Welcome to the website that serves as a presentation of the PhD project entitled Digitizing Archetypal Human Experience through Physiological Signals by Leonid Ivonin. The research findings obtained in this project were results of the collaboration between Huang-Ming Chang and Leonid Ivonin. The project was completed within the framework of Erasmus Mundus Joint Doctorate Program in Interactive and Cognitive Environments at Eindhoven University of Technology and Polytechnic University of Catalonia. The work on the project spanned the period of time from January, 2011 till May, 2014.

Here you can read a short summary of the PhD thesis, download the full text of the dissertation, learn about the software for digitizing emotional experience of people, and find other information related to this PhD project.

Digitizing Archetypal Human Experience through Physiological Signals

Summary

The problem of capturing human experience is relevant in many application domains. In fact, the process of describing and sharing individual experience lies at the heart of human culture. Throughout the courses of our lives we learn a great deal of information about the world from other people’s experience. Besides the ability to share utilitarian experience such as whether a particular plant is poisonous, humans have developed a sophisticated competency of social signaling that enables us to express and decode emotional experience. The natural way of sharing emotional experiences requires those who share to be co-present during this event. However, people have overcome the limitation of physical presence by creating a symbolic system of representations. This advancement came at a price of losing some of the multidimensional aspects of primary, bodily experience during its projection into the symbolic form. Recent research in the field of affective computing has addressed the question of digitization and transmission of emotional experience through monitoring and interpretation of physiological signals. Although the outcomes of this research represent a great step forward in developing a technology that supports sharing of emotional experiences, they do not seem to help in preserving the original phenomenological experience during the aforementioned projection. This circumstance is explained by the fact that in affective computing the focus of investigation has been aimed at emotional experiences which can be consciously evaluated and described by individuals themselves. Therefore, generally speaking, applying an affective computing technique for capturing emotions of an individual is not a deeper or more precise way to project her experience into the symbolic form than asking this person to write down a description of her emotions on a piece of paper. One can say that so far the research in affective computing has aimed at delivering technology that could automate the projection but it has not considered the problem of improving the projection in order to preserve more of the multidimensional aspects of human experience. This dissertation examines whether human experience, which individuals are not able to consciously transpose into the symbolic representation, can still be captured using the techniques of affective computing.

First, a theoretical framework for description of human experience which is not accessible for conscious awareness was formulated. This framework was based on the work of Carl Jung who introduced a model of a psyche that includes three levels: consciousness, the personal unconscious and the collective unconscious. Consciousness is the external layer of the psyche that consists of those thoughts and emotions which are available for one’s conscious recollection. The personal unconscious represents a repository for all of an individual’s feelings, memories, knowledge and thoughts that are not conscious at a given moment of time. The collective unconscious is a repository of universal modes and behaviors that are similar in all individuals. According to Jung, the collective unconscious is populated with archetypes. Archetypes are prototypical categories of objects, people and situations that existed across evolutionary time and in different cultures. When an archetype becomes activated and is experienced by a person, it will result in a complex in the personal unconscious. A complex in the personal unconscious is a conglomeration of emotions and ideas that are specific to the person and are product of the archetype. In this thesis, the unconscious experience that is related to archetypes was defined as the archetypal experience. It seemed reasonable to begin our inquiry into the digitization of the unconscious human experience with considering the problem of recognizing the archetypal experience because archetypes are supposed to be common among individuals. Moreover, they provide a convenient way to conceptualize the unconscious experience.

Having defined our theoretical framework we conducted an experiment where visual and auditory stimuli from standardized databases for elicitation of conscious emotions were demonstrated to subjects. Apart from the stimuli for conscious emotions, the subjects were exposed to stimuli that represented the archetype of the self. During presentation of the stimuli cardiovascular signals of the subjects were recorded. The experimental results indicated that heart rate responses of the participants were unique for every category of the stimuli including the archetypal one. These findings gave an impulse to perform another study where a broader spectrum of archetypal experiences was examined.

In our second study, we made a switch from visual and auditory stimuli to audiovisual stimuli because it was expected that videos would be more efficient in elicitation of conscious emotions and archetypal experiences than still pictures or sounds. The number of archetypes was increased, and overall, subjects were stimulated to feel eight various types of the archetypal experience. We also prepared stimuli for conscious emotions. In this experiment, physiological signals included cardiovascular, electrodermal, respiratory activities and skin temperature. The statistical analysis suggested that the archetypal experiences could be differentiated based on the physiological activations. Moreover, several prediction models were constructed based on the collected physiological data. These models demonstrated an ability to classify the archetypes with an accuracy that was considerably higher than the chance level.

As the results from the second study suggested a positive relationship between the archetypal experience and activations of physiological signals, it seemed reasonable to conduct another study in order to confirm the generalizability of our findings. However, before a new experiment started it was decided to build a tool that could facilitate collection of physiological data and recognition of the archetypal experience as well as conscious emotions. Such a tool would help us and other researchers in conducting experiments requiring interpretation of physiological signals. Our tool works on tablet computers and supports collection and analysis of data from wearable sensors.

The last study was conducted using similar methodology as the second experiment with several modifications that aimed at obtaining more robust results. The effort of conducting this study was considerably minimized by using the tool we developed. During the experiment we measured only cardiovascular and electrodermal activities of the subjects because our previous experiments showed that these two signals contributed significantly to the classification of the conscious emotions and the archetypal experience. The statistical analysis indicated a significant relationship between the archetypes portrayed in the videos and physiological responses of the subjects. Furthermore, using data mining methods we created prediction models that were capable of recognizing the archetypal experiences with the accuracy that was lower than in the second study but still considerably higher than the chance level.

Finally, we bring the results presented in this dissertation together and argue that our finding suggest a possibility of capturing the archetypal human experience through physiological data. Our work provides a basis for future research in the area of affective computing that could continue exploration of the hidden dimensions of human experience.

Full Text

The PDF document containing the full text of the PhD thesis can be downloaded using this link.

Slides from The Public PhD Defense

The slides that were demonstrated during the public PhD defence ceremony can be found here.

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