Cross-cultural research on facial expression and the developments of methods to measure facial expression are briefly summarized. What has been learned about emotion from this work on the face is then elucidated. Four questions about facial expression and emotion are discussed. What information does an expression typically convey? Can there be emotion without facial expression? Can there be a facial expression of emotion without emotion? How do individuals differ in their facial expressions of emotion? In 1965 when 1 began to study facial expression, 1 few thought there was much to be learned. Goldstein (1981) pointed out that a number of famous psychologists—F. and G. Allport, Brunswik, Hull, Lindzey, Maslow, Osgood, Titchner—did only one facial study, which was not what earned them their reputations. Harold Schlosberg was an exception, but he was more interested in how to represent the information derived by those who observed the face than in expression itself. 2 The face was considered a meager source of mostly inaccurate, culturespecific, stereotypical information (Bruner & Tagiuri, 1954). That this contradicted what every layman knew made it all the more attractive. Psychology had exposed the falseness of a folk belief, a counterintuitive finding.

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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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Abstract — Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding. I.

We describe an agent architecture that integrates emotions, drives, and behaviors, and that focuses on modeling some of the aspects of emotions as fundamental components within the process of decision-making. We show how the mechanisms of primary emotions can be used as building blocks for the acquisition of emotional memories that serve as biasing mechanisms during the process of making decisions and selecting actions. The architecture has been implemented into an object-oriented framework that has been successfully used to develop and control several synthetic agents and which is currently being used as the control system for an emotional pet robot. Introduction The traditional view on the nature of rationality has proposed that emotions and reason do not mix at all. For an agent to act rationally, it should not allow emotions to intrude in its reasoning processes. Research in Neuroscience, however, has provided evidence indicating quite the contrary, showing that emotions play a f...

Using social science methods to induce a state of frustration in users, we collected physiological, video and behavioral data, and developed a strategy for coupling these data with real-world events. The effectiveness of the proposed strategy was tested in a study with thirty-six subjects, where the system was shown to reliably synchronize and gather data for affect analysis. Hidden Markov Models were applied to each subject 's physiological signals of skin conductivity and blood volume pressure in an effort to see if regimes of likely frustration could be automatically discriminated from regimes when all was proceeding smoothly. This pattern recognition approach correctly classified these two regimes 67.4% of the time. Mouse-clicking behavior was also synchronized to frustration-eliciting events, and analyzed, revealing NN distinct patterns of clicking responses Keywords: Affect, affective computing, user interface, pattern recognition, human-computer interaction, b...

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.

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.