Introduction to ArtificiaI Intelligence in Higher Education
Hpai class 23 - emotion iii -051120
1. CIIC 5995-100 / ICOM 5995-100
Human Perspective in Artificial Intelligence
(HPAI)
Professor José Meléndez, PhD
“Thinking absent emotion – logical thinking – may be a useful
construct, but it is pure fiction.” - Dr. José Meléndez
2. Today
• Emotions III-IV
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3. Report
• Project Report & Software
• “Mini Mind Modules – Inner Robots & Bias”
• Subject to Due Dates Vote
• Due Friday May 15, 2020 by 11:59PM
4. Required Reading – Keep up the Pace
• Influence Tactics by Dr. George Simon Jr. (on Moodle)
• Excerpt of Chapter 6 of Character Disturbance: The
Phenomenon of Our Age
• The kinds of things we want AI to help us with.
• How Emotions are Made: The Secret Life of the Brain
• Chapter 6: How the Brain Makes Emotions
• Chapter 7: Emotions as Social Reality
• Chapter 8: A New View of Human Nature
• Chapter 9: Mastering Your Emotions
• Chapter 13: From Brain to Mind: The New Frontier
• The brain integrates, “so much information from multiple sources
so efficiently that it can support consciousness.”
5. Next Up
• Emotions
• Modeling Review
• A Traditional View
• In Decision Research
• In Artificial Intelligence Systems
https://time.com/3937351/consciousness-unconsciousness-brain/ (adapted)
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7. Thinking (rational & emotional)
• Integral Emotions are a result of or directly related to
the decision
• Incidental Emotions influence but are otherwise
unrelated to the decision
• Our thinking (rational & emotional) shapes our actions
9. Model Models – Towards Perspective
What about time?
What about emotional thinking?
10. Next Up
• Emotions
• A Traditional View
• In Decision Research
• In Artificial Intelligence Systems
11. Science of Emotion – Traditional View
• Emotions characterized by attributes:
• Something that “happens to” you
• “Flavors”: Positive, Negative, Neutral
• Eliciting or intentional object (aboutness)
• Enable pursuit of goals (serve function)
• Inhibit pursuit of goals
• Multi-component response
• Subjective (what it feels like)
• Body aspects (physiological including brain)
• Outward display of behavior
12. Aboutness – Traditional View
https://www.jstor.org/stable/20009513?seq=1#page_scan_tab_contents
13. Aboutness – Traditional View
https://www.jstor.org/stable/20009513?seq=1#page_scan_tab_contents
14. Aboutness – Traditional View
https://www.jstor.org/stable/20009513?seq=1#page_scan_tab_contents
15. Aboutness – Traditional View
https://www.jstor.org/stable/20009513?seq=1#page_scan_tab_contents
16. Aboutness – Traditional View
https://www.jstor.org/stable/20009513?seq=1#page_scan_tab_contents
17. Science of Emotion – Construction
• Emotions characterized by attributes:
• Something that “happens to” you construct.
• Affect “Flavors”: Positive, Negative, Neutral
• Eliciting or intentional object (aboutness)
• Enable pursuit of goals (serve function)
• Inhibit pursuit of goals
• Multi-component response
• Subjective (what it feels like)
• Body aspects (physiological including brain)
• Outward display of behavior is not a signature or “finger print”
• Thought/Cognition
18. Science of Emotion – Traditional View
• Emotions characterized by attributes:
• Something that “happens to” you
• “Flavors”: Positive, Negative, Neutral
• Eliciting or intentional object (aboutness)
• Enable pursuit of goals (serve function)
• Inhibit pursuit of goals
• Multi-component response
• Subjective (what it feels like)
• Body aspects (physiological including brain)
• Outward display of behavior
19. Science of Emotion – Construction
• Emotions characterized by attributes:
• Something that “happens to” you construct.
• Affect “Flavors”: Positive, Negative, Neutral
• Eliciting or intentional object (aboutness)
• Enable pursuit of goals (serve function)
• Inhibit pursuit of goals
• Multi-component response
• Subjective (what it feels like)
• Body aspects (physiological including brain)
• Outward display of behavior is not a signature or “finger print”
• Thought/Cognition
20. Emotion “Classification” – Traditional View
• Basic/Discrete
• Anger, Disgust, Fear, Happiness, Anger and Disgust
• Plus more “complex” emotion concept words
• Affective Circumplex
• Two Dimensional “State” (static - not time dependent)
• Valence (pleasant/unpleasant)
• Arousal (agitation/calmness)
• Primary classification systems limited to discrete or
steady-state responses.
• Akin to classifying your thoughts
• “Classification” of emotion is square peg in round hole
22. Affective Circumplex
• Flawed model of limited utility for Emotion Implementation
• Transforms diverse subjective concepts into subjective and
arbitrary dimensions (recall Feldman’s tribal studies)
• Requires to label emotions as good (pleasant) or bad
(unpleasant)
• Does not capture emotional space as continuous
• Creates false non-subjective, quantitative sense
• ”Low Arousal” is arbitrarily large negative quantity and not
approximately zero!!
How Emotions are Made, Figure 4-5
23. Emotion “Elicitation”
• Handbook of Emotion Elicitation and Assessment
• Tools & Methods to Elicit emotions
• Film clips (audio & visual) – reactivity, regulation,
understanding
• Static photos (visual) – Arousal and Valence “standard”
levels
• “Relived Emotions” – semi-structured of influence
• Autobiographical
• Shared memories (e.g. 9/11)
• Dyadic Interaction (“live”) – how you feel
34. Example: DEAP Data Set - Summary
• The DEAP dataset consists of two parts:
• The ratings from an online self-assessment where 120
one-minute extracts of music videos were each rated by
14-16 volunteers based on arousal, valence and
dominance.
• The participant ratings, physiological recordings and face
video of an experiment where 32 volunteers watched a
subset of 40 of the above music videos. EEG and
physiological signals were recorded and each participant
also rated the videos as above. For 22 participants
frontal face video was also recorded.
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
35. Example: DEAP Data Set - Files
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
36. DEAP Data Set – Online Ratings
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
37. DEAP Data Set – Elicitation Videos
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
38. DEAP Data Set – Participant Ratings
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
39. DEAP Data Set - Questionnaire
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
40. Example: DEAP Data Set - Files
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
41. Example: DEAP Data Set – File Details
https://www.researchgate.net/profile/Joseph_Erlichman/publication/230864997/figure/fig34/AS:341917163376655@1458530812418/Surface-map-of-EEG-electrode-locations.png
42. Example: DEAP Data Set - Files
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
43. Example: DEAP Data Set - Files
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
44. Example: DEAP Data Set – Data/Videos
https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
45. Next Up
• Emotions in Decision Research
• Emotions for Artificial Intelligence Systems