Emotional Appraisal : A Computational Perspective

Research on computational modelling of emotions has received significant attention in the last few decades. As such, several computational models of emotions have been proposed which have provided an unprecedented insight into the implications of the emotion theories emerging from cognitive psychology studies. Yet the existing computational models of emotion have distinct limitations namely:(i) low replicability - difficult to implement the given computational model by reading the description of the model, (ii) domain dependence - model only applicable in one or more predefined scenarios or domains, (iii) low scalability and integrability - difficult to use the system in larger or different domains and difficult to integrate the model in wide range of other intelligent systems. In this paper, we propose a completely domain-independent mathematical representation for computational modelling of emotion that provides better replicability and integrability. The implementation of our model is inspired by appraisal theory - an emotion theory which assumes that emotions result from the cognitive evaluation of a situation

Agents with a Moral Dimension (Doctoral Consortium)

ABSTRACT As argued by Categories and Subject Descriptors MORAL EMOTIONS Based on [9, 13] we argue that moral emotions are complex emotions involving cognitive processes. Given [9], we identify the following moral emotions : Pride, Self-reproach, Reproach, Admiration, Gratification, Gratitude, Anger and Remorse. In the field of Artificial Intelligence, we observed an increased interest in studying computational models of emotions; many computational models have been modele

Computational models of social and emotional turn-taking for embodied conversational agents: a review

The emotional involvement of participants in a conversation not only shows in the words they speak and in the way they speak and gesture but also in their turn-taking behavior. This paper reviews research into computational models of embodied conversational agents. We focus on models for turn-taking management and (social) emotions. We are particularly interested in how in these models emotions of the agent itself and those of the others in uence the agent's turn-taking behavior and vice versa how turn-taking behavior of the partner is perceived by the agent itself. The system of turn-taking rules presented by Sacks, Schegloff and Jefferson (1974) is often a starting point for computational turn-taking models of conversational agents. But emotions have their own rules besides the "one-at-a-time" paradigm of the SSJ system. It turns out that almost without exception computational models of turn-taking behavior that allow "continuous interaction" and "natural turntaking" do not model the underlying psychological, affective, attentional and cognitive processes. They are restricted to rules in terms of a number of supercially observable cues. On the other hand computational models for virtual humans that are based on a functional theory of social emotion do not contain explicit rules on how social emotions affect turn-taking behavior or how the emotional state of the agent is affected by turn-taking behavior of its interlocutors. We conclude with some preliminary ideas on what an architecture for emotional turn-taking should look like and we discuss the challenges in building believable emotional turn-taking agents

An emotion and cognitive based analysis of mental health disorders from social media data

[EN] Mental disorders can severely affect quality of life, constitute a major predictive factor of suicide, and are usually underdiagnosed and undertreated. Early detection of signs of mental health problems is particularly important, since unattended, they can be life-threatening. This is why a deep understanding of the complex manifestations of mental disorder development is important. We present a study of mental disorders in social media, from different perspectives. We are interested in understanding whether monitoring language in social media could help with early detection of mental disorders, using computational methods. We developed deep learning models to learn linguistic markers of disorders, at different levels of the language (content, style, emotions), and further try to interpret the behavior of our models for a deeper understanding of mental disorder signs. We complement our prediction models with computational analyses grounded in theories from psychology related to cognitive styles and emotions, in order to understand to what extent it is possible to connect cognitive styles with the communication of emotions over time. The final goal is to distinguish between users diagnosed with a mental disorder and healthy users, in order to assist clinicians in diagnosing patients. We consider three different mental disorders, which we analyze separately and comparatively: depression, anorexia, and self-harm tendencies.The authors thank the EU-FEDER Comunitat Valenciana 2014- 2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Chulvi-Ferriols, MA.; Rosso, P. (2021). An emotion and cognitive based analysis of mental health disorders from social media data. Future Generation Computer Systems. 124:480-494. https://doi.org/10.1016/j.future.2021.05.032S48049412

Embodied Robot Models for Interdisciplinary Emotion Research

Due to their complex nature, emotions cannot be properly understood from the perspective of a single discipline. In this paper, I discuss how the use of robots as models is beneficial for interdisciplinary emotion research. Addressing this issue through the lens of my own research, I focus on a critical analysis of embodied robots models of different aspects of emotion, relate them to theories in psychology and neuroscience, and provide representative examples. I discuss concrete ways in which embodied robot models can be used to carry out interdisciplinary emotion research, assessing their contributions: as hypothetical models, and as operational models of specific emotional phenomena, of general emotion principles, and of specific emotion ``dimensions''. I conclude by discussing the advantages of using embodied robot models over other models.Peer reviewe

A Model of Emotion as Patterned Metacontrol

Adaptive systems use feedback as a key strategy to cope with uncertainty and change in their environments. The information fed back from the sensorimotor loop into the control architecture can be used to change different elements of the controller at four different levels: parameters of the control model, the control model itself, the functional organization of the agent and the functional components of the agent. The complexity of such a space of potential configurations is daunting. The only viable alternative for the agent ?in practical, economical, evolutionary terms? is the reduction of the dimensionality of the configuration space. This reduction is achieved both by functionalisation —or, to be more precise, by interface minimization— and by patterning, i.e. the selection among a predefined set of organisational configurations. This last analysis let us state the central problem of how autonomy emerges from the integration of the cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. In this paper we will show a general model of how the emotional biological systems operate following this theoretical analysis and how this model is also of applicability to a wide spectrum of artificial systems