| Abstract |
Risk-taking behavior in traffic, such as speeding and drunk driving are major causes of accidents and often involve harmful consequences to the people's lives. Past research has shown that risk-taking behavior in traffic can be predicted by situational factors as well as personality measurements. However, the cognitive processes underlying risk-taking behavior in traffic are largely unexplored. To fill this gap, in my Ph.D. project, I aim to test laboratory tasks and models of risk-taking behavior to allow better prediction of why and when people exhibit risky behavior in traffic situations.
Therefore, the presented project aims to elucidate the cognitive components of traffic risk-related decision-making processes. For this purpose, my Ph.D. project will consist of three experimental subprojects. I will test in subproject A whether risk-taking in traffic can be predicted by measuring people's risk-taking behavior in a laboratory setting. To predict real-world risky behavior I will develop and test computational models of the decision-making processes that underlie behavior in the laboratory risk-tasks. I will then correlate the estimated model parameters with real-world risk-taking behavior. In addition to providing important knowledge about the external validity of the laboratory tasks, novel insights will be gained on the cognitive components of the involved decision-making processes. In subproject B, the most suitable laboratory task, determined in subproject A and the corresponding computational models will be adapted and used in a functional magnetic resonance imagining study to explore the neurological underpinning of risk-taking. This subproject will inform whether the hypothesized computational models are plausible from a biological point of view. In subproject C, I will examine to what extend the previous findings can be used to develop a new task that successfully allows discriminating between people that have a high propensity for speeding in traffic compared to risk-averse persons.
In summary, I will therefore first examine the external validity of laboratory risk-tasks, explore the cognitive components underlying behavior in these tasks, then test the biological plausibility of the computational models describing the cognitive components and finally I will put the acquired knowledge to the “reality-check” of discriminating real-world risk takers.
I strongly believe that the knowledge gained throughout the course of the project will be of great interest for decision-scientists of all fields, neuroscientist but also for society at large, as additional knowledge about the cognitive processes that underlie risk-taking in traffic situations may have influence in traffic-security campaigns.
Risk-taking behavior in traffic, such as speeding and dangerous street crossing are major causes of accidents and often involve harmful consequences to the people's lives. Past research has shown that risk-taking behavior in traffic can be predicted by situational factors as well as personality measurements. However, the cognitive processes underlying risk-taking behavior in traffic are largely unexplored. To fill this gap, we test laboratory tasks and models of risk-taking behavior to allow better prediction of why and when people exhibit risky behavior in traffic situations. For this, the project will consist of three experimental subprojects. I will test in subproject A whether risk-taking in traffic can be predicted by measuring people's risk-taking behavior in a laboratory setting. To predict real-world risky behavior I will develop and test computational models of the decision-making processes that underlie behavior in the laboratory risk-tasks. I will then correlate the estimated model parameters with real-world risk-taking behavior. In addition to providing important knowledge about the external validity of the laboratory tasks, novel insights will be gained on the cognitive components of the involved decision-making processes. In subproject B, we further investigate the underlying cognitive processes in the task using different methods and techniques. In subproject C, we examine to what extend the previous findings can be used to develop a new task that successfully allows discriminating between people that have a high propensity for real-life risk-taking to risk-averse persons. In summary, I will therefore first examine the external validity of laboratory risk-tasks, explore the cognitive components underlying behavior in these tasks, finally, I put the acquired knowledge to the “reality-check” of discriminating real-world risk takers. I strongly believe that the knowledge gained in this project will be of great interest for decision-scientists but also for society at large, as additional knowledge about the cognitive processes that underlie risk-taking in traffic situations may have influence in traffic-security campaigns.
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