Mood and Risk-Taking as Momentum for Creativity

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Creativity Research

Background:

• Understanding the interplay of internal states and decision-making is crucial for creativity.
• Existing research often examines mood or risk-taking in isolation, with limited computational approaches.
• The representational change theory offers a framework for understanding insight and potentially broader creative processes.

Purpose of the Study:

• To investigate the independent and combined effects of mood and risk-taking on divergent and convergent thinking.
• To apply a Q-learning computational model to quantify the influence of these factors on creative processes.
• To explore the relevance of representational change theory to creative thinking.

Main Methods:

• Utilized a Q-learning computational model to simulate decision-making under varying mood and risk-taking conditions.
• Empirically assessed divergent and convergent thinking performance.
• Analyzed the model's parameters to distinguish the roles of mood and risk-taking in updating values and decision-making.

Main Results:

• Mood did not show a significant direct relationship with either divergent or convergent thinking.
• Risk-taking positively influenced divergent thinking, particularly when associated with negative rewards.
• The findings align with the principles of representational change theory, suggesting its applicability to creative thinking.

Conclusions:

• Risk-taking is a significant factor in enhancing divergent thinking, especially in challenging reward scenarios.
• Mood's direct impact on creative thinking types may be less pronounced than previously assumed.
• The computational Q-learning model provides novel insights into the distinct roles of psychological factors in creativity.