Computational studies of consciousness

1. Computational studies of consciousness 2009. 4. 2. Yonsei University Dept. of Computer Science Young-Seol, Lee

2. 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

3. Outline • Is an organism conscious? • Animal consciousness • Higher order thought • The kernel architecture, experience, sleep and dreaming • Application to machines • Summary and conclusions

4. 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

5. 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’

6. 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?

7. 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

8. 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)

9. 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

10. 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

11. 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

12. 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

13. 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

14. Current perspective on five axioms • 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

15. Current perspective on five axioms • 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

16. Current perspective on five axioms • 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

17. The mechanistic use of the axioms: the kernel architecture • 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

18. The mechanistic use of the axioms: the kernel architecture • 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

19. The mechanistic use of the axioms: the kernel architecture • 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

20. The mechanistic use of the axioms: the kernel architecture • Attention • The perceptual automaton • Top state showing both A and B is a hazy depiction

21. The mechanistic use of the axioms: the kernel architecture • 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

22. The kernel architecture and visual awareness • KA fits into a model of visual awareness

23. 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

24. 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

25. Unstable vision • The degree of puzzlement of Necker cube illusion

26. 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

27. 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

28. 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

29. 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

30. Summary • Depiction • A mix of processing sensory information with muscular information that encodes source information about the sensory information