Flexibility and Adaptivity of Emotion Regulation: From Contextual Dynamics to Learning and Control

To effectively regulate their emotions, people have to continually adjust their emotion regulation strategies to changes in internal and external demands. Flexibility and adaptivity are thus vital to emotion regulation. Flexibility refers to the context-sensitive deployment of emotion regulation strategies while regulating one's own emotions. By contrast, adaptivity refers to the changes in such context-sensitive deployment of strategies that take place while regulating one's own emotions over time, and the control of such change processes. Flexibility is increased by having larger repertoire of strategies as this increases the odds that an appropriate strategy is available. On the other hand, having more emotion regulation strategies to choose from creates the need for decision. Because this decision-making process occurs in real time, it requires emotional stability and cognitive analysis. Over time, different experiences in choosing emotion regulation strategies give rise to learning which is one form of adaptivity. Flexibility in emotion regulation is provoked by the fluctuating contexts, whereas adaptations are induced by the frequency and intensity of emotion-regulatory activities. These adaptations are grounded in changes at a cellular and molecular level. The latter adaptations are often referred to by the term plasticity, or first-order adaptation. Often some form of control is applied to such adaptation processes, determining when and under which circumstances the adaptations should take place; this is often referred to by the term meta-plasticity or second-order adaptation. The above concepts are illustrated by simulated example scenarios based on different computational network models. In the first simulated scenario, a varying context shows the flexibility in the choice of emotion regulation strategies. In the second and third scenario, plasticity and metaplasticity are illustrated based on first- and second-order adaptive network models.