The ability to recognize affective states of a person... This paper argues that next-generation human-computer interaction (HCI) designs need to include the essence of emotional intelligence -- the ability to recognize a user's affective states -- in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI -- an automatic personalized analyzer of a user's nonverbal affective feedback.

We present a probabilistic model to monitor a user’s emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user’s emotional arousal (i.e., the state of the interaction) and its effects (i.e., bodily expressions that are known to be influenced by emotional reactions). The probabilistic model relies on a Dynamic Decision Network to leverage any indirect evidence on the user’s emotional state, in order to estimate this state and any other related variable in the model. This is crucial in a modeling task in which the available evidence usually varies with the user and with each particular interaction. The probabilistic model we present is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user’s learning and engagement during the interaction with educational games. 2 1.

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A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior.

Modern economic theory ignores the influence of emotions on decision-making. Emerging neuroscience evidence suggests that sound and rational decision making, in fact, depends on prior accurate emotional processing. The somatic marker hypothesis provides a systems-level neuroanatomical and cognitive framework for decision-making and its influence by emotion. The key idea of this hypothesis is that decision-making is a process that is influenced by marker signals that arise in bioregulatory processes, including those that express themselves in emotions and feelings. This influence can occur at multiple levels of operation, some of which occur consciously, and some of which occur non-consciously. Here we review studies that confirm various predictions from the hypothesis, and propose a neural model for economic decision, in which emotions are a major factor in the interaction between environmental conditions and human decision processes, with these emotional systems providing valuable implicit or explicit knowledge for making fast and advantageous decisions. © 2004 Elsevier Inc. All rights reserved.

Emerging technologies offer new ways of using entertainment technology to foster interactions between players and connect people. Evaluating collaborative entertainment technology is challenging because success is not defined in terms of productivity and performance, but in terms of enjoyment and interaction. Current subjective methods are not sufficiently robust in this context. This paper describes an experiment designed to test the efficacy of physiological measures as evaluators of collaborative entertainment technologies. We found evidence that there is a different physiological response in the body when playing against a computer versus playing against a friend. These physiological results are mirrored in the subjective reports provided by the participants. We provide an initial step towards using physiological responses to objectively evaluate a user’s experience with collaborative entertainment technology.

Abstract. We describe an Affect and Belief Adaptive Interface System (ABAIS) designed to compensate for performance biases caused by users ’ affective states and active beliefs. The ABAIS architecture implements an adaptive methodology consisting of four steps: sensing/inferring user affective state and performance-relevant beliefs; identifying their potential impact on performance; selecting a compensatory strategy; and implementing this strategy in terms of speci¢c GUI adaptations. ABAIS provides a generic adaptive framework for integrating a variety of user assessment methods (e.g. knowledge-based, self-reports, diagnostic tasks, physiological sensing), and GUI adaptation strategies (e.g. content- and format-based). The ABAIS performancebias prediction isbased on empirical ¢ndings from emotion research combined with detailed knowledge of the task context.The initial ABAIS prototype was demonstrated in the context of an Air Force combat task, used a knowledge-based approach to assess the pilot’s anxiety level, and adapted to the pilot’s anxiety and belief states by modifying selected cockpit instrument displays in response to detected changes in these states.

This article opens by noting that positive emotions do not fit existing models of emotions. Consequently, a new model is advanced to describe the form and function of a subset of positive emotions, including joy, interest, contentment, and love. This new model posits that these positive emotions serve to broaden an individual's momentary thought-action repertoire, which in turn has the effect of building that individual's physical, intellectual, and social resources. Empirical evidence to support this broaden-and-build model of positive emotions is reviewed, and implications for emotion regulation and health promotion are discussed. Even though research on emotions has this new perspective are featured. My hope is flourished in recent years, investigations that that this article will unlock scientific curiosity expressly target positive emotions remain few about positive emotions, not only to test the and far between. Any review of the psychologi- ideas presented here, but also to build other new cal literature on emotions will show that models that might illuminate the nature and psychologists have typically favored negative value of positive emotions. Psychology sorely emotions in theory building and hypothesis needs more studies on positive emotions, not testing. In so doing, psychologists have inadver- simply to level the uneven knowledge bases tently marginalized the emotions, such as joy, between negative and positive emotions, but interest, contentment, and love, that share a more critically, to guide applications and pleasant subjective feel. To date, then, psycholo- interventions that might improve individual and gy's knowledge base regarding positive emo- collective functioning, psychological welltions is so thin that satisfying answers to the question "What good are positive emotions?" have yet to be articulated. This is unfortunate. being, and physical health. Experiences of positive emotion are central to Why Have Positive Emotions human nature and contribute richly to the quality of people's lives (Diener & Larsen, Been Marginalized? 1993; Myers & Diener, 1995). But how? In At this point, it might be useful to inspect

Researchers are using emerging technologies to develop novel play environments, while established computer and console game markets continue to grow rapidly. Even so, evaluating the success of interactive play environments is still an open research challenge. Both subjective and objective techniques fall short due to limited evaluative bandwidth; there remains no corollary in play environments to task performance with productivity systems. This paper presents a method of modeling user emotional state, based on a user’s physiology, for users interacting with play technologies. Modeled emotions are powerful because they capture usability and playability through metrics relevant to ludic experience; account for user emotion; are quantitative and objective; and are represented continuously over a session. Furthermore, our modeled emotions show the same trends as reported emotions for fun, boredom, and excitement; however, the modeled emotions revealed differences between three play conditions, while the differences between the subjective reports failed to reach significance.

Funology: from usability to enjoyment. This chapter introduces the Product Emotion Measurement instrument (PrEmo), an instrument to assess emotional responses to consumer products. The non-verbal self-report instrument measures a set of 14 emotions. Each emotion in this set is portrayed with an animated cartoon character by means of dynamic facial, bodily, and vocal expression, and presented on a computer interface. Participants can report their responses by selecting those animations that correspond with their felt emotion(s). The chapter discusses the development of PrEmo in the context of existing instruments. In addition, an illustrative cross-culture study is reported, in which emotions evoked by car models have been measured in Japan and in The Netherlands. 1.