Exploring the Affective Loop

Research in psychology and neurology shows that both body and mind are involved when experiencing emotions (Damasio 1994, Davidson et al. 2003). People are also very physical when they try to communicate their emotions. Somewhere in between beings consciously and unconsciously aware of it ourselves, we produce both verbal and physical signs to make other people understand how we feel. Simultaneously, this production of signs involves us in a stronger personal experience of the emotions we express. Emotions are also communicated in the digital world, but there is little focus on users' personal as well as physical experience of emotions in the available digital media. In order to explore whether and how we can expand existing media, we have designed, implemented and evaluated /eMoto/, a mobile service for sending affective messages to others. With eMoto, we explicitly aim to address both cognitive and physical experiences of human emotions. Through combining affective gestures for input with affective expressions that make use of colors, shapes and animations for the background of messages, the interaction "pulls" the user into an /affective loop/. In this thesis we define what we mean by affective loop and present a user-centered design approach expressed through four design principles inspired by previous work within Human Computer Interaction (HCI) but adjusted to our purposes; /embodiment/ (Dourish 2001) as a means to address how people communicate emotions in real life, /flow/ (Csikszentmihalyi 1990) to reach a state of involvement that goes further than the current context, /ambiguity/ of the designed expressions (Gaver et al. 2003) to allow for open-ended interpretation by the end-users instead of simplistic, one-emotion one-expression pairs and /natural but designed expressions/ to address people's natural couplings between cognitively and physically experienced emotions. We also present results from an end-user study of eMoto that indicates that subjects got both physically and emotionally involved in the interaction and that the designed "openness" and ambiguity of the expressions, was appreciated and understood by our subjects. Through the user study, we identified four potential design problems that have to be tackled in order to achieve an affective loop effect; the extent to which users' /feel in control/ of the interaction, /harmony and coherence/ between cognitive and physical expressions/,/ /timing/ of expressions and feedback in a communicational setting, and effects of users' /personality/ on their emotional expressions and experiences of the interaction

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed

A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise

The potential contribution of the peripheral auditory efferent system to our understanding of speech in a background of competing noise was studied using a computer model of the auditory periphery and assessed using an automatic speech recognition system. A previous study had shown that a fixed efferent attenuation applied to all channels of a multi-channel model could improve the recognition of connected digit triplets in noise [G. J. Brown, R. T. Ferry, and R. Meddis, J. Acoust. Soc. Am. 127, 943?954 (2010)]. In the current study an anatomically justified feedback loop was used to automatically regulate separate attenuation values for each auditory channel. This arrangement resulted in a further enhancement of speech recognition over fixed-attenuation conditions. Comparisons between multi-talker babble and pink noise interference conditions suggest that the benefit originates from the model?s ability to modify the amount of suppression in each channel separately according to the spectral shape of the interfering sounds

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation