The last decade has shown a strong in#uence of cognitive psychology and cognitive science perspectives on human-computer interaction (HCI) theory, design and practice. As complex systems have come to dominate many real world domains, it has become necessary to use cognitive task analysis, cognitive modeling, and cognitive systems engineering as specialties that inform understanding of how humans adapt to unexpected situations, changes in performance demands, information and outcome uncertainties, and unintended consequences of actions. The decade has in turn also seen an increased awareness of the social components of HCI and joint human-machine decision making.
Researchers have applied "ndings from theory and "eldwork to address these issues and to develop new theories, methodologies, and innovative applications in the humancomputer interaction area. An explosion of human inferface technologies involving ecological interface designs, agents, virtual immersive workspaces, decision support systems, avatars, distributed architectures, and computer-supported cooperative work have come on the scene as the means to address these complex problems. Yet an important facet of complexity has been missing: the role of a!ect in human-computer interaction.
Psychological and neuroscience research over the past decade suggests that a!ect plays a critical role in decision making and performance, by in#uencing a variety of cognitive processes (e.g. attention, perception, planning, etc.). This includes not only the more familiar, extreme, and potentially negative in#uences of a!ective states, but also the highly adaptive a!ective modulation of problem solving and performance.
In spite of these emerging data about the critical role of a!ect in decision-making, the role and function of a!ect is generally not considered in the disciplines that address the broad issues of understanding complex systems and complex behavior. Models and designs proposed as unique solutions to HCI problems typically consider only cognitive factors. Consequently, the resulting systems are often unable to robustly adapt to real-world situations where a!ective factors may play a role, or even predominate. Cognition that is situated &&in the wild'' is particularly entwined with the in#uences of a!ect and consequent perceptions, beliefs, and situation assessments; all of which can signi"cantly in#uence proposed cognitive models, cognitive system designs, or "eld study applications.
* Prediction of the e!ects of distinct a!ective states on cognition and performance * Adapting decision aiding and user interface to the user's a!ective state to improve/maintain performance E Synthesis of a!ective states: * Generation of realistic facial expressions and behavioral manifestation of a!ective states in virtual agents 454 ANNOUNCEMENT E Applications: * Individual and team task training and assessment * Virtual reality assessment, treatment, and diagnosis environments * Decision support systems * Virtual reality gaming, edutainment, and infotainment TYPE OF SUBMISSION