Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

An Architecture for Personality-based, Nonverbal Behavior in Affective Virtual Humanoid Character

As humans we perceive other humans as individually different based – amongst other things – on a consistent pattern of affect, cognition, and behavior. Here we propose a biologically and psychologically grounded cognitive architecture for the control of nonverbal behavior of a virtual humanoid character during dynamic interactions with human users. Key aspects of the internal states and overt behavior of the virtual character are modulated by high-level personality parameters derived from the scientific literature. The virtual character should behave naturally and consistently while responding dynamically to the environment's feedback. Our architecture strives to yield consistent patterns of behavior though personality traits that have a modulatory influence at different levels of the hierarchy. These factors affect on the one hand high-level components such as ‘emotional reactions’ and ‘coping behavior’, and on the other hand low-level parameters such as the ‘speed of movements and repetition of gestures. Psychological data models are used as a reference to create a map between personality factors and patterns of behavior. We present a novel hybrid computational model that combines the control of discrete behavior of the virtual character moving through states of the interaction with continuous updates of the emotional state of the virtual character depending on feedback from interactions with the environment. To develop and evaluate the hybrid model, a testing scenario is proposed that is based on a turn-taking interaction between a human participant and a 3D representation of the humanoid character. We believe that our work contributes to individualized, and ultimately more believable humanoid artifacts that can be deploy in a wide range of application scenarios

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

EMPATH: A Neural Network that Categorizes Facial Expressions

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain

The morphofunctional approach to emotion modelling in robotics

In this conceptual paper, we discuss two areas of research in robotics, robotic models of emotion and morphofunctional machines, and we explore the scope for potential cross-fertilization between them. We shift the focus in robot models of emotion from information-theoretic aspects of appraisal to the interactive significance of bodily dispositions. Typical emotional phenomena such as arousal and action readiness can be interpreted as morphofunctional processes, and their functionality may be replicated in robotic systems with morphologies that can be modulated for real-time adaptation. We investigate the control requirements for such systems, and present a possible bio-inspired architecture, based on the division of control between neural and endocrine systems in humans and animals. We suggest that emotional epi- sodes can be understood as emergent from the coordination of action control and action-readiness, respectively. This stress on morphology complements existing research on the information-theoretic aspects of emotion

Emotions as Proximal Causes of Word of Mouth: A Nonlinear Approach

Service research tends to operationalize word of mouth (WOM) behavior as one of the many responses to service satisfaction. In this sense, little is known about its antecedents or moderators. The objective of this study was to investigate the role of customers' emotions during service experiences on WOM, applying nonlinear techniques and exploring the moderating role of customers' propensity for emotional contagion. Using the critical incidents technique, 122 customers recalled significant service experiences and the emotions they aroused, and reported if they shared said experiences with other individuals. We found that, whereas linear methods presented non-significant results in the relation emotions-WOM, nonlinear ones (Artificial Neural Networks) explained 46% of variance. Negative emotions were stronger predictors of WOM and the importance of emotions for WOM was significantly higher for individuals with high propensity for emotional contagion (R2 = .79) than for those with lower levels (R2 = .48). Theoretical and practical implications are discussed

The effect of state core self-evaluations on task performance, organizational citizenship behaviour, and counterproductive work behaviour

Although the personality-performance relationship has been studied extensively, most studies focused on the relationship between between-person differences in the Big Five personality dimensions and between-person differences in job performance. The current paper extends this research in two ways. First, we build on core self-evaluations (CSEs): an alternative, broad personality dimension that has proven to be a good predictor of job performance. Second, we tested concurrent and lagged within-person relationships between CSEs and task performance, organizational citizenship behaviour (OCB), and counterproductive work behaviour (CWB). To this end, we conducted two experience sampling studies; the first one assessing the relationship between state CSEs and levels of momentary task performance and OCB, and a second study in which employees reported on their level of state CSEs and momentary CWB. Results showed that there is substantial within-person variability in CSEs and that these within-person fluctuations relate to within-person variation in task performance, OCB, and CWB towards the organization, and CWB towards the individual. Moreover, CSEs prospectively predicted within-person differences in task performance and CWB towards the organization, whereas the reversed effect did not hold. These findings tentatively suggest that state CSEs predict performance, rather than the other way around