Altaf Hussain Abro

A Computational Model of the Relation between Regulation of Negative Emotions and Mood

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.

An Agent-Based Model for the Role of Social Support in Mood Regulation

In this paper, a computational model of a human agent is presented which describes the effect of social support on mood. According to the literature, social support can either refer to the social resources that individuals perceive to be available or to the support that is actually provided in problematic situations. The proposed model distinguishes between both roles of social support. Simulation experiments are done to analyze the effect of the different types of support in different scenarios. It is shown that support can help to reduce the induced stress and thus can contribute to healthy mood regulation and prevention of depression. This presented model provides a basis for an intelligent support system for people with mood regulation problems that take the social network of people into account.

A Computational Cognitive Model Integrating Different Emotion Regulation Strategies

In 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.

Doubting What to Eat: A Computational Model for Food Choice Using Different Valuing Perspectives

In this paper a computational model for the decision making process of food choices is presented that takes into account a number of aspects on which a decision can be based, for example, a temptation triggered by the food itself, a desire for food triggered by being hungry, valuing by the expected basic satisfaction feeling, and valuing by the expected goal satisfaction feeling.

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.

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.

A Cognitive Agent Model for Desire Regulation Applied to Food Desires.

In this paper a neurologically inspired cognitive agent model for desire regulation is presented that describes the desire generation process and a number of desire regulation strategies. This work addresses antecedent-focused desire regulation strategies. These strategies include reinterpretation, attention deployment and situation modification. The model has been used to perform a number of simulation experiments concerning food desire and eating behaviour.

With a little help from my friends: A computational model for the role of social support in mood regulation

The growing interest in the role of social support in mental and physical health has led to the development of several intelligent systems that aim to use social mechanisms to simulate healthy behaviour. In this paper a computational model of a human agent is presented which describes the effect of social support on mood. According to the literature, social support can either refer to the social resources that individuals perceive to be available or to the support that is actually provided in problematic situations. The proposed model distinguishes between both roles of social support. The role of social network characteristics has been taken into account, as an individual can perceive or receive social support through his/her social network. In addition, the number of connections (friends), strength of ties (relationships), social isolation and social integration have been studied. Simulation experiments have been done to analyze the effect of the different types of support in different scenarios and also to analyze the role of various social network characteristics on the mood level. It is shown that support can help to reduce the induced stress and thus can contribute to healthy mood regulation and prevention of depression. The presented model provides a basis for an intelligent support system for people with mood regulation problems that take the social network of people into account.

A Computational Cognitive Model of Self-monitoring and Decision Making for Desire Regulation

Desire regulation can make use of different regulation strategies; this implies an underlying decision making process, which makes use of some form of self-monitoring. The aim of this work is to develop a neurologically inspired computational cognitive model of desire regulation and these underlying self-monitoring and decision making processes. In this model four desire regulation strategies have been incorporated. Simulation experiments have been performed based for the domain of food choice.