The cognitive emotion process

Different theories of emotions have been introduced since the 19th century. Even though a large number of apparent differences between these theories exist, there is a broad consensus today that emotions consist of multiple components such as cognition, physiology, motivation, and subjectively perceived feeling. Appraisal theories of emotions, such as the Component Process Model (CPM) by Klaus Scherer, emphasize that the cognitive evaluation of a stimulus or event is the driving component of the emotion process. It is believed to cause changes in all other components and hence to differentiate emotion states. To test the CPM and gain more insights into the multi-componential emotion process, the present thesis examines two emotion sub-processes – the link between the cognitive and the feeling component (study 1) and the link between the cognitive and the physiological component (study 2) – by using different predictive modeling approaches. In study 1, four theoretically informed models were implemented. The models use a weighted distance metric based on an emotion prototype approach to predict the perceived emotion of participants from self-reported cognitive appraisals. Moreover, they incorporate different weighting functions with weighting parameters that were either derived from theory or estimated from empirical data. The results substantiate the examined link based on the predictive performance of the models. In line with the CPM, the preferred model weighted the appraisal evaluations differently in the distance metric. However, the data-derived weighting parameters of this model deviate from theoretically proposed ones. Study 2 analyzed the link between cognition and physiology by predicting self-reported appraisal dimensions from a large set of physiological features (calculated from different physiological responses to emotional videos) using different linear and non-linear machine learning algorithms. Based on the predictive performance of the models, the study is able to confirm that most cognitive evaluations were interlinked with different physiological responses. The comparison of the different algorithms and the application of methods for interpretable machine learning showed that the relation between these two components is best represented by a non-linear model and that the studied link seems to vary among physiological signals and cognitive dimensions. Both studies substantiate the assumption that the cognitive appraisal process is interlinked with physiology and subjective feelings, accentuating the relevance of cognition in emotion as assumed in appraisal theory. They also demonstrate how computational emotion modeling can be applied in basic research on emotions

The cognitive emotion process

Different theories of emotions have been introduced since the 19th century. Even though a large number of apparent differences between these theories exist, there is a broad consensus today that emotions consist of multiple components such as cognition, physiology, motivation, and subjectively perceived feeling. Appraisal theories of emotions, such as the Component Process Model (CPM) by Klaus Scherer, emphasize that the cognitive evaluation of a stimulus or event is the driving component of the emotion process. It is believed to cause changes in all other components and hence to differentiate emotion states. To test the CPM and gain more insights into the multi-componential emotion process, the present thesis examines two emotion sub-processes – the link between the cognitive and the feeling component (study 1) and the link between the cognitive and the physiological component (study 2) – by using different predictive modeling approaches. In study 1, four theoretically informed models were implemented. The models use a weighted distance metric based on an emotion prototype approach to predict the perceived emotion of participants from self-reported cognitive appraisals. Moreover, they incorporate different weighting functions with weighting parameters that were either derived from theory or estimated from empirical data. The results substantiate the examined link based on the predictive performance of the models. In line with the CPM, the preferred model weighted the appraisal evaluations differently in the distance metric. However, the data-derived weighting parameters of this model deviate from theoretically proposed ones. Study 2 analyzed the link between cognition and physiology by predicting self-reported appraisal dimensions from a large set of physiological features (calculated from different physiological responses to emotional videos) using different linear and non-linear machine learning algorithms. Based on the predictive performance of the models, the study is able to confirm that most cognitive evaluations were interlinked with different physiological responses. The comparison of the different algorithms and the application of methods for interpretable machine learning showed that the relation between these two components is best represented by a non-linear model and that the studied link seems to vary among physiological signals and cognitive dimensions. Both studies substantiate the assumption that the cognitive appraisal process is interlinked with physiology and subjective feelings, accentuating the relevance of cognition in emotion as assumed in appraisal theory. They also demonstrate how computational emotion modeling can be applied in basic research on emotions

Cathexis--a computational model for the generation of emotions and their influence in the behavior of autonomous agents

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 93-98).by Juan David Velásquez.M.S

On the Possibility of Robots Having Emotions

I argue against the commonly held intuition that robots and virtual agents will never have emotions by contending robots can have emotions in a sense that is functionally similar to humans, even if the robots\u27 emotions are not exactly equivalent to those of humans. To establish a foundation for assessing the robots\u27 emotional capacities, I first define what emotions are by characterizing the components of emotion consistent across emotion theories. Second, I dissect the affective-cognitive architecture of MIT\u27s Kismet and Leonardo, two robots explicitly designed to express emotions and to interact with humans, in order to explore whether they have emotions. I argue that, although Kismet and Leonardo lack the subjective feelings component of emotion, they are capable of having emotions

A Comparative Framework for Emotion Driven Agent Based Modelling

Emotions are an integral part of human decision making so it is important to integrate emotions into artificial agents to make them more realistic. In this thesis we intend to design and implement an artificial emotional response agent simulation using three psychological models for emotions and develop a corresponding algorithm for each depicting its process in order to find a suitable algorithm. After comparing the performance of the three algorithms we use Ortony, Clore and Collins(OCC) theory to generate emotions in a case study of a basic Hospital Simulation System. In this, there are patient and nurse agents who trigger emotions due to interaction with each other. Results show that OCC algorithm is advantageous when specific emotion has to be generated and is more accurate than other algorithms. Also from the experiments performed for the case study show that an increase in emotional stress leads to higher error rates in nurse task performance when their logical performance is compared with emotional performance

Affect and Learning: a computational analysis

In this thesis we have studied the influence of emotion on learning. We have used computational modelling techniques to do so, more specifically, the reinforcement learning paradigm. Emotion is modelled as artificial affect, a measure that denotes the positiveness versus negativeness of a situation to an artificial agent in a reinforcement learning setting. We have done a range of different experiments to study the effect of affect on learning, including the effect on learning if affect is used to control the exploration behaviour of the agent and the effect on learning when affect is communicated by a human (though real-time analysis of that human__s facial expressions) to a simulated robot. We conclude that affect is a useful concept to consider in adaptive agents that learn based on reinforcement learning and that in some cases affect can indeed help the learning process. Further, affective modelling in this way can help understand the psychological processes that underlie influences of affect on cognition. Finally, we have developed a formal notation for a specific type of emotion theory, i.e., cognitive appraisal theory.UBL - phd migration 201

Emotions, behaviour and belief regulation in an intelligent guide with attitude

Abstract unavailable please refer to PD

Designing and Testing an Experimental Framework of Affective Intelligent Agents in Healthcare Training Simulations

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyThe purpose of this study is to investigate how emotionally enabled virtual agents (VAs) in healthcare provision training simulations allow for a more effective level of understanding on how an emotionally enhanced scenario can affect different aspects of learning. This is achieved by developing virtual agents that respond to the user’s emotions and personality. The developed system also provides visual and auditory representations of the virtual agents’ state of mind. To enable the fulfilment of this purpose an experimental framework for incorporating emotional enhancements (concentrating on negative emotions such as stress, fear, and anxiety) into virtual agents in virtual training applications for healthcare provision is designed and implemented. The framework for incorporating emotional enhancements is designed based on previous research, on psychological theories (with input by experienced psychologists) and from input of experts in the area of healthcare provision. For testing the framework and answering the research question of this thesis the researcher conducted nine case studies. The participants were nursing students in the area of healthcare provision, and more specifically in the area of mental health, specialising in caring for patients with dementia. The results of the study showed that the framework and its implementation succeeded in providing a realistic learning experience, stimulated a better set of responses from the user, improved their level of understanding on how an emotionally enhanced scenario can affect the learning experience and helped them become more empathetic towards the person they cared for