1. International Commun tion Association Johan F. Hoorn Vrije Universiteit Faculty of Sciences, Department of Computer Science Section Information Management & Software Engineering Subsection Human Computer Interaction, Multimedia & Culture jfhoorn@cs.vu.nl May 25, 2003 San Diego, CA Personification: Metaphor and Fictional Character in CMC www.cs.vu.nl/~jfhoorn

2. Personification, what is it? Theory

3. Personification Pierre Mignard (1694). Time Clipping Cupid’s Wings. Fictional character (Time, Cupid) used as a metaphor (Time is a man, Love is a boy) for an abstraction (Time, Love)

4. Personification Fictional character (Robby) used as a metaphor (Human is machine) for an abstraction (Help, Search, Navigate) Bill Gates (1997). Robby the Robot. Software agents can be personifications

5. No Personification Fictional character (Builder) used literally (Builder is a tutor) for an abstraction (Help, Instruct, Create) RealTimeAide (2003). Building tutor. http://www.realtimeaide.com/tutor/tutor.htm For this agent, the metaphoric aspect is missing

6. What’s the use of personification in CMC? Research question

7. Ease of understanding Fun Task relevance User support “Look and feel” Etc. User effortMotivation Literal icon/dialog Metaphoric icon/dialog Mediated person/ Fictional character (FC) Personification (FC plus metaphor) Should we apply personifications?

8. User effortMotivation Literal icon/dialog+ (easy) - (no fun) Metaphoric icon/dialog- (difficult) + (surprising) Mediated person/- (build a ++ (involve- Fictional character (FC) relationship) ment) Personification- -+++ (FC plus metaphor) Personification is more effort for more motivation?

9. Agents, what do they communicate? Theory

10. Agent-Mediated Communication SenderMessageReceiver System’s stakeholder (e.g., client, designer, manager) Fictional character End-user Goals: - instruct - persuade - entertain Goals: - be instructed - be persuaded - be entertained + metaphor Match?

11. Agent-Mediated Communication SenderMessageReceiver’s perspective Fictional character End-user + metaphor Human processing Goals: - instruct - persuade - entertain System’s stakeholder (e.g., client, designer, manager) PEFiC Metaphor process Support user goals? noyes Use agent Don’t use agent

12. Goals: - instruct - persuade - entertain Goals: - be instructed - be persuaded - be entertained Match? Receiver System’s stakeholder (e.g., client, designer, manager) End-user yes no Maintain agent Alter agent Message Support other goal? no yes Agent-Mediated Communication Sender’s perspective http://www.csc.ncsu.edu/eos/users/l/lester/www/images/IPA/cosmo_ok.gif

13. Agent-Mediated Communication SenderMessageReceiver’s perspective End-user Human processing PEFiC Perceiving and Experiencing Fictional Characters For empirical evidence, see and hear:

14. Characters, how are they processed? Results of other studies

15. Norm Epistemics Aesthetics Ethics good beautiful realistic bad ugly unrealistic Involvement Distance Appreciation dissimilar irrelevant negative valence similar relevant positive valence % % ENCODECOMPARERESPOND Features of situation and Fictional Character Identification, empathy, sympathy, warm feelings, approach, etc. Detachment, antipathy, cold feelings, avoidance, etc. Appraisal domains Mediators Fuzzy feature sets Subjective norm vs. group norm PEFiC model

16. http://www.scpcug.com/wmwand12.html Task-irrelevant features (goal ‘instruction’) Relevant features if goal is ‘entertainment’ Peedy Involvement Distance Example of PEFiC in action for factor Relevance to user goals

17. What is the role of epistemics? From character to metaphor

18. Agent-Mediated Communication MessageReceiver’s perspective End-user Human processing RMP Race model of Metaphor Processing For empirical evidence, see: Part of Epistemics

19. descriptivefigurativedescriptivefigurative realistic descriptivefigurativedescriptivefigurative literalmetaphor unrealistic ASSOCIATION COMMUNICATION FORM EPISTEMICS drooling feet constrained suit ‘tutor is a human’ ‘product presenter is a dog’’ ‘human is a machine’ ‘conversation partner is a human’ Metaphor is part of Epistemics http://www.ics.uci.edu/~kobsa/courses/ICS104/course-notes/metaphors.ht; http://www.techfak.uni-bielefeld.de/ags/wbski/lehre/digiSA/Methoden_der_KI/WS0102/methki15.pdf literalmetaphor drooling (too enthusiastic) (saliva)

20. Metaphors, how are they processed? Results of other studies

21. Category match? Sufficient descriptive AND descriptive/figurative intersection? Sufficient descriptive/figurative intersection? Calculate descriptive/figurative intersection Activate descriptive and figurative features Activate descriptive and figurative features ‘Anomaly’ ‘Metaphor’ ‘Literal’ no yes Calculate descriptive intersection Race model of Metaphor Processing humanmachine yes no EEG: N400 at frontal cortex feet constrained Cosmo

22. How come metaphors are harder to get but do not take more time? Discussion Errors are the answer

23. Sufficient descriptive/figurative intersection? ‘Metaphor’ ‘Literal’ noyes Response times for literal and metaphor are about equal. No way telling whether these two information sources are serial or parallel Problem: Calculate descriptive/figurative intersection Calculate descriptive intersection If serial (1 before 2), applying metaphor is more time consuming and probably, more difficult to understand If parallel, metaphor can be applied without losing time-efficiency and trouble of understanding (1)(2)

24. Investigate Lateralized Readiness Potential (LRP) in response to partial error pattern (after Coles et al., 1995) Solution: Calculate descriptive/figurative intersection Calculate descriptive intersection (1)(2) ‘Literal’ ‘Metaphor’ Few errors for ‘Metaphor’ Many errors for ‘Literal’  invisible in behavioral measures (e.g., RT) because they are corrected before response execution  visible in EEG Thus, speed is not the difficulty in metaphor but accuracy is For full argumentation, see:

25. ‘Metaphor’‘Literal’ Partial error ‘Literal’ LRP low LRP highCorrect ‘Metaphor’ Predictions for contralateral effects of finger movement during metaphor processing (fictitious data) stimulus response buttons motor cortex stimulus onset stimulus onset

26. Shall we apply personifications, then? User effortMotivation Literal icon/dialog+ (easy) - (no fun) Metaphoric icon/dialog- (difficult) + (surprising) Mediated person/- (do I like the ++ (personal Fictional character (FC) character?) -ized) Personification- -+++ (FC plus metaphor) PEFiCRMP Appreciation (Fun) Task relevance Valence (User support) Aesthetics (“Look and feel”) Ethics (Good bot vs. bad bot) Epistemics (Graphic rendering) Similarity (cf. Avatars) Involvement-distance N400 (surprise) Two information sources: - descriptive - descriptive/figurative Time efficiency Category mismatch Error prone (LRP) high Personification is more effort for more motivation

27. Future work We developed a software package for testing existing and newly created agents: Stimulus and trial production, RTs, and in the future, questionnaires and EEG extensions. Downloads: http://www.antbed.tk/

28. What is it? What can you do with it?

29. Create environments in PowerPoint and let the agent do its actions Action preview

30. Personification: Metaphor and Fictional Character in CMC THE END Wanna know more? Visit www.cs.vu.nl/~jfhoorn