Sporrer, Juliana Katharina; (2025) The Cognitive and Computational Mechanisms of Risk-taking Behavior. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Every day, individuals make risky decisions—whether ignoring a chest pain, investing in a friend’s venture, or stepping outside during a storm. These decisions raise important questions: How do individuals behave under risk? How much risk are they willing to accept? And what factors drive their choices? In this thesis, I investigate the cognitive and computational characteristics of risk-taking behavior using increasingly complex and naturalistic paradigms, focusing on how internal traits and external contexts modulate decisions. In the first study (Chapter 2), I demonstrate that risk aversion is enhanced in anxious depressed patients after a worry induction, compared to baseline or depressed patients. This finding underscores the transient nature of risk preferences in generalized anxiety and suggests that these preferences may be driven by anxiety symptoms rather than causing them. The second study (Chapter 3), conducted with large online samples, reveals that transdiagnostic compulsivity is the primary predictor of cautious behavior in an approach-avoidance conflict task set in a risky foraging scenario. This challenges traditional views on these tasks, which have been extensively used to assess the effects of anxiolytic agents. The final study (Chapter 4) utilizes fully immersive virtual reality to examine naturalistic escape decisions under risk of predation by bio-realistic threats. The results show that escape decisions can be dynamically updated, depend on personal and threat characteristics; and are implemented to optimize secondary goals. I also demonstrate that different types of threat-related behavior rely on distinct computational mechanisms. These findings indicate that escape decisions are not instinctive but depend on flexible computational mechanisms that integrate both internal and external factors. Together, these studies converge to highlight the complexity and flexibility of human risk-taking behavior. By bridging clinical, computational, and ecologically valid approaches, this thesis advances our mechanistic understanding of how humans navigate risk and offers new insights into the cognitive processes that underpin these decisions.

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