The Interplay of Emotion Regulation and Mental Health : An Adaptive Dynamical Systems Analysis Approach

For many disorders, proper emotion regulation is a crucial protective factor. Therefore, this volume provides an in-depth exploration of emotion regulation through the lens of computational modeling and analysis. Emotion regulation is a critical psychological process that enables individuals to manage their emotional responses in diverse situations. This book examines how flexibility in emotion regulation strategies such as suppression, cognitive reappraisal, and attention modulation supports mental well-being by allowing individuals to adapt their emotional responses according to context. Drawing from recent advances in psychology and neurobiology, the book presents a series of models that highlight the role of contextual sensitivity and the influence of factors such as age, gender, and personality traits on regulatory preferences. One of the themes of this book is the contextual nature of emotion regulation, where flexibility enables instant contextual shifts between different strategies based on situational demands. Several models simulate scenarios in which specific emotions, such as anger or anxiety, are context-sensitive according to the individual’s goals and context. Simulation results reveal how certain emotions may strongly depend on achieving personal or external goals, underscoring the dynamic nature of emotion regulation. In addition, another important theme is adaptivity of emotion regulation. This does not concern instant contextual changes but longer-term changes in response to experiences over time. Learning processes to strengthen emotion regulation over time may be very important to reduce negative health effects induced by reduced emotion capabilities. These forms of adaptivity are also extensively addressed in this book. In addition to general mechanisms of emotion regulation, this book also addresses specialized applications for various psychological and behavioral conditions. These include computational models for conditions like anxiety, autism spectrum disorders, social media addiction, burnout, and eating disorders. 

For many disorders, proper emotion regulation is a crucial protective factor. Therefore, this volume provides an in-depth exploration of emotion regulation through the lens of computational modeling and analysis. Emotion regulation is a critical psychological process that enables individuals to manage their emotional responses in diverse situations. This book examines how flexibility in emotion regulation strategies such as suppression, cognitive reappraisal, and attention modulation supports mental well-being by allowing individuals to adapt their emotional responses according to context. Drawing from recent advances in psychology and neurobiology, the book presents a series of models that highlight the role of contextual sensitivity and the influence of factors such as age, gender, and personality traits on regulatory preferences. One of the themes of this book is the contextual nature of emotion regulation, where flexibility enables instant contextual shifts between different strategies based on situational demands. Several models simulate scenarios in which specific emotions, such as anger or anxiety, are context-sensitive according to the individual’s goals and context. Simulation results reveal how certain emotions may strongly depend on achieving personal or external goals, underscoring the dynamic nature of emotion regulation. In addition, another important theme is adaptivity of emotion regulation. This does not concern instant contextual changes but longer-term changes in response to experiences over time. Learning processes to strengthen emotion regulation over time may be very important to reduce negative health effects induced by reduced emotion capabilities. These forms of adaptivity are also extensively addressed in this book. In addition to general mechanisms of emotion regulation, this book also addresses specialized applications for various psychological and behavioral conditions. These include computational models for conditions like anxiety, autism spectrum disorders, social media addiction, burnout, and eating disorders.