PhD Document - Universidad de Las Palmas de Gran Canaria

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Conscious Unconscious CHAPTER 2. THE CASE OF SOCIAL ROBOTICS Explicit cognition Implicit cognition Immediate memory Long-term memory Novel, informative, and significant events Routine, predictable, uninformative events Attended information Unattended information Focal contents Fringe contents (e.g. familiarity) Declarative memory (facts) Procedural memory (skills) Supraliminal stimulation Subliminal stimulation Effortful tasks Spontaneous/automatic tasks Remembering (recall) Knowing (recognition) Available memories Unavailable memories Strategic control Automatic control Grammatical strings Implicit underlying grammars Rehearsed items in Working Memory Unrehearsed items Explicit inferences Automatic inferences Episodic memory (autobiographical) Semantic memory (conceptual knowledge) Normal vision Blindsight (cortical blindness) Table 2.2: Some widely used euphemisms for conscious and unconscious phenomena (taken from [B.J. Baars, 2004]). atedness Hypothesis even proposes that generic intelligence evolves only after social intelligence has developed). In fact, unconscious processes are behind all or part of what we call social abilities, like face and language recognition (as opposed to other mental processes like for example solving a differential equation, which require conscious effort) [Bargh and Williams, 2006]. From [Wheatley and Wegner, 2001]: "Much of our behavior in social life is unconsciously automatic. There is evi- dence that people can respond automatically and unthinkingly to facial expres- sions, body gestures, hints about a person’s sex, ethnicity, or sexual orientation, information about someone’s hostility or cooperativeness, and a variety of other social stimuli. People also have unconscious automatic responses to things they like and dislike, from foods or books to ideas and social groups. ... Many of the automatic behaviors we do every day are things of which we are perfectly aware at the outset. We know we are getting in the car and heading off to work, for instance, or we know we are beginning to take a shower. Yet because we have done the act so often driving to work every day, showering every darn year, whether we need it or not we no longer need to think about the act after we have consciously launched it. These behaviors are often acquired skills, actions that become automatic only after significant repetition. 18
CHAPTER 2. THE CASE OF SOCIAL ROBOTICS When we begin to learn an action, such as driving, we think of the action at a very detailed level. We think ‘engage clutch, move gear shift down into second, lift left foot off the clutch and right foot onto gas.’ Skill acquisition starts off as labored, conscious learning and after consistent, frequent practice becomes more automatic and unconscious. Once the action is well learned, the behavior becomes automatic in the sense that it does not require constant conscious monitoring." At this point it seems clear that there must be a difference in the process involved in building precision manipulators or calculus machines and that involved in building a robot that can socialize with humans as we do. Note that many other problems in robotics and perception also suffer from our lack of conscious access to the mechanisms involved. How- ever, this effect is comparatively more accentuated in social robotics for, as explained before, social skills appear earlier in life. 2.3 The Reason why Social Robots may not be Robust When building a social robot, for the most advanced abilities (face detection, face recognition, speech recognition,...) researchers normally resort to machine learning techniques and, particularly, to supervised learning. Machine learning studies computer algorithms that improve automatically through experience [Mitchell, 1997]. In supervised learning, given a training set (examples), algorithms can be built for providing outputs for novel samples (i.e. different from those in the training set). This capacity to learn from experience results in a system that can offer increased efficiency and effectiveness. Most importantly, it is the most appropriate option for cases in which we can not think of a concise relationship between in- puts and outputs but there are many labelled examples available from which something can be learned. Learning algorithms use the available training samples to guide a search for a solution in a hypothesis space Hi. A robust solution has to predict, i.e. produce the correct output for future, unseen samples. Training samples alone are not sufficient for this, for there is a large number of possible hypotheses that fit the training set. This is the essential idea behind Wolpert’s No Free Lunch theorem (see [Wolpert, 1996]), which states that, on the criterion of prediction performance, there are no reasons to prefer the hypotheses selected by one learning algorithm over those of another. The perfect fit to a training set does not guarantee low error for future, unseen sam- 19
Page 1: Departamento de Informática y Sist
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Page 10 and 11: Gobierno de Canarias, Ministerio de
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Page 15 and 16: List of Figures 1.1 Abilities vs. l
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CHAPTER 5. PERCEPTION Figure 5.10:
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CHAPTER 5. PERCEPTION 5.3 Audio-Vis
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CHAPTER 5. PERCEPTION modules, inst
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CHAPTER 5. PERCEPTION each angle we
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CHAPTER 5. PERCEPTION hypothesis is
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CHAPTER 5. PERCEPTION M4.- Pattern
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CHAPTER 5. PERCEPTION nods and shak
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CHAPTER 5. PERCEPTION effect of thi
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CHAPTER 5. PERCEPTION effects of am
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CHAPTER 5. PERCEPTION image decreas
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CHAPTER 5. PERCEPTION with an incre
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CHAPTER 5. PERCEPTION However, we a
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CHAPTER 5. PERCEPTION where y0 is t
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CHAPTER 5. PERCEPTION be distinguis
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CHAPTER 5. PERCEPTION in the sense
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CHAPTER 5. PERCEPTION a) b) c) d) F
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CHAPTER 5. PERCEPTION the habituati
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Chapter 6 Action "CHRISTOF: We’ve
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CHAPTER 6. ACTION Expression: "Surp
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CHAPTER 6. ACTION 6.1.3 Implementat
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CHAPTER 6. ACTION Figure 6.3: Facia
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CHAPTER 6. ACTION c α error 0 o 0.
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CHAPTER 6. ACTION fear anger sorrow
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CHAPTER 6. ACTION However, we belie
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Chapter 7 Behaviour "A good name, l
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CHAPTER 7. BEHAVIOUR used defines t
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CHAPTER 7. BEHAVIOUR step environme
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CHAPTER 7. BEHAVIOUR Predicate Desc
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CHAPTER 7. BEHAVIOUR Action Descrip
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CHAPTER 7. BEHAVIOUR Name Action to
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CHAPTER 7. BEHAVIOUR ➔ Basic emot
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CHAPTER 7. BEHAVIOUR [Reilly, 1996]
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Chapter 8 Evaluation and Discussion
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CHAPTER 8. EVALUATION AND DISCUSSIO
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Chapter 9 Conclusions and Future Wo
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CHAPTER 9. CONCLUSIONS AND FUTURE W
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Appendix A CASIMIRO’s Phrase File
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APPENDIX A. CASIMIRO’S PHRASE FIL
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Bibliography [Adams et al., 2000] B
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BIBLIOGRAPHY [Bruce et al., 2001] A
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BIBLIOGRAPHY [Driscoll et al., 1998
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BIBLIOGRAPHY [Hernández et al., 20
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BIBLIOGRAPHY [Kjeldsen, 2001] R. Kj
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BIBLIOGRAPHY [Martin et al., 2000]
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BIBLIOGRAPHY [Oviatt, 2000] S. Ovia
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BIBLIOGRAPHY [Schulte et al., 1999]
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BIBLIOGRAPHY [Tyrrell, 1993] T. Tyr
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Index absolute pitch, 16 action sel
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INDEX proprioception, 39 prosopagno
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