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 ScanQR Code to Verify your Class Attendance https://forms.gle/newZj7do8D6KVPwz8 https://forms.gle/newZj7do8D6KVPwz8

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) ScanQR Code to Verify your Class Attendance https://forms.gle/newZj7do8D6KVPwz8 https://forms.gle/newZj7do8D6KVPwz8

6. Models of Intelligence Sheri Van Dijk, MSW

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

8. Towards a Perspective Model Example Only

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

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

21. Affective Circumplex How Emotions are Made, Figure 4-5

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

24. Example: Elicitation – Static Photos https://www.researchgate.net/figure/A-sample-of-standardized-images-classified-as-representing-each-basic-emotion-within-the_fig2_280312285

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

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

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

Hpai class 23 - emotion iii -051120

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