towards the generation of visual qualia in artificial cognitive architectures
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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 - PowerPoint PPT PresentationTRANSCRIPT
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Towards the Generation of Visual Qualia in Artificial Cognitive Architectures
Raúl Arrabales, Agapito Ledezma, Araceli SanchisCarlos III University of Madrid
Computer Science Department
BRAIN INSPIRED COGNITIVE SYSTEMS14 – 16 July 2010, Madrid, Spain.
Third International ICSC Symposium onModels of Consciousness (MoC 2010)
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Contents Introduction Computational Model CERA-CRANIUM Experimental Setting Preliminary Results Conclusions Future Work
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Main Objective
Explore the possibility of specification of the content of visual qualia using a
computational model based on the Global Workspace Theory.
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The “redness” of red
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How are qualia generated?
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Qualia in Humans
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LIGHT
Retina
Spike Stream
Sensation
?
BRAINSENSES MIND
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Qualia in Humans
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LIGHT
Retina
Spike Stream
Sensation
?
BRAINSENSES MIND
How are sensations produced?
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The Mind-Body Problem
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MaterialObservable
ImmaterialPrivate
BRAIN MIND
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Dimensions of Consciousness
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Phenomenal Consciousness
Functional Consciousness
Respo
nses Stimuli
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Dimensions of Consciousness
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Phenomenal Consciousness
Functional Consciousness
Respo
nses Stimuli
Qualia
“Hard Problem”
“Easy Problems”
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Dimensions of Consciousness
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Phenomenal Consciousness
Functional Consciousness
Respo
nses Stimuli
Qualia
“Hard Problem”
“Easy Problems”
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What are qualia?
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Private
IneffableIntegrated
Structured
Qualia
Presence“The redness of red”
“Hard Problem”
Sensations
“A headache”
“The flavor of an ice-cream”
“Enjoying a song”
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Why are qualia so elusive?
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A B“Red” “Red”
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How to study phenomenology?
Heterophenomenology (Dennett, 1991).
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1st Person Observations
Qualia
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How to study phenomenology?
Heterophenomenology (Dennett, 1991).
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3rd Person Observations
2nd Person Observations
1st Person Observations
Inspection Report
Qualia
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How to study phenomenology?
Heterophenomenology (Dennett, 1991).
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3rd Person Observations
2nd Person Observations
1st Person Observations
Inspection Report
Qualia
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Machine Consciousness
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Human Consciousness Models
Machine Consciousness Models
Human Consciousness
Machine Consciousness
Analysis and Modeling
Design and Implementation
Comparison (Synthetic Phenomenology)
Adaptation to Computational
Models
PhysicalNeurophysiologicCognitive
Artificial Neural NetworksHybrid SystemsCognitive Architectures
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How to study phenomenology?
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2nd Person Observations
1st Person Observations
Report
Qualia
2nd Person Observations
1st Person Observations
Report
Qualia
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Contents Introduction Computational Model CERA-CRANIUM Experimental Setting Preliminary Results Conclusions Future Work
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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.
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Proposed Model
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Perceptual Content
Proprioceptive Sensing
Exteroceptive Sensing
Stage 1Perceptual
Content Representation
Stage 2Introspective
Perceptual Representation
Stage 3Self-Modulation and
Report
Sensory Data Visual Sensors(dot stimulus)
Somatosensory System
(sensor positions)
World Reconstruction
Introspection
Modulation / Reportability
Meta-Representation
Meta-Management
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Application to Visual Experience
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150 ms 150 ms10 ms 10 ms
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Proposed Model
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Perceptual Content
Proprioceptive Sensing
Exteroceptive Sensing
Stage 1Moving dot
Stage 2What is it like to see a moving dot
Stage 3I report to be watching
a moving dot
Sensory Data(left dot – blank – right
dot – blank)
Visual Sensors(dot stimulus)
Somatosensory System
(sensor positions)
World Reconstruction
Introspection
Modulation / Reportability
Meta-Representation
Meta-Management
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GWT Computational ModelGlobal Workspace Theory (Baars, 1988, 1997).
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WorkingMemory(Scene)
SpecializedProcessors(Audience)
Spotlight
Context Formation and Executive Guidance(Director, scene designer, etc. behind the scenes)
Interim coalition
BroadcastBroadcast
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Contents Introduction Computational Model CERA-CRANIUM Experimental Setting Preliminary Results Conclusions Future Work
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CERA-CRANIUM
A framework for experimentation with cognitive models of consciousness.
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CERA-CRANIUM
Agent
Model
Sensors Actuators
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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.
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CERA-CRANIUMMinimal Implementation
What should be the next action of the agent?What should be the next “conscious” content of the agent?
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Physical LayerMission-specific
LayerCore Layer
SensorServices
MotorServices
Sensors
Actuators
CERAROBOT
…
CRANIUM Workspace
Single Percepts
Complex Percepts
Sensor Service
Commands
Core L
ayer
CERA Viewer
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CERA-CRANIUM Observer
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CERA. Core Layer
(focus onsaliencies)
CERA. Physical Layer
Sensor Service
Sensor Service
Simple Percepts
…
CRANIUM Workspace
Complex Percepts
Pre-processors
…
Sensor Service
CERA. S-MSensors
…
Aggregators
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CERA-CRANIUM Observer
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CERA. Core Layer
(focus onsaliencies)
CERA. Physical Layer
Sensor Service
Sensor Service
Simple Percepts
…
CRANIUM Workspace
Complex Percepts
Pre-processors
…
Sensor Service
CERA. S-MSensors
…
Aggregators
Percept Aggregators
Complex Percepts
M(SCJ)
Simple Percepts
Sensor Preprocessors
Timer
Proprioception
N(δSj)N(δSJ)
j t
Sensor Readings
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CERA-CRANIUM Observer
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Working Memory(GW)
Raw Monomodal Sensory Data
“Spotlight”
Context Formation ProcessesCoordination Processes
Sensors
Control Signal
Asynchronous InputHigh Bandwidth
Sequential OutputLow Bandwidth
Context ArtificialQualia
Integrated Multimodal Representations
Specialized Processors
GOALS
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Contextualization
Bottom-Up: Native Spatiotemporal contexts.
Top Down: Specific contexts induced from the Core
Layer.
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Contextualization
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Contextualization
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Single Percepts
Complex Percept
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Contents Introduction Computational Model CERA-CRANIUM Experimental Setting Preliminary Results Conclusions Future Work
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Specialized Processors
Region of Interest detector for white objects.
Motion Detector.
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Visual Experience
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CERACRANIUM
Visual Stimuli Human
Observer
Robot Cam
“I see an object moving downwards”
CERA Viewer
Artificial Qualia
Specification
Content Specification can
be directly compared
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Visual Experience
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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.
CERACRANIUM
Visual Stimuli Human
Observer
Robot Cam
“I see an object moving downwards”
CERA Viewer
Artificial Qualia
Specification
Content Specification can
be directly compared
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Contents Introduction Computational Model CERA-CRANIUM Experimental Setting Preliminary Results Conclusions Future Work
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Preliminary Results
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(a)
“Objet resting on the ground”
(b)
“Object moving uniformly from the right to the
left”
“Ball moving back and forth from the left to
the right”
(c)
RDS SIMULATOR
SIMULATED CAM
CERA VIEWER
S1
S2
S3
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Contents Introduction Computational Model CERA-CRANIUM Experimental Setting Preliminary Results Conclusions Future Work
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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?
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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. …
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Thank you for your attention. Any questions?
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