Intra and interindividual Connectivity Analyses (IICA) A neuropsychological method to improve creativity in teams

Abstract

Creativity is usually conceived as generating ideas or problem solutions that are both novel and potentially useful. In particular, if new teams are formed, e.g., in a company, they would benefit if (a) each team member can apply as many of his/her strengths as possible in the company and (b) the team members can cooperate and combine as many of their strengths as possible. Discovering such human resource potential in a company is a crucial creative task. Several neuroimaging studies have demonstrated that divergent thinking is associated with higher functional connectivity between different brain regions. However, psychological methods to improve awareness and regulation of neurocognitive processes involved in creativity (i.e., metacognition of creativity) and thereby boost such functional connections remain elusive.Based on neuropsychological findings from creativity research, we propose a metacognitive training for promoting creativity called intra- and interindividual connectivity analysis (IICA).In a role-play, participants were divided into two groups. Each group was tasked with finding out how to apply their strengths beneficially in a company (for example, a psychiatric clinic). The fluency and originality of their ideas were measured as dependent variables of creativity. In the experimental group, the proposed intra- and interindividual connectivity analysis (IICA) method was carried out, while the control group was supposed to solve the task using the common brainstorming method. The results show that IICA led to significantly higher fluency and originality rates than the control group. These data suggest that IICA can be a promising metacognitive method to promote team creativity and cooperation potential. Future studies should validate the effect of this method in further companies and investigate the neurobiological foundations of our approach using neurophysiological methods and computational modeling.