A neurocomputational perspective on behavioral economics: A study of emotional processes.
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
In this study we have used a neural network model in order to investigate the impact of emotional processes on economic phenomena. We have built a replica of a model presented by Frank and Claus (2006, Anatomy of a Decision: Striato-Orbitofrontal Interactions in Reinforcement Learning, Decision Making, and Reversal, Psychological Review), although using other software. The model displays, similar to the one referred to above, the characteristics that constitute prospect theory. Additionally, we take a first step toward investigating what explanatory power this model might have in studying the influence of emotions on economic decision-making. We note that by externally altering the activity level in the amygdala, a brain region that has been proven essential for emotional reactions, the risk attitudes of the model can be manipulated. We find that a decrease in activity in the amygdala implies a lower degree of risk-averse, as well as a higher degree or risk-seeking, behavior. Finally, we conclude that response times are longer and choice uncertainty higher for tasks that involve only negative outcomes as compared to tasks that involve partially or exclusively positive outcomes, a result that can be linked to e.g. decision field theory.
Place, publisher, year, edition, pages
2011. , 56 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-59703OAI: oai:DiVA.org:kth-59703DiVA: diva2:476088