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Towards the Generation of Visual Qualia in Artificial Cognitive Architectures

BRAIN INSPIRED COGNITIVE SYSTEMS 14 – 16 July 2010, Madrid, Spain . Third International ICSC Symposium on Models of Consciousness ( MoC 2010). Towards the Generation of Visual Qualia in Artificial Cognitive Architectures Raúl Arrabales, Agapito Ledezma , Araceli Sanchis

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Towards the Generation of Visual Qualia in Artificial Cognitive Architectures

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  1. BRAIN INSPIRED COGNITIVE SYSTEMS14 – 16 July 2010, Madrid, Spain. Third International ICSC SymposiumonModels of Consciousness (MoC 2010) Towards the Generation of Visual Qualia in Artificial CognitiveArchitectures Raúl Arrabales, Agapito Ledezma, Araceli Sanchis Carlos III University of MadridComputerScienceDepartment

  2. Contents • Introduction • Computational Model • CERA-CRANIUM • Experimental Setting • Preliminary Results • Conclusions • Future Work

  3. Main Objective Explore the possibility of specification of the content of visual qualia using a computational model based on the Global Workspace Theory.

  4. The “redness” of red

  5. How are qualia generated?

  6. Qualia in Humans LIGHT Retina Sensation Spike Stream ? SENSES BRAIN MIND

  7. Qualia in Humans LIGHT How are sensations produced? Retina Sensation Spike Stream ? SENSES BRAIN MIND

  8. The Mind-Body Problem BRAIN MIND MaterialObservable ImmaterialPrivate

  9. Dimensions of Consciousness Phenomenal Consciousness Functional Consciousness Responses Stimuli

  10. Dimensions of Consciousness Phenomenal Consciousness Qualia “Hard Problem” Functional Consciousness “Easy Problems” Responses Stimuli

  11. Dimensions of Consciousness Phenomenal Consciousness Qualia “Hard Problem” Functional Consciousness “Easy Problems” Responses Stimuli

  12. What are qualia? Integrated Ineffable “Enjoying a song” Qualia Private Structured Presence “The redness of red” Sensations “Hard Problem” “The flavor of an ice-cream” “A headache”

  13. Why are qualia so elusive? “Red” “Red” A B

  14. How to study phenomenology? Heterophenomenology(Dennett, 1991). Qualia 1st Person Observations

  15. How to study phenomenology? Heterophenomenology(Dennett, 1991). Qualia 1st Person Observations Report Inspection 2nd Person Observations 3rd Person Observations

  16. How to study phenomenology? Heterophenomenology(Dennett, 1991). Qualia 1st Person Observations Report Inspection 2nd Person Observations 3rd Person Observations

  17. Machine Consciousness Human Consciousness Physical Neurophysiologic Cognitive Analysis and Modeling Human Consciousness Models Adaptation to Computational Models Comparison (Synthetic Phenomenology) Machine Consciousness Models Design and Implementation Artificial Neural Networks Hybrid Systems Cognitive Architectures Machine Consciousness

  18. How to study phenomenology? Qualia 1st Person Observations Report Qualia 2nd Person Observations 1st Person Observations Report 2nd Person Observations

  19. Contents • Introduction • Computational Model • CERA-CRANIUM • Experimental Setting • Preliminary Results • Conclusions • Future Work

  20. Working Hypotheses about Qualia • They are related to cognitive functions. • Their contents have a functional role. • They are the ultimate outcome of the perception process.

  21. Stage 3 Self-Modulation and Report Meta-Management Perceptual Content Stage 2 Introspective Perceptual Representation Meta-Representation Exteroceptive Sensing Proprioceptive Sensing Stage 1 Perceptual Content Representation Visual Sensors (dot stimulus) Somatosensory System (sensor positions) Sensory Data Modulation / Reportability World Reconstruction Introspection Proposed Model

  22. Application to Visual Experience 150 ms 10 ms 150 ms 10 ms

  23. Stage 3 I report to be watching a moving dot Meta-Management Perceptual Content Stage 2 Whatisitliketosee a movingdot Meta-Representation Exteroceptive Sensing Proprioceptive Sensing Stage 1 Moving dot Visual Sensors (dot stimulus) Somatosensory System (sensor positions) Sensory Data (left dot – blank – right dot – blank) Modulation / Reportability World Reconstruction Introspection Proposed Model

  24. Context Formation and Executive Guidance (Director, scene designer, etc. behind the scenes) WorkingMemory (Scene) Spotlight Broadcast Broadcast SpecializedProcessors (Audience) Interim coalition GWT Computational Model Global Workspace Theory (Baars, 1988, 1997).

  25. Contents • Introduction • Computational Model • CERA-CRANIUM • Experimental Setting • Preliminary Results • Conclusions • Future Work

  26. CERA-CRANIUM A framework for experimentation with cognitive models of consciousness. CERA-CRANIUM Model Sensors Actuators Agent

  27. CERA-CRANIUM • CERA (Conscious and Emotional Reasoning Architecture) • Layered Control Architecture • CRANIUM (Cognitive Robotics Architecture Neurologically Inspired Underlying Manager) • Runtime Environment for the creation and management of specialized processors sharing a global working memory.

  28. Core Layer ROBOT CERA Commands Sensor Service SensorServices Physical Layer Sensors Mission-specific Layer Single Percepts Complex Percepts MotorServices Core Layer Actuators CRANIUM Workspace … CERA-CRANIUMMinimal Implementation • What should be the next action of the agent? • What should be the next “conscious” content of the agent? CERA Viewer

  29. CRANIUM Workspace … CERA-CRANIUM Observer CERA. S-M CERA. Physical Layer CERA.Core Layer (focus onsaliencies) Sensors Sensor Service Sensor Service Complex Percepts Simple Percepts … … Sensor Service Pre-processors Aggregators

  30. Sensor Preprocessors Percept Aggregators CRANIUM Workspace … CERA-CRANIUM Observer CERA. S-M CERA. Physical Layer CERA.Core Layer (focus onsaliencies) Sensors Sensor Service Sensor Service Complex Percepts Simple Percepts … … Sensor Service Pre-processors Aggregators Sensor Readings Complex Percepts Simple Percepts t j N(δSj) M(SCJ) Timer N(δSJ) Proprioception

  31. Context Control Signal Context Formation ProcessesCoordination Processes GOALS Working Memory(GW) Sensors ArtificialQualia “Spotlight” Asynchronous InputHigh Bandwidth Sequential OutputLow Bandwidth Specialized Processors Integrated Multimodal Representations Raw Monomodal SensoryData CERA-CRANIUM Observer

  32. Contextualization • Bottom-Up: • Native Spatiotemporal contexts. • Top Down: • Specific contexts induced from the Core Layer.

  33. Contextualization

  34. Contextualization Single Percepts Complex Percept

  35. Contents • Introduction • Computational Model • CERA-CRANIUM • Experimental Setting • Preliminary Results • Conclusions • Future Work

  36. Specialized Processors • Region of Interest detector for white objects. • Motion Detector.

  37. Visual Stimuli “I see an object moving downwards” Human Observer Content Specification can be directly compared Artificial Qualia Specification Robot Cam CERA Viewer CERA CRANIUM Visual Experience

  38. Visual Stimuli “I see an object moving downwards” Human Observer Content Specification can be directly compared Artificial Qualia Specification Robot Cam CERA Viewer CERA CRANIUM Visual Experience • Visual Stimuli: • S1: Static white object in a dark background. • S2: White object moving along a rectilinear trajectory. • S3: Two stationary white blinking rounded spots.

  39. Contents • Introduction • Computational Model • CERA-CRANIUM • Experimental Setting • Preliminary Results • Conclusions • Future Work

  40. Preliminary Results (a) (b) RDS SIMULATOR “Object moving uniformly from the right to the left” S2 SIMULATED CAM S1 (c) “Ball moving back and forth from the left to the right” CERA VIEWER “Objet resting on the ground” S3

  41. Contents • Introduction • Computational Model • CERA-CRANIUM • Experimental Setting • Preliminary Results • Conclusions • Future Work

  42. Conclusions • Using GWT will shed light on whether or not the model can account for typical human perceptual effects. • Synthetic Phenomenology might help us understand qualia. • For instance: Does the presence of perceptual illusions correlates with better perception accuracy in noisy environments?

  43. Future Work • More complex stimuli. Multimodal stimuli. Real world scenarios. • Better specification and representation of the content of Artificial Qualia. • Improve the Cognitive Architecture: • Expectations. • Emotions. • …

  44. Thank you for your attention. Any questions?

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