Effects of Emotional Agents on Human Players in the Public Goods Game

Transcription

1 Effects of Agents on Human Players in the Public Goods Game Katharina Göttlicher, Sabine Stein, Dirk Reichardt Duale Hochschule Baden-Württemberg Stuttgart {ai06037, Abstract In previous work we chose the public goods game as a small and adequate example scenario for emotional agents. In our experiment we focus on the influence of emotional reactions of the emotional agents on a human player. The artificial emotions of the agents are generated by a model of emotion which is adapted to the scenario. In our experiment we compare the reactions of the human player in a scenario with and without visual feedback of the other agents. As a visual feedback we use the ECA Greta to embody the emotions generated by our model. Adequate emotion recognition could be proven by an online survey validating the correct valence and intensity perception of the relevant emotions. As one of the first results, a significantly higher amount of investment can be stated for the human player in case he or she sees the facial representation of the emotional state of the other (virtual) players during the public goods game. After splitting up the game in two sections, it could be shown that this significant difference is only true for the second part of the game. Keywords: emotion, personality, mood, agent, game theory, ECA. 1. Introduction Emotion is considered the basis of human social interaction and is the key factor in explaining costly punishment. Fehr and Gächter [1] have examined human behavior within the public goods game and explained punishment acts by emotion rather than rational thinking. The interesting question behind our studies concerns the effect of virtual emotional agents on a human player in such a game. How can an emotional avatar influence human behavior? This question is certainly interesting for many fields, one of which being computer games. In previous work, our virtual emotional agent was developed, which integrates a model of personality and mood and generates virtual emotions by a model based upon the theory of Orthony, Clore and Collins (OCC, see [2],[3],[4]). Several experiments included the public goods game (PGG). In our experiment we combined the model of our virtual emotional agent with the emotional conversational agent (ECA) GRETA. This paper describes the first experiment, which focuses on the effect of seeing a face of the other players versus not having this visual feedback during the game. 2. The Agent Model The public goods game (PGG) with punishment option is a well suited small scenario in which affectbased decisions are shown in human players [1]. In previous work, we adopted the basic model of Ortony, Clore and Collins [2] enhanced by a concept of mood and by a personality model to build up our emotional agent (see [3,4]). The current emotion is computed as a result of a 2 step process. First the elicitation of an emotion is computed using the OCC categories. This is done by a function which integrates the situation and the personality traits. The result is filtered by a mood function in which the mood can amplify or damp the current emotion. In our experiment we use two relaxed versions of the agent in order to represent the players in the PGG. Since the PGG scenario does not (necessarily) induce all 22 OCC emotions, we reduced the number of emotions in the model to anger, joy, shame, remorse as well as hope and fear. However, not all of them have a clear connection to facial expressions and in case of hope and fear only low elicitation levels are plausible. Therefore, mainly anger and joy are expected to be conceived by the human player. For technical reasons, the group size was set to 3 participants in the PGG, one of which is the human player. Both virtual agents are given different personalities (following the Big Five Model) and goal preferences such that different emotional behavior is shown. A preference for getting in the desired mood could contradict the rational goal of optimizing the chances for the best monetary outcome. There are two situations in which the agent has to make a decision: investment and punishment. In the investment phase, the agent tries to predict the effect of his potential actions. For each investment decision, two calculations are made to determine the agent s action. On the one hand there is the predictive emotional reasoning. The idea is to compute the upcoming actions and reactions for all options the agent is comparing. By using the agent model, the emotions are computed and eventually the mood in which the agent would most /09/$ IEEE

2 likely end up is determined when using a certain option. Finally the emotional reasoning votes for the decision which leads to the most desired mood (which depends on the agent s personality). On the other hand, the rational decision is computed using statistics, taking monetary outcome as a basis for decision-making. The agent has to decide whether to react emotionally or rationally. The decision depends on the goal preference setting as well as on the current distance to a goal. As far as punishment is concerned, impulsive reactions are integrated, such that a punishment is made in case a threshold for anger is surpassed. Furthermore, the concept of coping is integrated in the model such that punishing shifts the mood towards a desired mood of the agent. In contrast to the investment phase, there is no prediction of future states or consequences of the action involved. 3. The Experiment The experiment is designed to investigate whether and how emotional feedback derived from emotional agents described above and displayed through the ECA Greta affects human playing behavior in the public goods game Method Experimental Design A basic crossover experimental design was used where every individual was tested with both experimental conditions in two consecutive periods. The participants were randomly assigned to two equally sized groups (n AB = n BA = 10). Group (A,B) played the PGG with emotional feedback in the first phase and without emotional feedback in the second phase while Group (B,A) did the opposite. During the washout phase (between phases I and II), the second application was loaded and people were generally talking about the game with the conductors of the experiment (see table 1). Table 1: Experimental design of emotional feedback study Group (A,B) (n AB =10) Group (B,A) (n BA =10) Phase I Washout Phase Phase II PGG with PGG without Talking about the Game Talking about the Game PGG without PGG with The crossover plan was chosen over the two-sampledesign (comparison of two conditions in two parallel groups) because of its efficiency and improved accuracy through repeated measurement and the need for less participants as every participant represents its own control [5] Procedure The data acquisition took place from the 9 th to the 23 rd of March Eight participants (four for each group) were recruited from a socio-scientific lecture at Duale Hochschule Baden Württemberg (DHBW) Stuttgart while the rest of the participants were recruited from collegiate friends and relatives of the project owners. The people were given a short verbal introduction of what institution the study belongs to and of what the experiment is about, i.e. that they would have to play a computer game with two agents with the goal to make as much money as possible. If they agreed to take part, they were given a questionnaire to fill in their personal information, followed by a written introduction of the game. The written instructions were the same for each group. Before playing the condition with emotional feedback, they were given oral instructions explaining that the two faces are their teammates in the game and that they had to watch their facial reactions/expressions after each step of the game. After playing the PGG two times à twelve rounds (order depending on group membership), the participants completed a second (feedback-) questionnaire before finally receiving an explanation of the background and research question of the study Sample Twenty people who were randomly assigned to the two experimental groups of different playing order participated in the main study. Eleven of them were male and nine female. The majority of participants were between 21 and 30 years old (n=15) while three people stated their age under 20 years and two people were between 31 and 40 years old. The sex and age distribution between the two groups can be read from table 2. Sex was almost evenly distributed while age was a little more diversified in group (B,A) than in group (A,B). Eighty percent (n=16) of the participants had general qualification for university entrance ( German Abitur ) as highest educational level and one in each case had finished secondary technical school ( Hauptschule ), secondary modern school ( Realschule ) and university degree respectively. Various responses were obtained regarding the frequency of use of computer games: seven said that they never play, five very seldom, four sometimes, and two play computer games rather seldom and rather often respectively. According to the feedback received, none of the participants were familiar with the Public Goods Game neither before nor after they had played the game.

3 Figure 1: Application PGG with emotional feedback (here displayed with neutral faces) Table 2: Sample description: sex and age separated for the two experimental groups. Group (A,B) Group (B,A) Sex 5 male, 5 female 6 male, 5 female Age 9: years, 1: years 3: up to 20 years, 6: years, 1: years The Public Goods Game with from the Embodied Conversational Agent Greta The Public Goods Game (PGG) with an option of punishment is a suitable scenario by which emotional decisions of human players can be monitored. The Public Goods Game is in our case a game for three players, one human and two agents. Each player has to pay per round 0 to 20 on a common account. The public account is bearing 60% interest. The invested amount plus interest is equally split regardless of one player s contribution to the public account. After the payout, each player may punish the others if he or she wants (for example, if the other one has paid nothing). The punisher has to pay an amount p (for human players only 2 or 5 ) for punishing the other one while the punished player s account is diminished by 4*p. The discrete amounts of punishment options for human players were chosen in order to assure a spontaneous, emotional reaction. The emotional feedback of the teammates is displayed through the Embodied Conversational Agent (ECA) called Greta ( This agent is already used in several European projects like CALLAS ( SEMAINE ( or HUMAINE ( The facial expressions are defined by a set of eight facial areas and complex expressions are generated based on a face partitioning approach and on Paul Ekman s studies [6]. (For more technical details see for example [6], [7]). The whole application (PGG with emotional agents) under conditions with emotional feedback looked as displayed in figure 1. (The condition without emotional feedback was played only with the upper part without the two Greta faces but otherwise looked the same.) In order to minimize the source of irritation, it was decided to use two identical faces (instead of two different avatars) which were both named Greta, just with different numbers. Gender preferences or differences as well as different associations with names could therefore be excluded. The Gretas had neutral faces in the beginning of the experiment and showed emotional reaction for five seconds after payment of money and after punishment respectively in different levels of intensity between 0

4 and 1, depending on the intensity values of the emotional agent. Between those emotional reactions they turned back to neutral faces again. The evaluation of the test rounds exhibited that both Gretas only showed the emotions joy, anger and shame during this experiment. Greta1 showed in the condition with emotional feedback only anger and shame while Greta2 showed anger and joy. In the condition without emotional feedback the Gretas showed the same emotional pattern, although not visible to the participants. The correct emotion recognition of the six in this experiment used emotions (joy, anger, fear, hope, remorse, shame) plus neutral face was evaluated with an online survey on 106 Information Technology students of the DHBW Stuttgart. (The survey was sent out to 210 students resulting in a return rate of 50.5%). In the online survey, people were asked to evaluate the six emotions in three different intensity levels low (0.2), medium (0.5) and high (0.8) and additionally three neutral faces resulting in a set of 21 pictures of Greta. The picture order was randomly assigned to each participant. The participants were prompted to assess the pictures regarding valence (positive or negative emotion or neutral face) as well as regarding intensity (on a five point rating scale from little to much emotion). It was decided to let the participants only evaluate the valence of the emotion instead of letting them choose from a list of exact emotions because in the experiment it was also only important to perceive the tendency of the emotion. Results generally show satisfying emotion recognition. As only anger, joy and shame were produced by the emotional agents in this experiment, only the results of these three emotions plus the neutral face is shown in table 3. Table 3: Percentage of negative, positive and neutral evaluated emotions for anger, joy and shame valence intensity negativitivtral pos- neu- (1-2) (3) (4-5) anger low med high shame low med high joy low med high neutral intensity is low. Although shame is not one of Ekman s basic emotions and also not classified within the FACS (facial action coding system, see [8]), the participants satisfactorily classified it as a negative emotion. Concerning intensity, joy and anger were best evaluated although medium intense emotions seem not easy to judge right. In addition to the survey, people were asked about their impression of Greta and her emotions. Some participants were surprised by the displayed emotions ( Why is she angry now? ) but some statements like You need not look so angry, I will not pay more anyway! indicated that people felt like playing against humans or at least were paying attention to the faces Hypothesis and Data Analysis The following hypothesis was tested: Human Players who get emotional feedback displayed by avatars that represent their teammates and emotionally react to the players actions show different playing behavior in terms of payment amount and punishment amount to the two avatars than human players who play the same game without emotional feedback. The relevant variables were logged to text-files during the game. These text-files together with the personal information from questionnaires were submitted to the statistical software program SPSS After the preparation of the raw data (e.g. summarizing of the observations, visually preparation of the raw data etc.), an ANOVA for repeated measurements was calculated for the three dependent variables (means of round two to eleven). The first and the last round were not included because of a possible disturbing factor of beginning and finishing the task. After screening the visually displayed raw data, a difference in the first playing rounds and the last playing rounds was observed To prove this, two separate ANOVAs with repeated measurement were calculated for the rounds two to six and rounds seven to eleven respectively Results The multivariate test for the whole game (10 turns (12 minus first and last turn)) shows no significant difference, but a trend difference between a player s behavior with and without emotional feedback (F 3,17 = 2.79, p =.072, ns.). The univariate tests show a significance of the variable amount of investment (F 1,19 = 5.233, p<.05), see table 4. The results in table 3 show that all emotions are classified in the right valence in all three intensity levels. Joy seems to be the easiest to recognize while anger is well classified in the intensities medium and high but can be easily confused with the neutral face if the

5 Table 4: Univariate tests for the whole game amount of investment emotional feedback µ σ F-value p-value df df error with without punishment to Greta1 with without punishment to Greta2 with without After splitting the whole game in two sections (five turns from two to six and five turns from seven to eleven), the visual screening of the data showed that the significance of the whole game may derive from differences in the second period of the game. This assumption could be verified by separate ANOVAs with repeated measurements for values from the two sections. The multivariate test for round two to six was not significant (F 3,17 =.209, n.s.) and also the univariate tests showed no significant difference in any of the dependent variables (see table 5). Table 5: Univariate tests for round two to six amount of investment emotional feedback µ σ F-value p-value df df error with without punishment to Greta1 with without punishment to Greta2 with without However, the significance shown for the whole game derived from the last part of the game (turn seven to eleven). The multivariate test for this second section showed that there are very significant differences in the playing behavior within the two different conditions of receiving emotional feedback versus not receiving emotional feedback (F 3,17 = 6.195, p<.01). The univariate tests show again, that the significance derives from the differences in the amount of investment (see table 6). In summary, the hypothesis that human players who get emotional feedback displayed by avatars show different playing behavior than human players who play the same game without emotional feedback could be approved for the investment amount but not for the punishment amount to the two agents. A significantly higher amount of investments can be stated for the human player in case he or she sees the facial representation of the emotional state of the other (virtual) players during the public goods game. After splitting up the game in two sections, it could be shown that this significant difference is only true for the second part of the game. Table 6: Univariate tests for round seven to eleven amount of investment emotional feedback µ σ F-value p-value df df error with without punishment to Greta1 with without punishment to Greta2 with without Conclusion The general goal of the study was to examine if receiving emotional feedback from avatars has an impact on human behavior within the public goods game. It could be shown that people tend to invest significant more money on the public account, if they see the faces with emotional reactions as opposed to playing just against two invisible agents. Further investigations illustrated that this difference in paying behavior is only true for the second part of the game. The restriction of the significant difference in paying amount to the latter part of the game could be an indication of emotional feedback being only effective for longer lasting tasks. However, it is also possible that - as none of the participants was familiar with the game - they did not have the capacity to pay attention to the faces or emotions due to the initial novelty. Given the fact that the results of Fehr and Gächter [1] explained punishment acts by emotion rather than through rational thinking, it is surprising that only the payment amount brought significant differences and not as expected, the emotional action of punishment. Further investigations could adopt the effect of strategic use of emotional expressions, i.e. using an avatar that always looks sad in order to invoke feelings of charity. Further experiments in this research field may also investigate the effect of emotional feedback within other settings than the public goods game. For example it could be interesting to assess if emotional feedback from an avatar included in a car assistance system can influence the driving behavior. The experiment itself could also be replicated with an extended washout phase between the two phases in the crossover design (e.g. giving the participants simple calculations) or with other participants than only students. Nevertheless, this first finding of an emotional avatar influencing human behavior is a promising start for further research and implementation in several fields.

6 5. Acknowledgements We wish to thank Catherine Pelachaud and the whole Greta team. Special thanks to Radoslaw Niewiadomski who helped us with integrating Greta into the game as well as with conducting the online survey. Many thanks also to Prof. Dr. Stefan Krause and his students as well as all other students who participated in the experiment. 6. References [1] Fehr, E., Gächter, S., Altruistic Punishment in Humans, Nature, Volume 415, pp (2002) [2] Reichardt, D., A definition approach for an emotional Turing Test, in Proceedings of the Second International Conference on Affective Computing and Intelligent Interaction (ACII), Lisbon, Portugal, LNCS Springer Verlag (2007) [3] Reichardt, D., Will Artificial Agents Show Altruistic Punishment In The Public Goods Game?, in Proceedings of the 1st Workshop Emotion and Computing Current Research and Future Impact, Reichardt, D., Levi, P., Meyer, J.-J. Chr. (Editors), 29th Annual German Conference on Artificial Intelligence, Bremen, ISBN X (2006) [4] Reichardt, D., "Towards Virtual Emotions and Emergence of Social Behaviour", in: Proceedings of the 8th International Conference on Intelligent Virtual Agents (IVA'08), Tokyo, Japan (2008) [5] Lehmacher, W. Verlaufskurven und Crossover., in: K. Überla, P.L. Reichertz and N. Victor, Editors, Medizinische Informatik und Statistik. Berlin, Springer Verlag (1987) [6] Niewiadomski,R., Ochs, M. & Pelachaud, C. Expressions of Empathy in ECAs., in: Prendinger, H., Lester, J., Ishizuka, M., Intelligent Virtual Agents. 8th International Conference, IVA (2008) [7] Bevacqua, E., Mancini, M, Niewiadomski,R., Pelachaud, C., An expressive ECA showing complex emotions., in Proceedings of the AISB Annual Convention, Newcastle, UK, (2007) [8] Ekman, P., Gesichtsausdruck und Gefühl 20 Jahre Forschung von Paul Ekman, 1. Auflage, Junfermann Verlag, Paderborn (1988)