1. A Mechanism for Learning, Attention Switching, and Consciousness Janusz Starzyk School of Electrical Engineering and Computer Science, Ohio University, USA http://people.ohio.edu/starzykj October 20, 2010.

2.  Attention  Biological perspective  Emergence of consciousness  Functional requirements  Computational model of consciousness  Attention switching  Mental saccades  Implications  Summary Outline Photo: https://www.adbusters.org/magazine/87/philosophy-zero-point.html

3.  How a single thought emerges in your brain?  What motivates you to learn or do anything? Big Questions Photo: http://tsvetankapetrova.wordpress.com/2009/06/30/5-setbacks-that-stop-you-big-time/  How can you switch your attention from one activity to another?  What is necessary for cognition, intelligence, and consciousness?  These are but few questions important to philosophers, cognitive neuroscientists, psychologists, artificial intelligence researchers, etc.

4.  Can computational models be provided that demonstrate some of these phenomena?  Can we make a practical use of them in autonomous machines working in real time in natural environments?  This talk will address some of these questions. Big Questions http://www.geneang.com/Presence_Healing,_LLC/Neuroscience_of_Consciousness.html

5. 5 Attention  The term attention is used when there is a clear voluntary act.  We ask people to pay attention and they can chose to do so or not.  Voluntary attention is involved in preparing and applying goal directed selection for stimuli and responses.

6. 6 Attention  Attention selects information for cognitive process  Selection is driven by perceptions, emotions, motivations and is under executive control.  Without flexible, voluntary attention, we would not be able to change behavior or deal with unexpected emergencies or opportunities.  Without stimulus-driven attention we would not be able to respond quickly to significant external events.  Thus we need both voluntary and automatic attention.

7. 7 Brain basis of attention  Attention can be based on internal goals (finding a friend in the crowd) or external environment (alarm, bright colors)  William James wrote that attention helps to:  Perceive  Conceive  Distinguish  Remember  Shorten reaction time  Attention to a location improves the accuracy and speed of detecting target at this location.

8. 8 Brain basis of attention  Maintaining attention against distraction requires a significant effort;  E.g. trying to study when your roommate plays a loud music  Thus mental effort comes from struggle between voluntary (goal driven) and automatic attention.

9. 9 Brain basis of consciousness  Conscious cognition is close to attention, but not the same.  You can tell people – please pay attention but not - please be conscious.  You may be aware (conscious) of reading this text but you may be not aware of the touch of your chair, gravitational forces, background conversation, your feelings for a friend, or your major life goals.  Consciousness is not just a passive experience of sensory inputs, but an active involvement and perception.  “Self "-related phenomena such as preference, self-recognition, reflection, and planning are central to an understanding of consciousness.

10. 10  Differences between conscious and unconscious phenomena Conscious Unconscious  1. Explicit cognition Implicit cognition  2. Immediate memory Longer term memory  3. Novel, informative, andRoutine, predictable, significant eventsand nonsignificant events  4. Attended information Unattended information  5. Focal contents Fringe contents (e.g., familiarity)  6. Declarative memoryProcedural memory (facts, etc.)(skills, etc.)  7. Effortful tasks Spontaneous/automatic tasks  8. Remembering (recall) Knowing (recognition)  9. Available memories Unavailable memories Consciousness

11. 11  Differences between conscious and unconscious phenomena Conscious Unconscious  10. Strategic control Automatic control  11. Grammatical strings Implicit underlying grammars  12. Rehearsed items inUnrehearsed items in Working Memory Working Memory  13. Wakefulness and Deep sleep, coma, sedation dreams (cortical arousal)(cortical slow waves)  14. Explicit inferences Automatic inferences  15. Episodic memory Semantic memory (autobiographical) (conceptual knowledge)  16. Intentional learning Incidental learning  17. Normal vision Blindsight (cortical blindness) Consciousness

12. Evolution and consciousness – appearance and evolution of consciousness Living BeingEvolutionary traits Analogous feasibility in machines Human Beings  Fully developed cross-modal representation  Sensory capabilities: auditory, taste, touch, vision, etc.  Pre-frontal cortex: planning, thought, motivation Impossible at present Hedgehog (earliest mammals)  Cross-modal representation  Sensory capabilities: auditory, touch, vision (less developed), etc.  Small frontal cortex Impossible at present Birds  Primitive cross-modal representation  Sensory capabilities: auditory, touch, vision, olfactory.  Primitive associative memory Associative memories Photos: http://images.google.com/

13. Living BeingEvolutionary traits Analogous feasibility in machines Reptiles *  Olfactory system  Primitive vision Computer vision (emerging) Hagfish (early vertebrate)  Primitive olfactory system  Primitive nervous system Artificial neural networks Lower level animals (hydra, sponge, etc.)  Sensory motor units  Point to point nervous system Mechanical or electronic control systems * inconclusive\consciousness in transition Photos: http://images.google.com/ Evolution and consciousness – absence of consciousness

14. Emergence of Consciousness WeekHuman Fetus brain development 6Cortical cells come at the correct position 20Cortical region is insulated with myelin sheath 25Development of local connections between neurons 30Fetus’ brain generates electrical wave patterns Photos: http://daymix.com/Fetus-Brain-Development/ http://www.humanillnesses.com/Behavioral-Health-A-Br/The-Brain-and-Nervous-System.html?Comments[do]=mod&Comments[id]=1

15.  Brain is self-organizing and sparse Human Brain at Birth 6 Years Old 14 Years Old Rethinking the Brain, Families and Work Institute, Rima Shore, 1997. Emergence of Consciousness

16. Thompson, R. A., & Nelson, C. A. (2001). Developmental science and the media: Early brain development. American Psychologist, 56(1), 5-15. Synaptic Density over the Lifespan Conclusion : Consciousness emerges gradually

17. 17 1.Planning, setting goals and initiating actions 2.Monitoring outcomes and adapting to errors 3.Mental effort in pursuing difficult goals 4.Having motivations 5.Initiating speech and visual imagery 6.Recognizing other’s people’s goals 7.Engaging in social cooperation and competition 8.Feeling and regulating emotions 9.Storing and updating working memory 10.Active thinking 11.Enabling conscious experiences 12.Sustained attention in the face of distraction 13.Switching attention 14.Decision making and changing strategies 15.Planning and sequencing actions 16.Unifying the syntax and meaning of language 17.Resolving competition between plans csiwebcomics.com Frontal lobe functions

18.  Nobody has a slightest idea of how anything material can be conscious – Jerry Alan Fodor prof. of philosophy and cognitive science at Rutgers  The quality or state of being aware especially of something within oneself - Merriam Webster Dictionary  …our subjective experience or conscious state involving awareness, attention, and self reference – prof. Jeanette Norden neuroscientist in Vanderbilt.  Consciousness is a dynamic process and it changes with development of brain. Further, at macro-level there is no consciousness centre and at micro-level there are no committed neurons or genes dedicated to consciousness – prof. Susan Greenfield neuroscientist director of Royal Institution GB Description of Consciousness

19. 1. Conscious system is aware of past and present and is capable of critical analysis; 2. is aware of the environment in which it resides; 3. has a perception of its internal states 4. is capable to predict and explain current and past events; 5. is capable of autonomous construction of future actions; 6. can utilize past actions in the formulation of future plans: 7. is able to locate itself in its relationship to other entities; 8. can generate an internal representation of itself and its environment 9. is capable of autonomously and selectively directing its attention to address current important situations. Conscious System Requirements

20. 20 Neural model for consciousness  A neural net architecture for attention and visual consciousness.  Visual information flows from V1 to areas V2-V4, and finally IT where objects are detected.  Each area has its inhibitory neurons to sharpen differences at that level.  Posterior parietal neurons (PP) bias visual neurons that detect the object in that spatial location.  Prefrontal neurons in area 46 are involved in voluntary attentional selection. Attention and conscious flows.

21. Proposed approach to machine consciousness  Define consciousness in functional terms  Identify minimum functional requirements for consciousness  Identify functional blocks, their roles, their inter-relationships  Propose a computational model of a conscious machine Photo: http://www.theglobalintelligencer.com/aug2007/fringe

22. Definition of Machine Consciousness Consciousness is attention driven cognitive perception motivations, thoughts, plans and action monitoring. A machine is conscious IFF besides ability to perceive, act, learn and remember, it has a central executive mechanism that controls all the processes (conscious or subconscious) of the machine; Photo: www.spectrum.ieee.org/.../biorobot11f-thumb.jpg

23.  Consciousness requires –Intelligence (ability) –Awareness (state)  Not necessary alive  How to model consciousness? Consciousness:

24.  Intelligence  Central executive  Attention and attention switching  Mental saccades  Cognitive perception  Cognitive action control Photo: http://eduspaces.net/csessums/weblog/11712.html Consciousness: functional requirements

25. Computational Model of Machine Consciousness Semantic memory Sensory processors Data encoders/ decoders Sensory units Motor skills Motor processors Data encoders/ decoders Motor units Emotions, rewards, and sub-cortical processing Attention switching Action monitoring Motivation and goal processor Planning and thinking Episodic memory Queuing and organization of episodes Episodic Memory & Learning Central Executive Sensory-motor Inspiration: human brain Photo (brain): http://www.scholarpedia.org/article/Neuronal_correlates_of_consciousness

26. 26 Sensory and Motor Hierarchies  Sensory and motor systems appear to be arranged in hierarchies with information flowing between each level of the sensory and motor hierarchies.

27. Sensory- Motor Block Semantic memory Sensory processors Data encoders/ decoders Sensory units Motor skills Motor processors Data encoders/ decoders Motor units Emotions, rewards, and sub-cortical processing Sensory-motor  sensory processors integrated with semantic memory  motor processors integrated with motor skills  sub-cortical processors integrated with emotions and rewards

28. Central Executive  Platform for the emergence, control, and manifestation of consciousness  Controls its conscious and subconscious processes  Is driven by  attention switching  learning mechanism  creation and selection of motivations and goals ahsmail.uwaterloo.ca/kin356/cexec/cexec.htm

29. Attention switching Action monitoring Motivation and goal processor Planning and thinking Central Executive  Tasks o cognitive perception o attention o attention switching o motivation o goal creation and selection o thoughts o planning o learning, etc. Central Executive

30.  Interacts with other units for o performing its tasks o gathering data o giving directions to other units  No clearly identified decision center  Decisions are influenced by o competing signals representing motivations, pains, desires, plans, and interrupt signals need not be cognitive or consciously realized o competition can be interrupted by attention switching signal Attention switching Action monitoring Motivation and goal processor Planning and thinking Central Executive

31.  Attention  is a selective process of cognitive perception, action and other cognitive experiences like thoughts, action planning, expectations, dreams  Attention switching  is needed to have a cognitive experience  leads to sequences of cognitive experiences Comic: http://lonewolflibrarian.wordpress.com/2009/08/05/attention-and-distraction-what-are-you-paying-attention-to-08-05-09/ Attention Switching !!!

32.  Dynamic process resulting from competition between representations related to motivations sensory inputs internal thoughts including spurious signals (like noise). blog.gigoo.org/.../

33. Attention Switching !!! Thus, while paying attention is a conscious experience, switching attention does not have to be. May be a result of : deliberate cognitive experience (and thus fully conscious signal) subconscious process (stimulated by internal or external signals)

34. Mental Saccades  This in turn activates memory traces in the global workspace area that will be used for mental searches (mental saccades).  Selected part of the image resulting from an eye saccade.  Perceived input activates object recognition and associated areas of semantic and episodic memory.

35. Mental saccades in a conscious machine

36. Computational Model: Summary  Self-organizing mechanism of emerging motivations and other signals competing for attention is fundamental for conscious machines.  A central executive controls conscious and subconscious processes driven by its attention switching mechanism.  Attention switching is a dynamic process resulting from competition between representations, sensory inputs and internal thoughts  Mental saccades of the working memory are fundamental for cognitive thinking, attention switching, planning, and action monitoring Photo: http://www.prlog.org/10313829-homeless-man-earns-250000-after-viewing-prosperity-consciousness-video-subliminal-mind- training.html

37. Computational Model: Implications  Motivations for actions are physically distributed o competing signals are generated in various parts of machine’s mind  Before a winner is selected, machine does not interpret the meaning of the competing signals  Cognitive processing is predominantly sequential o winner of the internal competition is an instantaneous director of the cognitive thought process, before it is replaced by another winner  Top down activation for perception, planning, internal thought or motor functions o results in conscious experience decision of what is observed and where is it planning how to respond o a train of such experiences constitutes consciousness

38. Conclusions 1.Consciousness is computational 2.Intelligent machines can be conscious

39. Sounds like science fiction?  If you’re trying to look far ahead, and what you see seems like science fiction, it might be wrong.  But if it doesn’t seem like science fiction, it’s definitely wrong. From presentation by Feresight Institute

40. Questions ?? Photo: http://bajan.wordpress.com/2010/03/03/dont-blame-life-blame-the-way-how-you-live-it/

41. References  J. A. Fodor, "The big idea: can there be science of the mind," Times Literary Supplement, pp. 5-7, July 1992.  J. Norden, Understanding the brain, Video lecture series.  M. Velmans, "Where experiences are: Dualist, physicalist, enactive and reflexive accounts of phenomenal consciousness," Phenomenology and the Cognitive Sciences, vol. 6, pp. 547-563, 2007  A. Sloman, "Developing concept of consciousness," Behavioral and Brain Sciences, vol. 14 (4), pp. 694-695, Dec 1991.  W. H. Calvin and G. A. Ojemann, Conversation with Neil's brain: the neural nature of thought and language: Addison-Wesley, 1994.  J. Hawkins and S. Blakeslee, On intelligence. New York: Henry Holt & Company, LLC., 2004.  S. Greenfield, The private life of the brain. New York: John Wiley & Sons, Inc., 2000.  Nisargadatta, I am that. Bombay: Chetana Publishing, 1973.  D. C. Dennett, Consciousness Explained, Penguin Press,1993.  D. M. Rosenthal, The nature of Mind, Oxford University Press, 1991.  B. J. Baars “A cognitive theory of consciousness,” Cambridge University Press, 1998. Photo: http://s121.photobucket.com/albums/o209/TiTekty/?action=view&current=hist_sci_image1.jpg

42. Embodied Intelligence –Mechanism: biological, mechanical or virtual agent with embodied sensors and actuators –EI acts on environment and perceives its actions –Environment hostility is persistent and stimulates EI to act –Hostility: direct aggression, pain, scarce resources, etc –EI learns so it must have associative self-organizing memory –Knowledge is acquired by EI Definition  Embodied Intelligence (EI) is a mechanism that learns how to minimize hostility of its environment

43. Embodiment of a Mind  Embodiment is a part of the environment that EI controls to interact with the rest of the environment  It contains intelligence core and sensory motor interfaces under its control  Necessary for development of intelligence  Not necessarily constant or in the form of a physical body  Boundary transforms modifying brain’s self- determination

44.  Brain learns own body’s dynamic  Self-awareness is a result of identification with own embodiment  Embodiment can be extended by using tools and machines  Successful operation is a function of correct perception of environment and own embodiment Embodiment of a Mind

45. Motivated Learning  Various pains and external signals compete for attention.  Attention switching results from competition.  Cognitive perception is aided by winner of competition.  Definition: Motivated learning (ML) is pain based motivation, goal creation and learning in embodied agent.  Machine creates abstract goals based on the primitive pain signals.  It receives internal rewards for satisfying its goals (both primitive and abstract).  ML applies to EI working in a hostile environment.

46. Reinforcement Learning Motivated Learning  Single value function  Measurable rewards  Can be optimized  Predictable  Objectives set by designer  Maximizes the reward  Potentially unstable  Learning effort increases with complexity  Always active  Multiple value functions  One for each goal  Internal rewards  Cannot be optimized  Unpredictable  Sets its own objectives  Solves minimax problem  Always stable  Learns better in complex environment than RL  Acts when needed http://www.bradfordvts.co.uk/images/goal.jpg