Taking are affected by personality

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Abstract

Classic game theory predicts that individuals should behave as rational agents in order to maximize their gain. In real life situations it is observed that human decision making does not follow this theory. Specific patterns of activity in several brain circuits identified in recent years have been associated with irrational and imperfect decision making. Brain activity modulated by dopamine and serotonin is assumed to be among the main drivers of the expression of personality traits and patients affected by Attention deficit hyperactivity disorder (ADHD) are characterized by altered activity in those neuromodulating circuits. We investigated the effect of fairness and personality traits on neuronal and psychological mechanisms of decision making and risk taking in two sets of experiments based on the Ultimatum Game (UG) and the Investment Game (IG). In the UG we found that Fairness and Conscientiousness were associated with responder’s gain and with event-related potentials (ERP) components Feedback-Related Negativity (FRN) and Late Positive component (LPP). In the IG the sum gained during the risky gambling task were presented immediately after half of the trials (condition “high frequency feedback”, HFFB), while the other half were presented at the end of each block (condition “low frequency feedback”, LFFB). Conscientiousness, Agreeableness and Sincerity influenced latencies of the negative deflection occurring at around 200ms (N200) and the positive wave peaking at around 250ms (P250) components. The contingent negative variation (CNV) component was affected in a different way in controls and participants with ADHD as a function of the feedback frequency (HFFB versus LFFB). These results clearly show that imperfect decision making and risk taking are affected by personality traits and cannot be accounted by models based on rational computations.

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Mesrobian, S. K., Bader, M., Götte, L., Villa, A. E. P., & Lintas, A. (2015). Taking are affected by personality. Studies in Computational Intelligence, 538, 145–184. https://doi.org/10.1007/978-3-319-15144-1_6

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