This paper focuses on how capabilities to interpret another agent’s emotions, and their biological realisation can be modelled. First a cognitive and a biological agent model to generate emotional states are introduced. These models involve the generation and sensing of body states. Next it is shown how emotion reading can be modelled both at a cognitive and at a biological level, following the Simulation Theory approach to mindreading. Furthermore, a cognitive Theory Theory model for emotion reading is presented. Finally, it is shown how the cognitive and biological agent models can be related. 1.

Abstract. By adopting neurological theories on the role of emotions and feelings, an agent model is introduced incorporating the reciprocal interaction between believing and feeling. The model describes how the strength of a belief may not only depend on information obtained, but also on the emotional responses on the belief. For feeling emotions a recursive body loop is assumed. The model introduces a second feedback loop for the interaction between feeling and belief. The strength of a belief and of the feeling both result from the converging dynamic pattern modelled by the combination of the two loops. For some specific cases it is described, for example, how for certain personal characteristics an optimistic world view emerges, or, for other characteristics, a pessimistic world view. 1

Abstract. To improve the performance and wellbeing of humans in complex human-computer interaction settings, an interesting challenge for an ambient (or pervasive) agent system is to recognise the emotions of humans. To this end, this paper introduces a computational model to estimate the process of emotion generation based on certain triggers. The model has been implemented and tested using the modelling language LEADSTO. A first evaluation indicates that the model is successful in estimating a person’s emotions, and is robust to different parameter settings. 1

In this paper a cognitive agent model is presented that on the one hand incorporates multiple functions of preparation states such as mirroring an observed action of another agent, imitation of another agent’s action, or imagining an action, and on the other hand incorporates a super mirroring function enabling the agent to keep track of the context of a mental process such as, mirroring another agent, imitation of another agent, action imagination, or own action performance. These cognitive functions have been adopted from mirror neuron and super mirror neuron functions informally described in neurological literature and formalised in a cognitive agent model. Example simulations have been generated that illustrate its functioning, and a mathematical analysis has been made.

Abstract * Two types of modelling approaches exist to reading an observed person’s emotions: with or without making use of the observing person’s own emotions. This paper focuses on an integrated approach that combines both types of approaches in an adaptive manner. The proposed models were inspired by recent advances in neurological context. Both a neural model and a more abstracted cognitive model are presented. In the first place emotion reading is modelled involving (preparatory) mirroring of body states of the observed person within the observing person. This involves a recursive body loop: a converging positive feedback loop based on reciprocal causation between preparations for body states and emotions felt. Here emotion reading involves the person’s own body states and emotions in reading somebody else’s emotions: first the same feeling is developed by mirroring, and after feeling the emotion, it is imputed to the other person. In the second place, as an extension an adaptive process is modelled based on Hebbian learning of a direct connection between a sensed stimulus concerning another agent’s body state (e.g., face expression) and an emotion imputation state. After this Hebbian learning process the emotion is imputed to the other agent before it is actually felt, or even without it is felt. Both the mirroring and Hebbian learning processes first have been modelled at a neural level, and next, in a more abstracted form at a cognitive level. By means of an interpretation mapping the paper shows the relation between the obtained cognitive model and the neurological model. In addition to specifications of both models and the interpretation mapping, simulation results are shown, and automated verification of relevant emerging properties is discussed. 1

Abstract. Within cognitive models, desires are often considered as functional concepts that play a role in efficient focusing of behaviour. In practice a desire often goes hand in hand with having certain feelings. In this paper by adopting neurological theories a model is introduced incorporating both cognitive and affective aspects in the dynamics of desiring and feeling. Example simulations are presented, and both a mathematical and logical analysis is included. 1.

Abstract. Trust is often assumed to depend on experiences. Models for the dynamics of trust in relation to experiences usually have a cognitive nature, leaving affective aspects out of consideration. However, neurological findings show more and more how in mental processes cognitive and affective aspects are intertwined. In this paper, by adopting neurological theories on the role of emotions and feelings, a model for trust dynamics is introduced incorporating the relation between trust and feeling. The model makes use of a Hebbian learning principle and describes how trust does not only depend on experiences viewed as information obtained over time, but also on emotional responses and feelings related to experiences. 1

Abstract. Trust dynamics can be modelled in relation to experiences. Both cognitive and neural models for trust dynamics in relation to experiences are available, but were not yet related or compared in more detail. This paper presents a comparison between a cognitive and a neural model. As each of the models has its own specific set of parameters, with values that depend on the type of person modelled, such a comparison is nontrivial. In this paper a comparison approach is presented that is based on mutual mirroring of the models in each other. More specifically, for given parameter values set for one model, by automated parameter estimation processes the most optimal values for the parameter values of the other model are determined to show the same behavior. Roughly spoken the results are that the models can mirror each other up to an accuracy of around 90%. 1

Abstract. In this paper an adaptive decision model based on predictive loops through feeling states is analysed from the perspective of rationality. Four different variations of Hebbian learning are considered for different types of connections in the decision model. To assess the extent of rationality, a measure is introduced reflecting the environment’s behaviour. Simulation results and the extents of rationality of the different models over time are presented and analysed.

Abstract. An agent's beliefs usually depend on cognitive factors, but also affective factors may play a role. This paper presents an agent model that shows how such affective effects on beliefs can emerge and become stronger over time due to experiences obtained. In this way an effect of judgment by ‘experience’ or ‘gut feeling ’ can be obtained. It is shown how based on Hebbian learning a connection from feeling to belief can develop. Some example simulation results and a mathematical analysis of the equilibria are presented.