher wie Menschen Anlageentscheidungen treffen. Die Ergebnisse aus Projekt II zeigen, dass Risiko-Gewinn Modelle Entscheidungen auf Verhaltensebene gut erklären können und dass die Komponenten dieser Modelle (Wert, Risiko und Risikoeinstellung) während einer Entscheidung im Gehirn repräsentiert werden. Insbesondere die Repräsentation des Risikos in der anterioren Insula spricht eher für Risiko-Gewinn Modelle als für nutzenbasierte Modelle, da Risiko eine explizite Komponente von Risiko-Gewinn Modellen, nicht aber von nutzenbasierten Modellen ist. In Projekt III werden mithilfe einer Literaturanalyse das dopaminerge und das serotoninerge Hirnsystem als wichtige Neurotransmittersysteme im Kontext ökonomischer Entscheidungen identifiziert. Beide sind bekannt dafür, sich im Verlauf der Lebensspanne Veränderungen zu unterziehen. Ebenso ändert sich das ökonomische Verhalten während der Lebenszeit. Zusammengenommen deuten diese Erkenntnisse auf einen triadischen Zusammenhang zwischen (a) ökonomischen Entscheidungen, (b) dopaminerger und serotoninerger Neuromodulation und (c) Altern hin.