Functional Cortical networks associated with personality, emotional intelligence and decision making

Background: Many studies have investigated the neural correlates associated with personality by using a variety of neuroimaging techniques and psychometrics (Kennis et al., 2013). Previous electroencephalogram (EEG) studies have examined the relationship between emotional traits (Stenberg 1995) and personality traits (Takahashi et al 2005). In this EEG based pilot study, a multidisciplinary strategy of combining brain imaging techniques (LORETA) and EEG coherence analysis with psychological constructs was applied to explore the relationships between personality traits. In particular, the relationship between emotional intelligence with participants’ reactions and decision making to rich complex stimuli such as advertising videos was examined. Methods: A preliminary study group of 45 participants, (Mean age 30.8 years SD 11.9 years, Education = 14.8 years SD 1.5 years ) had their EEG recorded with a Quikcap electrode system while watching a series of videos which were selected based on various marketing categories such as, food & drink, community interests, celebrities and social issues. Using the Swinburne University Emotional Intelligence Test or SUEIT (Stough, Palmer 2001), individuals were categorized into two groupings; those who scored high and low in emotional intelligence (EI). EEG was analyzed offline to remove muscle and ocular artifact. Results: Selected epochs of EEG were then analyzed to produce LORETA and EEG coherence data during decision making. Preliminary findings suggest that participants with high or low emotional intelligence (EI) were receptive to certain types of social issues or visual imagery. This was validated by the associated brain functional connectivity networks for each group. Also, correlations with other personality scales (NEO-PI Costa & McCrae 1992) demonstrated personality types react differently to imagery, content and humor. Discussion/Conclusion: Both LORETA and EEG coherence data demonstrated distinct functional connectivity associated with each group and may help to explain their associated perceptions and behaviors. More specifically, the high EI group demonstrated clear preferences in specific video material when contrasted with the low EI group. The NEO-PI has been used in previous psychophysiology studies of emotional arousability but not with complex social stimuli. The use of a multidisciplinary approach can assist in the identification of the neural networks associated with personality and provide insights into individual differences in perception