Towards the Generation of Visual Qualia in Artificial Cognitive Architectures

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)

http://www.conscious-robots.com/index.php


www.Conscious-Robots.com

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.

3

4

The “redness” of red

5

How are qualia generated?


Qualia in Humans

6

LIGHT

Retina

Spike Stream

Sensation

?

BRAINSENSES MIND


Qualia in Humans

7

LIGHT

Retina

Spike Stream

Sensation

?

BRAINSENSES MIND

How are sensations produced?


The Mind-Body Problem

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MaterialObservable

ImmaterialPrivate

BRAIN MIND


Dimensions of Consciousness

9

Phenomenal Consciousness

Functional Consciousness

Respo

nses Stimuli



10



Respo

nses Stimuli

Qualia

“Hard Problem”

“Easy Problems”



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Respo

nses Stimuli

Qualia

“Hard Problem”

“Easy Problems”


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”

http://upload.wikimedia.org/wikipedia/commons/b/b2/Inverted_qualia_of_colour_strawberry.jpg


Why are qualia so elusive?

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A B“Red” “Red”



How to study phenomenology?

Heterophenomenology (Dennett, 1991).

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1st Person Observations

Qualia





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3rd Person Observations

2nd Person Observations


Inspection Report

Qualia





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3rd Person Observations



Inspection Report

Qualia


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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Report

Qualia



Report

Qualia




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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


Application to Visual Experience

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150 ms 150 ms10 ms 10 ms


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


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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CERA-CRANIUM

A framework for experimentation with cognitive models of consciousness.

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CERA-CRANIUM

Agent

Model

Sensors Actuators


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


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. 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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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


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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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


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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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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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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Towards the Generation of Visual Qualia in Artificial Cognitive Architectures

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