Emotion representation and physiology assignments in digital systems

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152 C. Peter, A. Herbon / Interacting with Computers 18 (2006) 139–170 Fig. 4. Clustered emotion. Instead, changes in physiological reactions compared to the baseline are of interest, which is why the data will have to be z-transformed. Appropriate analysis methods, like multiple regression analysis, will then have to be conducted to reveal correlations between the dimensions and physiology. In a third step, after having attained a respectively high number of emotion ratings and related physiological measures, cluster analysis can be used to group those emotions into clusters that do not differ significantly from each other concerning their physiology and their position in the coordinate system. The resulting emotion can then be defined by its means (see Fig. 4). In the fourth and last step, it should be possible to identify a characteristic pattern in the physiological signals for each of the clusters. Those can be used in further processing steps to automatically detect and discriminate distinctive affective states of the user in the given context. In order for an affective application to measure emotion, distinct patterns of signals from a variety of sensors, cameras, microphones, and other sources need to be found. As described in Section 2, some studies have focused on finding dimension-correlates in the past and have turned out to show promising results. Still, there is a great need for further research using the concept of mixed emotions and avoiding labelling emotions with emotion words, for instance by using Wierzbicka’s emotion descriptions or Lang’s SAM. 5. Experimental validation We made a first effort to validate the two-dimensional model of emotions. We conducted steps one and two as described above. Subjects had to perform computing
C. Peter, A. Herbon / Interacting with Computers 18 (2006) 139–170 153 tasks while emotion-related physiology changes were recorded. Data was obtained for skin conductivity (electro-dermal activity, EDA), skin temperature, heart rate, and pupil dilation. At the end of each task, which took 3–5 min each, the subjects had to rate their feeling during the past activity on SAM. Tasks to be performed included: a counting task, a search task, a repetition task (painting), and a game. Each task was to be performed in different difficulty levels to cover as much of the two-dimensional space of valence and arousal as possible and to make sure that the influence of task difficulty would not falsify the affect data. We assumed that the difficulty level will very much influence both, the emotions or feelings about completing the task and the physiological reactions accompanying the completion. Since the goal of the experiment was to find correlations between physiological changes and self-reported emotional states, the part of the physiological response which was due to the cognitive processes such as problem solving or skilled behaviour was a non-interesting variance which had to be controlled. Results show that physiology does correlate with the emotion dimensions used by the suggested model. For data analysis, we first used multiple regression analyses with rather weak results. We then applied Multivariate Pearson Correlation on all six parameters, revealing several significant coherences. Especially for EDA and pupil diameter, highly significant correlations to valence and arousal could be observed. Heart rate correlation was rather weak, but significant also. Skin temperature by itself does not appear to be a useful predictor, but seems to be suitable to further refine predictions based on the other parameters. The most promising data was achieved for pupil diameter, which correlated K.328 with arousal and up to K.5 with valence. EDA correlations were almost as strong with up to K.439 for the valence dimension and K.3 for arousal. We did not find any significant physiological differences between the three difficulty levels used and did therefore not consider them for further analyses. We encountered different methodological problems using real life scenarios instead of pictures or films (as usually done in psychological studies): Real-life scenarios are harder to control, i.e. the subjects’ behaviour and resulting emotions are harder to predict. We did have trouble inducing negative, low arousing emotions (quadrant four), which was not indicated by our pre-tests. This problem was probably one of population; by using highly motivated and interested college students, they were not very likely to be bored by the experiment. Our pre-tests also indicated socially desirable emotion ratings, which is why in the main study the introduction put great emphasis on honesty and pointed out that ratings are not assessed to judge the scenarios themselves or even the test-leaders’ competence. Using self-assessment of emotions was additionally difficult because of real-life scenarios lasting several minutes compared to photographs, which are usually presented for a few seconds each. Subjects had to recall their emotions and maybe also to name the prevailing ones, since they might have gone through different emotional states during a task. In addition, the artificial laboratory situation and the fact of being connected to different technical devices are also likely to have prevented subjects from calming down (see Peter et al., 2005). We do believe that further research must be devoted to the set up
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