E-motional advantage: performance and satisfaction gains with affective computing.
ABSTRACT Emotions are now recognized as complex human control systems, crucial to decision making, creativity, playing and learning. Affective technologies may offer improved interaction and commercial promise. In the past, research has focused on technical development work, leaving many questions about user preferences unanswered. For this user-centered study, 60 participants played a simple 'word ladder' game under different controlled conditions. Using 2 x 2 factorial design, and a Wizard of Oz scenario, half the participants interacted with a system that adapted on the basis of the user's emotional expression and half were told the system could react to their emotional expressions. We established that when using an apparently affective system, users perform significantly better and report themselves as feeling significantly happier. We also discuss behavioral responses to the different conditions. These results are relevant to the design of future affective systems.
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ABSTRACT: What encourages people to refer to a robot as if it was a living being? Is it because of the robot's humanoid or animal-like shape, its movements or rather the kind of interaction it enables? We aim to investigate robots' characteristics that lead people to anthropomorphize it by comparing different kinds of robotic devices and contrasting it to an interactive technology. We addressed this question by comparing anthropomorphic language in online forums about the Roomba robotic vacuum cleaner, the AIBO robotic dog, and the iPad tablet computer. A content analysis of 750 postings was carried out. We expected to find the highest amount of anthropomorphism in the AIBO forum but were not sure about how far people referred to Roomba or the iPad as a lifelike artifact. Findings suggest that people anthropomorphize their robotic dog significantly more than their Roomba or iPad, across different topics of forum posts. Further, the topic of the post had a significant impact on anthropomorphic language.01/2012; DOI:10.1109/ARSO.2012.6213399
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ABSTRACT: In this literature review we explain anthropomorphism and its role in the design of socially interactive robots and human-robot interaction. We illustrate the social phenomenon of anthropomorphism which describes people's tendency to attribute lifelike qualities to objects and other non lifelike artifacts. We present theoretical backgrounds from social sciences, and integrate related work from robotics research, including results from experiments with social robots. We present different approaches for anthropomorphic and humanlike form in a robot's design related to its physical shape, its behavior, and its interaction with humans. This review provides a comprehensive understanding of anthropomorphism in robotics, collects and reports relevant references, and gives an outlook on anthropomorphic human-robot interaction.Proceedings of the 4th international conference on Social Robotics; 10/2012
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ABSTRACT: The studies presented in this article explore a human-centered conceptualization of agents and agency based on the observation that people attribute agency to sufficiently complex interactive systems. Although agency attribution appears to be an unconscious human response, findings from social psychology, affective computing, and perceptual-motor studies suggest agency attribution influences human–computer interaction (HCI). Three studies are presented that examine whether recent findings on agency attribution in physical environments also apply in the virtual environments characteristic of HCI. Results of the studies indicate that agency effects operate in desktop computing environments. Agency effects, however, appear to be influenced by learning effects that preserve a previously observed relationship between perception and action but alter how this effect is expressed. Results suggest that there are both bottom-up and top-down contributions to agency effects in HCI.International Journal of Human-Computer Interaction 09/2013; 29(12):798-813. DOI:10.1080/10447318.2013.777826 · 0.72 Impact Factor