Computational studies of consciousness

2009. 4. 2.

Yonsei UniversityDept. of Computer Science

Young-Seol, Lee

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Outline

• Introduction and overview

• Some history and those involved

• The aims of computational models of consciousness

• The challenge of phenomenology: is there a hard problem?

• Depiction: the key mechanism for conscious representation

• Current Perspectives on five axioms

• The mechanistic use of the axioms: the kernel architecture

• The kernel architecture and visual awareness

• Unstable vision

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Outline

• Is an organism conscious?

• Animal consciousness

• Higher order thought

• The kernel architecture, experience, sleep and dreaming

• Application to machines

• Summary and conclusions

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Introduction and Overview

•Professional philosopher’s perspective– The link between computational material and consciousness

• Difficult and puzzling topic

– Making machines that are conscious like ourselves• Particularly discourage

– Computational modeling• The part of the philosophical process of understanding

•Computational studies– Extracting the physical features that support a real material

• Ex) a real hurricane in a real world

– Creating it in a virtual world (in computer)• Ex) a virtual hurricane in a virtual world

– The purpose of virtual hurricane in a virtual world• How a real hurricane will behave in a real world

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Introduction and Overview

•Hurricane → consciousness– Extracting physical features that support real consciousness

• Real consciousness in a real brain

– Creating it in a virtual world (in computer)• Creating virtual consciousness in a virtual world

– Physical features of consciousness → difficult

•Aim of the paper– Designing a machine that will be ‘conscious of a real world in

the way we are’is not aim– Discussion of the very first step of extracting important

mechanisms necessary for the building of a virtual machine– Breaking consciousness down

• Five distinct ‘axioms’to facilitate the design process

– Looking at the current nature of the paradigm of ‘Macine Consciousness’

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Introduction and Overview

•Discussion of some important puzzles related to the mechanisms of consciousness

– Why does vision play tricks sometimes?– How does one check for the presence of consciousness?– Are animals conscious?– Is there higher order consciousness?– What happens in unconscious moments?

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Some history and those involved

• International conference on Artificial Neural Networks (1992)– The discovery of the neural correlates of consciousness

• Suggestion of future challenge for neural network researchers

•A small workshop sponsored by Swartz foundation (2001, May)

– The paradigm of machine consciousness• Establishment among international workers• “one day computers or robots could be conscious. … we know

of no fundamental law or principle operating in this universe that forbids the existence of subjective feelings in artifacts designed or evolved by humans”

•Franklin (2003)– Intelligent distribution agent based on Baars’Global workspace

• Creating a system the users of which believe that they are dealing with an entity that is aware of their needs

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Some history and those involved

•Franklin (2003)– Intelligent distribution agent based on Baars’Global workspace

• A ‘functional’approach to models which create systems that appear to be conscious through their behavior

•Aleksander and Dunmall (2003)– ‘phenomenological’ approaches

• Concerning mechanisms that may be needed to generate internal sensations

•Major issues at conferences since 2003– Association for the Scientific Study of Consciousness (ASSC)– Artificial Intelligence and Simulation of Behavior (AISB)

• The design of conscious machines (2006)• Machines with imagination (2005)• The generation of a synthetic phenomenology (2006)

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The aims of computational models of consciousness

•Extraction of the essential physical features of consciousness– Hypothesising such physical features using an axiomatic

decomposition– Desired virtual organism conscious of a virtual world – Assessment of what this tells us about a real organism that is

conscious of a real world

•Motivation for machine models of consciousness– To understand in a constructive way– To be able to discuss formally some puzzling features of

consciousness• unstable vision, illusions, test for being consciousness,

unconsciousness …

– To encourage those who work with conscious organisms to face the complexity of the brain in a formal way

– To ask if implemented systems have new uses

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The aims of computational models of consciousness

•Consciousness (in this paper)– A definite product of the brain with five axiomatic features

• Presence, imagination, attention, planning and emotions

•Technological basis of this work– Neural architectures studied with a computationally efficient

model of the neuron– Kernel architecture (KA)

• Ensemble of five axiomatic features• Central mechanism that support the axioms → Depiction

– Sensory pathways in the brain– Signals from the musculature of the body

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The challenge of phenomenology: is there a hard problem?

•Phenomenology– Personal, internal feelings of being conscious– Introspection will be the starting point for computational

studies

•Implication for computational studies– A link between experienced conscious states and observable

states of some underlying physical computational mechanism– Computation (at this paper)

• State development of an architecture that is controlled• The prototype model for such an architecture

– Living brain

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The challenge of phenomenology: is there a hard problem?

•Implication for computational studies– Assertion 1

• To include phenomenology in a computational model of consciousness it is necessary to abstract the physical/informational properties of the brain that are hypothesised to determine conscious states and study these as structures that are virtual on a conventional computer architecture in order to check the validity of the hypotheses

•Virtual architecture which is brain-like in broad essence– Chalmers (1996)

• While work on the physical may be very interesting, it does not lead to an understanding of the phenomenological

– Consciousness is free from physical process– To understand phenomenology

• Start with an organism that is phenomenologically conscious

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Depiction: the key mechanism for conscious representation

•Depiction– A direct representation of where elements of the world are in

the world which is encoded by the efforts of the mechanism to attend to such elements

•Assertion 2– The parts of a mechanism that sustain conscious experience

can only do so if they are the product of a depictive process

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Current perspective on five axioms

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•Dividing conscious experience into five axioms– Following introspective route– Aleksander and Dunmall (2003)

•Presence– I feel that I am an entity in world that is outside of me

• The feeling of being an empowered, active agent in a real sensory world

•Imagination– I can recall previous sensory experience as a more or less

degraded version of that experience. Driven by language I can imagine experiences I never had

• Creating scenarios that might have been experienced or even ones that are not close to reality

Current perspective on five axioms

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•Attention– I am selectively conscious of the world outside of me and can

select sensory events I wish to imagine• Sometimes what we choose to experience is automatic• Ex) bright flash, sound bang, etc.• Ex) identifying the make of a car

– Bonnet, wheels, rear for seeing an emblem

•Volition (previously called Planning)– I can imagine the results of taking actions and select an

action I wish to take• Imagine taking the actions in succession• Prediction of the ensuing results even if they may not be very

clear• Ex) choosing between going to a restaurant we know and

going to a previously untried restaurant

Current perspective on five axioms

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•Emotion– I evaluate events and the expected results of actions

according to criteria usually called emotions– Decision about which restaurant will be chosen → evaluation– Ex) eating stake in a steak house

• Pleasure of the experience• Negative feeling about the intake of cholesterol

– Ex) unknown restaurant• A fear of the unknown• Excitement of a new adventure

The mechanistic use of the axioms: the kernel architecture

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•Next question– How they(fix axioms) can interlock to provide a sensation of a

unified consciousness ?

•Kernel Architecture (KA)– An abstract physical structure composed of neural areas– The word‘kernel’

• It appears as part of many models that have been implemented in the past as is seen below

The mechanistic use of the axioms: the kernel architecture

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•Presence– Input of the neuron

• Sensory signal• Some frame of reference in the world

– Output of the neuron• Representative of where an element of the sensory world is

located

– Ex) binding problem• A small moving green triangle stimulates representational

neurons in different parts of the visual cortex– A color area, a motion area and a shape area– How this integrates to provide a coherent sensation ?

• The three separate neurons (each related to 3 kinds)– The sensation is one of overlap

– Perceptual module in KA• Support mechanisms for Axiom 1

The mechanistic use of the axioms: the kernel architecture

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•Imagination– An example of the simplest imaginational act

• Looking at an object in each side of the room– Left (object A), Right (object B)

– Input of the neuron• The depiction in the perceptual module

– Iconic learning• Transfer of input pattern Ai to the state variables of the

memory module to become Ai’

• Ai and Ai’ need not be exactly same– Memory is less vivid than perception

The mechanistic use of the axioms: the kernel architecture

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•Attention– The perceptual automaton– Top state showing both A and B is a hazy depiction

The mechanistic use of the axioms: the kernel architecture

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•Volition and Emotion– Simple scenario (restaurant)

• P1 : depiction of the menu• A1 : a memory of the first item (pizza)• R1 : experience of eating the pizza• E1j, e1k : Emotions accompanied the experience • Wantedness value : activates action modules

The kernel architecture and visual awareness

•KA fits into a model of visual awareness

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Unstable vision

•Well-known ‘Necker cube’– A ‘reversible’ figure

• Feature A is in front of B (or situation reverse)• Once reversal is experienced can neither stop it nor control it

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Unstable vision

•Well-known ‘Necker cube’– KA

• Two major pathways between input and eventual muscular action

– Ventral pathway and dorsal one• Ventral path

– Carrying signals from visual input that identify what is seen

• Dorsal path– A control input to action areas of the brain

– Participants with lesioned ventral pathway• Generating appropriate actions in response to visual input• Without reporting any awareness of the visual event• ‘Blind sight’

– Dorsal pathway does not contribute to conscious sensation

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Unstable vision

•The degree of puzzlement of Necker cube illusion

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Is an organism conscious?

•Assertion 3– There is a prima facie case for an organism to be conscious if

it has a system that is isomorphic with the Kernel Architecture hence support the five axiomatic mechanisms

•Necessary, not sufficient

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Animal consciousness

•Finding KA in brain– Concomitant with being conscious as – Ka may clearly be seen in invertebrates such as bees

• But not in amoebae

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Higher order thought

•High order thought (Rosenthal, 1993)– A sensation becomes conscious only if it is accompanied by a

higher level awareness that the perception is happening– In HOT theory, an organism has conscious thoughts only if

they occur at two levels

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Summary

•Studying philosophical theme of consciousness– Using computer modeling– Assumption

• Consciousness arises from the machinery of the brain and that computational work

•Phenomenological approach– Explicit representation of personal sensation– Decomposition of consciousness

• Five axioms– Presence in the world– Imagined existence– Choice of experience through attention– Volition and emotion

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Summary

•Depiction– A mix of processing sensory information with muscular

information that encodes source information about the sensory information

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