70 research outputs found
Dynamic Facial Expression of Emotion Made Easy
Facial emotion expression for virtual characters is used in a wide variety of
areas. Often, the primary reason to use emotion expression is not to study
emotion expression generation per se, but to use emotion expression in an
application or research project. What is then needed is an easy to use and
flexible, but also validated mechanism to do so. In this report we present such
a mechanism. It enables developers to build virtual characters with dynamic
affective facial expressions. The mechanism is based on Facial Action Coding.
It is easy to implement, and code is available for download. To show the
validity of the expressions generated with the mechanism we tested the
recognition accuracy for 6 basic emotions (joy, anger, sadness, surprise,
disgust, fear) and 4 blend emotions (enthusiastic, furious, frustrated, and
evil). Additionally we investigated the effect of VC distance (z-coordinate),
the effect of the VC's face morphology (male vs. female), the effect of a
lateral versus a frontal presentation of the expression, and the effect of
intensity of the expression. Participants (n=19, Western and Asian subjects)
rated the intensity of each expression for each condition (within subject
setup) in a non forced choice manner. All of the basic emotions were uniquely
perceived as such. Further, the blends and confusion details of basic emotions
are compatible with findings in psychology
Modeling Cognitive-Affective Processes with Appraisal and Reinforcement Learning
Computational models can advance affective science by shedding light onto the
interplay between cognition and emotion from an information processing point of
view. We propose a computational model of emotion that integrates reinforcement
learning (RL) and appraisal theory, establishing a formal relationship between
reward processing, goal-directed task learning, cognitive appraisal and
emotional experiences. The model achieves this by formalizing evaluative checks
from the component process model (CPM) in terms of temporal difference learning
updates. We formalized novelty, goal relevance, goal conduciveness, and power.
The formalization is task independent and can be applied to any task that can
be represented as a Markov decision problem (MDP) and solved using RL. We
investigated to what extent CPM-RL enables simulation of emotional responses
cased by interactive task events. We evaluate the model by predicting a range
of human emotions based on a series of vignette studies, highlighting its
potential in improving our understanding of the role of reward processing in
affective experiences.Comment: 15 pages, 7 figure
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