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User Experience: Beyond Cognition and Emotion … Matthias RAUTERBERG Eindhoven University of Technology – TU/e The Netherlands 2014

Interaction Paradigms in Computing

time 1960 2020

Personal computing Man-machine-interaction

1980 2000

Cooperative computing Computer-mediated-interaction

Social computing Community-mediated-interaction

Cultural computing Cross cultural-interaction

© Matthias Rauterberg, 2014 Eindhoven University of Technology 2/28

What is Culture? Culture is the integration pattern of human behavior that includes - attitudes, - norms, - values, - beliefs, - actions, - communications and language - institutions of a race, ethnic, religious and/or social group. The word culture comes from the Latin root colere (to inhabit, to cultivate, or to honor). In general, it refers to human activity; different definitions of culture reflect different theories for understanding, or criteria for valuing, human activity. Anthropologists use the term to refer to the universal human capacity to classify experiences, and to encode and communicate them symbolically. They regard this capacity as a defining feature of the genus Homo.


Attitudes Norms Values Beliefs

Etc.

Attitudes Norms Values Beliefs

Etc.

conscious

unconscious


“It is not the consciousness of men that determines their being, but, on the contrary, their social being that determines their consciousness.”

Karl MARX [1818-1883]


Salem, B.; Nakatsu, R.; Rauterberg, M. (2009). Kansei experience: Aesthetic, emotions and inner balance. International Journal on Cognitive Intelligence and Natural Intelligence, vol. 3, no. 2, pp. 18-36.

Daniel Kahneman Map of Bounded Rationality: A Perspective on Intuitive Judgement and Choice . Nobel Prize Lecture, 8 December 2002


consciousness * propositional representation

* synchronization via global workspace

phenomenal consciousness * internal processing

unconsciousness * parallel processing in modular subsystems

verbal behaviour

nonverbal behaviour

multi modal sensory input

mul

ti m

odal

act

uato

r out

put

Hofmann, W., & Wilson, T. (2010). Consciousness, introspection, and the adaptive unconscious. In B. Gawronski & B. K. Payne (Eds.), Handbook of Implicit Social Cognition: Measurement, Theory, and Applications (pp. 197-215). New York: Guilford Press.



Sandy PENTLAND

After 1910 the discoveries of Carl Gustav JUNG about the collective unconscious and the related archetypes were challenging. Jung dreamt a great deal about the dead, the land of the dead, and the rising of the dead. These represented the unconscious itself -- not the "little" personal unconscious that Freud made such a big deal out of , but a new collective unconscious of humanity itself , an unconscious that could contain all the dead, not just our personal ghosts. Jung began to see the mentally ill as people who are haunted by these ghosts, in an age where no-one is supposed to even believe in them. If we could only recapture our mythologies, we would understand these ghosts, become comfortable with the dead, and heal our mental illnesses.

(1875-1961)



The Hero's Journey

Joseph CAMPBELL [1904-1987]

A collaborative project between two PhD students

Leonid IVONIN

• Generation of ideas • Statistical analysis • Writing of articles

Shared activities

• Processing of physiological signals and application of data mining methods

• Development of technical infrastructure for the experiments

• Sensing application (ArcheSense)

Huang-Ming CHANG

• Identification, selection, and preparation of audiovisual stimuli for elicitation of psychological states

• Selection of appropriate questionnaires

• Presenting application (ArcheBoard)


How to design for the unconscious?

2.Elicitation 3.Recognition Emotional

Responses

Mapping

Affective Stimuli

1.Explicit Emotions (e.g. joy, anger, sadness, etc.)

The State of the Art


Elicitation Recognition Smile

Mapping

The State of the Art

The emotion of Joy


• Perceptual Quality Black, White

• Physical Content Dove

• Symbolic meaning Peace, Spirit, etc.

What induces emotions?

Problem of Current States

Emotion Appraisal

• Barrett, L. F. (2012). Emotions are real. Emotion, 12(3), 413–29.


Number of participants: • 34

Two types of stimuli: • Visual (pictures) • Auditory (sounds)

Five categories of stimuli: • Archetypal • Positive-relaxing • Positive-arousing • Neutral • Negative

Six stimuli per category Physiological data:

• Heart Rate • Galvanic Skin Response • Skin Temperature Examples of visual stimuli

The findings indicated a significant relationship between the categories of stimuli (including archetypal) and physiological signals.

Hypothesis: Psychological states related to archetypal stimuli would lead to different patterns of physiological activations.


Study 1 - method

Number of participants: • 36

Two types of stimuli: • Film clips with explicit emotions • Film clips with archetypal experiences

Five categories of explicit stimuli: • Neutral • Amusement • Fear • Joy • Sadness

Physiological data: • ECG (HR + HRV) • Skin conductance (response + level) • Respiration • Skin temperature

Duration of each stimulus: Approximately 5 minutes

Self-report ratings after every stimulus

Eight categories of archetypal stimuli: • Anima • Animus • Hero departure • Hero initiation • Hero return • Mentor • Mother • Shadow

Hypothesis: Various archetypal experiences lead to recognizable patterns of physiological activations that could be differentiated using computational intelligence algorithms.


Study 2 - method

Dynamic patterns of the heart rate responses of the participants to the film clips presentations.

The mean values and 95% confidence intervals of the HR are represented with the bold lines and the vertical bars for each of the psychological condition.


Study 2 - results

kNN SVM Naïve Bayes LDA

Archetypal experiences

Classification rate 78 82 85.5 83

Cross-validated classification rate 74 75.5 79.5 79.5

Explicit emotions

Classification rate 77.6 75.2 78.4 77.6

Cross-validated classification rate 72 71.2 74.4 74.4

Archetypal experiences and explicit emotions

Classification rate 62.8 55.7 72.6 69.8

Cross-validated classification rate 49.8 68.3 57.2 61.2

Classification Performance Achieved with Different Methods


Study 2 - results

Classification methods: k-nearest neighborhood (kNN), support vector machine (SVM), naïve Bayes, and linear discriminant analysis (LDA), Only the best accuracy is reported.

More information about ArcheSense can be found at http://hxresearch.org.

ArcheSense is a tool for evaluation of human experience with products or media based on physiological data of people.


A tool for evaluation of unconscious human experience

ArcheSense tool

http://hxresearch.org/�

Number of subjects: • 23

Two types of stimuli: • Film clips with explicit emotions • Film clips with archetypal experiences

Five categories of explicit stimuli: • Neutral • Active-pleasant • Active-unpleasant • Passive-pleasant • Passive-unpleasant

Physiological data: • ECG (HR + HRV) • Skin conductance (response + level)

Duration of each stimulus: • Approximately 1 minute

Number of stimuli per category: 3 Self-report ratings after every stimulus

Seven categories of archetypal stimuli: • Anima • Hero departure • Hero initiation • Hero rebirth • Hero return • Mentor • Mother • Shadow

Hypothesis: Various archetypal experiences lead to recognizable patterns of physiological activations that could be differentiated using computational intelligence algorithms.


Study 3 - method

Rain drops down

Reborn from Fire

Reborn from Fire and Thunder Strom

“God is in the rain.”


Categories of the film clips Number of states

Self-reports Physiological data

Anima, hero departure, hero initiation, hero rebirth, hero return, mentor, shadow

7 28.0 36.7

Anima, hero departure, mentor, shadow 4 42.0 53.3

Anima, hero initiation, mentor, shadow 4 43.1 57.1

Anima, hero rebirth, mentor, shadow 4 38.4 52.9 Anima, hero return, mentor shadow 4 40.6 56.1

Active-pleasant, active-unpleasant, neutral, passive-pleasant, passive-unpleasant

5 50.4 50.7

Active-unpleasant, neutral, passive-pleasant, passive-unpleasant

4 64.9 57.2

Classification methods: k-nearest neighborhood (kNN), support vector machine (SVM), naïve Bayes, linear discriminant analysis (LDA), and Adaptive Boosting with decision trees (AdaBoost). Only the best accuracy is reported.

Comparison of the classification accuracy achieved using the self-report questionnaires and the physiological data (between-subject classification).

Archetypes

Explicit emotions


Study 3 - results

Study #1: • ‘Exploratory’ study • Pictures and sounds (presentation for 6 sec) • 1 archetype, 4 explicit emotions • Between-subject design • Statistically significant results • 23.3% classification accuracy (5 classes)

Study #2: • Film clips (length 5 min) • 8 archetypes, 5 explicit emotions • 1 stimuli per class • Between-subject design • Statistically significant results • 79.5% classification accuracy (8 classes)

Study #3: • Film clips (length 1 min) • 7 archetypes, 5 explicit emotions • 3 stimuli per class • Between-subject and within-subject designs • Statistically significant results • 57.1% classification accuracy (4 classes, between-subject),

70.3% classification accuracy (7 classes, within-subject)


Overview

Performance of Predictive Models

Recognition Technique

Affective Stimuli Archetypal Symbols Explicit Emotions

Self-report Data (Conscious) Poor Superb

Physiological Data (Unconscious) Good Good

Classification Rate in General

Implicit Emotion Emotion toward Archetypal Symbols

State of the Art

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Three main conclusions can be drawn: (1) Conscious and unconscious reactions are different.

(2) The unconscious is sensitive to archetypes.

(3) The unconsciousness is at least important as the consciousness.


Thank you for your attention. A door goes open to a new world…


© Matthias Rauterberg, 2013 29/27

Chang H.M., Ivonin L., Díaz M., Català A., Chen W., Rauterberg M. (2014). Enacting archetypes in movies: Grounding the unconscious mind in emotion-driven media.Digital Creativity (published online) [IF=0.370] [SJR]. Chang HM., Ivonin L., Díaz M., Català A., Chen W., Rauterberg M. (2014). Unspoken emotions in movies: The basis of emotion-driven storytelling systems. Informatik-Spektrum, vol. 37, no. 6, pp. 539-546 (online) [IF=0.148] [SJR]. Ivonin L., Chang HM., Díaz M., Català A., Chen W., Rauterberg M. (2014). Beyond cognition and affect: Sensing the unconscious. Behaviour & Information Technology(published online) [IF=0.856] [SJR]. Chang H.M., Ivonin L., Díaz M., Català A., Chen W., Rauterberg M. (2013). From mythology to psychology: Identifying archetypal symbols in movies. Technoetic Arts: A Journal of Speculative Research, vol. 11, no. 2, pp. 99–113 [SJR]. Ivonin L., Chang H.M., Chen W., Rauterberg M. (2013). Unconscious emotions: Quantifying and logging something we are not aware of. Personal and Ubiquitous Computing, vol. 17, no. 4, pp. 663-673 [IF=2.853] [SJR]. Chang H.M., Diaz M., Catala A., Chen W., Rauterberg M. (2014). Mood boards as a universal tool for investigating emotional experience. In: A. Marcus (ed.), Design, User Experience and Usability - DUXU 2014, (Part IV, Lecture Notes in Computer Science, vol. 8520, pp. 220–231), Berlin - Heidelberg New York: Springer. Chang H.M., Ivonin L., Diaz M., Catala A., Chen W., Rauterberg M. (2013). Experience the world with archetypal symbols: A new form of aesthetics. In: N. Streitz, C. Stephanidis (eds.), Distributed, Ambient, and Pervasive Interactions DAPI - HCI international (Lecture Notes in Computer Science, vol. 8028, pp. 205–214), Berlin - Heidelberg: Springer [IF=0.499]. Chang H.M., Ivonin L., Diaz M., Catala A., Chen W., Rauterberg M. (2013). What do we feel about archetypes: Self-reports and physiological signals. In: Proceedings of 21st European Signal Processing Conference (September 9-13, 2013, Marrakech, Morocco; paper no. 1569742621). Chang H.M., Ivonin L., Chen W., Rauterberg M. (2013). Feeling something without knowing why: Measuring emotions toward archetypal content. In: M. Mancas, N. d’ Alessandro, X. Siebert, B. Gosselin, C. Valderrama, T. Dutoit (eds.), Proceedings of Intelligent Technologies for Interactive Entertainment - INTETAIN (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 124, pp. 22–31), © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering: Springer.

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