El concurso

Since it can be a key element of human-robot interaction design, the effects of task structure on users‘ perception are also worth probing. Mutlu et al. (2006) used the humanoid robot-Honda‘s ASIMO to experimentally demonstrate effects of user attribute of gender and the task structure of competition and cooperation on perception of the emotional expressions of the sociable robots. They found out that men perceived the robot ASIMO less positively in the competitive task than in the cooperative task, while women‘s perceptions relied on the social attributes of the robot and did not change based on the task structure, which suggested that the task structure and the individual attributes of users should be matched to the interaction style of the robot during the designing of human-robot interaction.

3.4 Summary and Conclusion

This chapter contains a review of how different theories of emotion in psychology are adapted to create convincing and believable synthetic emotions

of some emotional robots. In addition, androids such as Bristol Robotics Laboratory‘s Jules (Jaeckel et al. 2008a; 2008b; Henrik et al. 2009) using certain mathematical models which map from facial expressions of human beings to a robot‘s muscle or servo space are introduced.

Socially interactive robots, varying from humanoid, android, creature-like, to simply non-humanoid and non-zoomorphic in form, are often equipped with rich facial features to enable them to participate in emotion based interactions. The facial expressions of a robot can be created by means of the FACS (Ekman and Friesen, 1978; 2002). These robots should be able to only express a single affective state at a time as they can only be in a single state at a time (Breazeal, 2003a). They also need to be able to communicate this single affective state to humans through a discrete facial expression. Hence, the accuracy of selecting and conveying a robot‘s specific affective state, along with the timing of its emotional response, become important factors in achieving efficient communication. The arbitration of its specific affective state and the control of the timing can be processed by its own affect space, for instance, Russell‘s circumplex model of affect (Russell, 1997; Posner et al. 2005). As has been mentioned before, a robot‘s computational model of emotion becomes useful when coordinating a robot to respond to the external environment appropriately. The importance of such a model has been demonstrated in the Kismet project (Breazeal, 2002; 2003), which took a more complicated approach to a robot‘s emotions than that of the Probo project (Saldien et al. 2010).

However, the external environment that these robots interacted with in the previous experiments did not involve surrounding contexts with high emotional valence (e.g., valenced music or video). For instance, in the Kismet project, the effect of the external environment on the robot‘s internal state, which in turn affects its emotional expressions, was focused on. It seems that researchers in this field have made little effort to match the robot‘s emotional responses to the emotional external environment (e.g., a surrounding emotional context).

Moreover, Creed and Beale (2005) pointed out that little is known about how humans respond psychologically to synthetic emotions (through the use of

textual content, speech (synthetic and recorded), and synthetic facial expressions) and what effect they have on a user's perceptions, behavior and performance. In their paper, they raised several questions for future experiments, such as ―Can emotional agents influence or change a user's attitudes, beliefs, and behavior more effectively than unemotional agents?‖. Furthermore, Beale and Creed (2009) stated that ―it is not sufficient to simply ask whether emotional agents are better or worse than unemotional agents. Instead, a better question is that of which kind of emotional expression, expressed in which way, influences which elements of a user’s perceptions and behavior?‖. (Beale and Creed, 2009, p. 757)

There are some studies looking at Beale and Creed‘s question. For example, Moshkina and Arkin‘s (2005) experiment which involved a Sony robotic dog Aibo arrived at the conclusion that:(1) participants believed that the robot displayed emotions and/or personality and their interaction was made more pleasant by emotions and/or personality displayed by the robot at the same time; (2) the participants‘ negative mood was reduced when they interacted with a robot that displayed emotions; (3) women were more susceptible to emotional cues and had more desire to show their emotions to the robot. The above results also supported the hypothesis that emotional agents influence or change a user's attitudes, beliefs, and behavior more effectively than unemotional agents. Regarding the iCat, researchers have not only used it to examine the influence of perceived social abilities of a robot on a user‘s attitude towards and acceptance of the robot (Heerink et al. 2006), but also used it to examine the effects of iCat‘s emotional behavior on users‘ perception (Leite et al. 2008). Leite et al. (2008) used the iCat robot and chess (with physical electronic chessboard that detects the board state and sends it to the computer) to develop a chess game platform, where the robot‘s affective state was influenced by every move the player made, which meant that users could know whether they had made a good or a bad move by looking at iCat‘s expressions. By investigating the effects of the robot‘s emotional behavior on the user‘s perception of the game state, they found that a social robot with emotional behavior could perform the task of helping users to understand a gaming

situation better than a robot without emotional behavior.

Empirical research involving the Sony robotic dog Aibo and Philips emotional robot iCat are not the only studies investigating the significance of user attributes (e.g., attitudes, beliefs, and behavior) besides robot attributes (e.g., robot‘s emotional capability). Beale and Creed (2009) pointed out that a number of recent studies have examined the influence of synthetic agent emotion on user attitudes, perceptions and behavior (for examples, Maldonado et al. 2005; Prendinger et al. 2003; Berry et al. 2005; Morkes et al. 1998). Although the results of these studies were often inconclusive and contradictory, it was clear from all of these studies that synthetic emotions expressed by agents had the potential to influence user attitudes and behavior in a variety of ways, even though they were only rated as having a negative impact on the interaction on a couple of occasions.

As Beale and Creed (2009) suggested, it is worth exploring the question of which kind of emotional expression, expressed in which way, influences which elements of a user‘s perceptions and behavior. As can be seen in the cases of Kismet (Breazeal, 2002) and Probo (Saldien et al. 2010), the facial expressions of the robot were only perceived in the absence of any surrounding emotional context (e.g., music or video). Will participants‘ judgments of the facial cues be different given a surrounding emotional context? Will the mood states of the participants be affected by the facial cues of the robot or by the surrounding emotional context?

Beale and Creed (2009) compiled the following suggestions for investigations of the ways in which emotional agents affect users:

(1) Always validate emotional expressions: Before conducting an experiment, one should make sure that subjects can perceive the emotional expressions from an emotional agent relatively correctly. The extent to which users perceive the emotional expressions as the researchers intended should be tested as a pre-experiment.

(2) Fair comparison of emotion: One should ensure that the comparison between conditions is fair, for example, an emotional agent versus an unemotional one, a happy and warm emotional agent versus a sad and cold emotional agent, and strictly controlled experimental scenario versus less controlled and more natural environment. Failure to do this ultimately results in the validity of the reported effects being lowered.

(3) Be explicit about descriptions of emotional expression: In order for other researchers to assess the validity of experimental results, it is imperative that authors go into detail about which emotions were expressed, when they were expressed (e.g. what events triggered a response), which (if any) model of emotion (basic emotions versus cognitive emotions such as frustration, grief, humiliation, pride, and jealousy) was used, and how they were expressed (i.e. what was said, what textual content was used, what facial expressions were used).

(4) Statistics: All means and standard deviations should be included in the paper to facilitate statistical comparisons between studies.

(5) Finer grained approach: Researchers should focus on exploring which types of emotions, expressed in which ways, have which type of influence on an interaction.

The above suggestions serve as my guidelines for the experiments reported in this thesis. A set of experiments, partially designed to answer Beale and Creed (2009)‘s questions about which kind of emotional expression, expressed in which way, influences which elements of a user‘s perceptions and behavior, are presented in next chapter.

Chapter 4: Contextual Effects on Judgments about Synthetic