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

Mohr, Peter N. C.; Biele, Guido; Krugel, Lea K.; Li, Shu-Chen; Heekeren, Hauke R.

doi:10.1016/j.neuroimage.2009.10.060

Cited by 60 publications

(64 citation statements)

References 41 publications

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“…Both brain regions were usually observed in the context of decision making (for review, see Heekeren et al, 2008). In the context of reward-based decision making, activity in the right DLPFC is related to valuing choice options (Camus et al, 2009;Mohr et al, 2010a). These results indicate that DLPFC and parietal cortex do not play a general role in risk processing but a specific role in risk processing during decision making.…”

Section: Discussionmentioning

confidence: 99%

Neural Processing of Risk

Mohr¹,

Biele²,

Heekeren³

2010

J. Neurosci.

373

302

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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.

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Section: Discussionmentioning

confidence: 99%

Neural Processing of Risk

Mohr¹,

Biele²,

Heekeren³

2010

J. Neurosci.

373

302

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show abstract

“…In the context of risk processing, thalamic and insular contributions were reported during intertemporal choices involving losses which were associated with accompanying negative emotions (Xu et al, 2009). Thalamus and insula were also found to be involved in risky decisions and in anticipating risk (Huettel, 2006;Mohr et al, 2010aMohr et al, , 2010b). We here propose medial thalamus and anterior insula to be involved in the mediation of bodily interoceptive signals for evaluation purposes in response to the faced hazard.…”

Section: Anatomical and Functional Features Of The Brain Regions Invomentioning

confidence: 99%

Neural correlates of reward processing in schizophrenia — Relationship to apathy and depression

Simon

Biller

Walther

et al. 2010

Schizophrenia Research

207

179

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The present study employs a new framework to categorise the heterogeneous findings on the relationship between impaired reward processing and negative and affective symptoms of schizophrenia. Based on previous behavioural and neuroimaging studies we postulate that "wanting" (i.e. anticipation) of a reward is specifically related to apathy, whereas "liking" (i.e. hedonic impact) is related to anhedonia and depression-symptoms commonly observed in schizophrenia. Fifteen patients with schizophrenia or schizoaffective disorder treated with atypical antipsychotic drugs and fifteen healthy controls performed a probabilistic monetary incentive delay task while undergoing functional magnetic resonance imaging. At the group level we found no significant differences between patients and controls in neural activation during anticipation or receipt of a reward. However, in patients with schizophrenia specific relationships between ventral-striatal activation and symptoms were observed. Ventral-striatal activation during reward anticipation was negatively correlated with apathy, while activation during receipt of reward was negatively correlated with severity of depressive symptoms. These results suggest that the link between negative symptoms and reward anticipation might specifically relate to apathy, i.e. a lack of motivation and drive. Impaired hedonic reward processing might contribute to the development of depressive symptoms in patients with schizophrenia, but it is not directly associated with self-rated anhedonia. These results indicate the necessity of more specifically differentiating negative and affective symptoms in schizophrenia in order to understand the role of the reward system in their pathogenesis.

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“…In other words, the spatial factors of the fMRI data are different. In particular, the 5 active regions are generated with varying size to reflect a more realistic situation in accordance with the study of Mohr, Biele, Krugel, Li and Heekeren (2010 We implement the 3D Image FPCA and 1D FPCA to the generated fMRI data. As the average number of voxels in each simulated active region is about eight times the number of previous simulation, the active regions are determined as the voxels over Table 5.…”

Section: Robust Testingmentioning

confidence: 99%

“…It is important to understand which parts of the human brain regulate specific decision-making tasks and which neural processes drive investment decisions (see Rangel, Camerer and Montague, 2008;Mohr, Biele, Krugel, Li and Heekeren, 2010). Among others, neuroeconomics is the science that investigates such relationships and has identified decision-making-related brain regions.…”

Section: Introductionmentioning

confidence: 99%

Risk Related Brain Regions Detected with 3D Image FPCA

Chen,

He,

Majer

2015

SSRN Journal

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Risk attitude and perception is reflected in brain reactions during RPID experiments. Given the fMRI data, an important research question is how to detect risk related regions and to investigate the relation between risk preferences and brain activity. Conventional methods are often insensitive to or misrepresent the original spatial patterns and interdependence of the fMRI data. In order to cope with this fact we propose a 3D Image Functional Principal Component Analysis (3D Image FPCA) method that directly converts the brain signals to fundamental spatial common factors and subject-specific temporal factor loadings via proper orthogonal decomposition. Simulation study and real data analysis show that the 3D Image FPCA method improves the quality of spatial representations and guarantees the contiguity of risk related regions. The selected regions provide signature scores and carry explanatory power for subjects' risk attitudes. For in-sample analysis, the 3D Image method perfectly classifies both strongly and weakly risk averse subjects. In out-of-sample, it achieves 73-88% overall accuracy, with 90-100% rate for strongly risk averse subjects, and 49-71% for weakly risk averse subjects.

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

Cited by 60 publications

References 41 publications

Neural Processing of Risk

Neural Processing of Risk

Neural correlates of reward processing in schizophrenia — Relationship to apathy and depression

Risk Related Brain Regions Detected with 3D Image FPCA

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