A Computational Cognitive Model Integrating Different Emotion Regulation Strategies

AbstractIn this paper a cognitive model is introduced which integrates a model for emotion generation with models for three different emotion regulation strategies. Given a stressful situation, humans often apply multiple emotion regulation strategies. The presented computational model has been designed based on principles from recent neurological theories based on brain imaging, and psychological and emotion regulation theories. More specifically, the model involves emotion generation and integrates models for the emotion regulation strategies reappraisal, expressive suppression, and situation modification. The model was designed as a dynamical system. Simulation experiments are reported showing the role of the emotion regulation strategies. The simulation results show how a potential stressful situation in principle could lead to emotional strain and how this can be avoided by applying the emotion regulation strategies decreasing the stressful effects

Validation of a Computational Model for Mood and Social Integration

The social environment of people is an important factor for the mental health. However, in many internet interventions for mental health the interaction with the environment has no explicit role. It is known that the social environment can help people to reduce the feelings of loneliness and has a positive impact on mood in particular. Participation in social activities and maintaining social interaction with friends and relatives are frequently seen as indicators of a happy and healthy life. It is also commonly accepted that being integrated in social network has a strong protective effect on health and helps to avoid feelings of loneliness. In this paper we present a computational model that can be used for analyzing and predicating the mood level of individuals by taking into account the social integration, the participation in social activities and the enjoyableness of those activities. In addition to this, we explain the method that we developed to validate the computational model. For the validation, we use real EMA data that was collected from E-COMPARED project. This model allows to make more precise predictions on the effect of social interaction on mood and might be part of future internet interventions

Monitoring the Impact of Negative Events and Deciding about Emotion Regulation Strategies

Humans have a number of emotion regulation strategies at their disposal, from which in a particular situation one or more can be chosen. The focus of this paper is on the processes behind the choice of these regulation strategies. The paper presents a neurologically inspired cognitive computational model of a monitoring and decision mechanism for emotion regulation incorporating different strategies (expressive suppression, reappraisal or reinterpretation, and situation modification). It can be tuned to specific characteristics of persons and events. This paper won the Best Paper Award at the EUMAS'16 conference

Fotografia B127

In this paper a computational model is presented that describes the role of emotion regulation to reduce the influences of negative events on long term mood. The model incorporates an earlier model of mood dynamics and a model for the dynamics of emotion generation and regulation. Example model simulations are described that illustrate how adequate emotion regulation skills can prevent that a depression is developed

Modeling the effect of regulation of negative emotions on mood

In this paper a computational model is presented that describes the role of emotion regulation to reduce the influences of negative events on mood. Emotion regulation is a process based on a set of regulatory strategies used by persons to down-regulate their negative emotions or to up-regulate their positive emotions. For a given situation, the selection of specific regulation strategies is dependent on that particular situation. The current paper presents work focusing on a cognitive reappraisal (re-interpretation) strategy, that involves changing the way one interprets a stimulus or situation, or alter the semantic representation of an emotional stimulus in order to reduce the influence of such stimuli. The model incorporates an earlier model of mood dynamics and a model for the dynamics of emotion generation and regulation. Example model simulations are described that illustrate how adequately emotion regulation skills can avoid or delay development of a depression. The presented computational analysis shows how regulation of stressful emotions helps unstable persons to avoid a depression, and to postpone it in very unstable persons. Furthermore, the analysis shows that if a stressful event persists for a longer time period, then emotion regulation can also help an unstable person to prevent the mood level from becoming too low, for a certain time