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What is Self Awareness?This article offers one look at the notion of self awareness as it bearson the project of building cognitively sophisticated robots. A

companion article addresses some simple objections.• Introduction

• Picking Definitions of Self Awareness

• Self Models: What They Are

○ The Denser Approach

○ The Easier Approach

• Self Models: What Good Are They?

• Objections

Introduction

The single most significant point for dispelling the confusionswhich sometimes infest talk of self awareness is that it need havenothing whatsoever to do with consciousness. No single definitionof self awareness commands a dominant mind share in the specialistliterature, and it is true that it is sometimes conflated withconsciousness. But within the context of analysing cognition and of  building robots, we are safe in treating self awareness as a purely

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cognitive characteristic, while 'consciousness' refers to the phenomenal properties of mental processes. (For details, see theshort notes on consciousness or on qualia in the section containingtutorials & introductions.)

The relationships between (cognitive) self awareness and

(phenomenal) consciousness make interesting discussion topics, andthey live at the heart of a great deal of contemporary philosophy of mind. It may be, for instance, that achieving the cognitive trick of self awareness guarantees phenomenal consciousness, or it may bethat consciousness could even exist without any cognition at all.Fascinating though these questions are, it's beyond the scope of thisshort paper to flesh out positions on such relationships (see MindOut of Matter for some of that!). The important point for now issimply to recognise that the two are altogether different things.

Picking Definitions of Self Awareness

Precise definitions of self awareness vary wildly, but usually theyare picked to capture one or several aspects of the term's intuitivemeaning. The intuitive understanding of self awareness underlyingmost of the literature on the topic is bound up with the notion of anorganism using some concept of its own self -- as an item situatedwithin an environment -- to affect its behaviour. For instance,experiments show that most primate species are unable to recognisethemselves in mirrors. (Specifically, if a spot of paint is applied totheir forehead and the animals are then offered a mirror, they fail torespond to the image in the mirror by touching their forehead toinvestigate the spot; chimpanzees and humans are notableexceptions.) This, it has been suggested, indicates that such animals

lack self awareness -- even though they may perfectly well be phenomenally conscious. (I.e., they feel pain, see red, etc.) On thisview, because the animals apparently lack the ability to translate a picture in a mirror into information about themselves, they are not'self aware'.

In my own work, I actually tend to avoid this slant on self awareness. This psychologically inspired notion of self awarenessseems to me to place too much weight on an organism's capacity toconceptualise its self in a highly abstract fashion.

Instead, my principal interest centres on the question of whether anorganism (or robotic system) can be said to have a self at all,

whether or not it actively engages in abstract conceptualisation of that self. In Mind Out of Matter , I develop an information theoreticdescription of the self model , understood as a type of data structurewhich can reasonably be called the self of that which instantiates it.Self models capture some notion of 'self awareness' in the sense thatorganisms possessing self models display cognitive processes whichincline us to credit them with something like the intuitive notion of self awareness. As such, they offer one way of making precise that

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intuitive understanding of self awareness. But the notion is clearlymuch broader than that to which psychologists appeal in denyingself awareness to most primates. I would like to make two thingsexplicit:

• I take self models to be one cognitive route to self 

awareness, but not necessarily the only route; other architectures might also embody cognitive processes which justify our calling them self aware.

• The notion of a self model is a definition, but its role in realorganisms is an empirical hypothesis.

In other words, I am just defining a class of architectures whichyield some important features of the intuitive notion of self awareness, and I am hypothesising that such an architecturesubserves the type of cognitive processes which, in real organisms,would lead us to characterise those organisms as self aware.

Self Models: What They AreThe Denser Approach

Each 'weasel word' in the following paragraph will be spelled out below:

The self model is understood as a conditionally coupled,functionally active representation of the environmentally situatedsensory and motor system of which it is itself a part. (Perhaps thelabel 'self-in-a-world-as-it-looks-from-here model' is moredescriptive, since the dynamic data structure essentially reflects acentred perspective on the world and the system within it.) The self model functionally represents itself, the environment, and theinteractions between and within each.

The fundamental notion of information underlying notions like 'datastructure' is that of algorithmic information content. I takerepresentation to be a symmetric relationship obtaining betweentwo physical bodies when those bodies contain substantial mutualinformation content; alternatively, two items represent each other when they are not algorithmically independent. (For those with aninterest in the literature, I would draw this to your attention: Hey, anactual formal definition of representation!) Extending thedefinition, an item x  functionally represents another  y when itcontains substantial mutual information content both with y andwith a set of transformation axioms describing the range of changesin y for a given domain of conditions. Calling a representation or functional representation functionally active means that the relevant body of information plays a role as a representation in a functionalsystem. ('Functional system' can be another weasel word, but it is beyond the present scope. In Chapter 5 of Mind Out of Matter , Idefine a novel measure of process complexity called functional logical depth and use it to provide what is arguably the first precise

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and mathematically nontrivial account of functional systems.)Finally, a functionally active representation which is conditionallycoupled to that which is represented may temporarily becomeinformation theoretically 'disconnected' from that which it isrepresenting, in the sense that the actual state of that represented

may deviate from the information represented. The degree of suchuncoupling is quantified by the conditional information relation between the representation and the represented.

The Easier Approach

Here's the easy way to make sense of the above informationtheoretic look at self models.

The self model is a body of information about an organism and itsenvironment and about the flow of changes between and within thetwo. That body of information plays a central role in directing the behaviour of the organism. Unlike the case of a modern digitalcomputer, in which a very sophisticated central processing unitoperates on what are usually comparatively simpler data structures,the self model is a sophisticated data structure which affects theoperation of comparatively simpler (at the relevant level of description) biological components. The conditional coupling of theself model to the organism and the environment allows the organismto derive information about counterfactual conditions in itself or itsenvironment. In other words, because it contains information aboutthe interactions of changes between and within the organism and theenvironment, the self model can yield 'answers' to endogenouslygenerated questions about what would happen if current conditionswere other than they are. It is this capacity to test counterfactual

hypotheses which underlies what Popper famously described as acreature's ability to allow its hypotheses to die in its stead.

Self models are notably absent in all current artificial systems of which I am aware.

The capacities which self models subserve, however, are notablyabundant in the natural world. (Note that this does not necessarilyimply that self models themselves are similarly abundant; recall thatthe existence of self models is itself an empirical hypothesis.)

Self Models: What Good Are They?

So suppose one has a self model. What good is it?

The principal advantages of the self model derive from thePopperian notion described above: an organism may rehearsealternative courses of action without actually engaging in them, itmay model the viewpoint of its conspecifics by modelling itself inthe physical position of another individual, and so on. Within aselectionist context, the advantages of having a self model over nothaving one are, ceteris paribus, straightforward. How aboutrobotics, and machine cognition more generally?

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Creating machines with a sense of self is unexplored territory. Themachines we use today are under a sort of selective pressure, albeitof a fairly bland sort. Sometimes, high price/performance ratios winout, although installed base, software availability, and many other factors combine to press one or another variety of computer to the

fore and send others to extinction. Will the cognitive sophisticationso important for organisms competing in the biological realm proveuseful in the artificial realm as well? Just how would a machinewith a sense of self stack up? How might it exploit its 'self awareness' to get itself selected over the latest shiny PowerPC or  plain vanilla Pentium II?

One thing is almost certain: just as the appearance of biologicalorganisms with progressively richer notions of self radicallytransformed the selective biological environments within whichthey emerged, so too will the emergence of machines with a senseof self radically transform the selective dynamics of the

marketplace. While they will initially occupy the commercialequivalent of a novel and tiny environmental niche, I believe thateventually they will largely displace today's 'dumb' boxes, renderingthem a quaint bit of nostalgia.

By analogy to the biological world, today's computers have yet toclimb out of the primordial silicon soup. It took Nature 3.8 billionyears to create us, but I believe it will soon become clear that wecan move just a little faster than that when it comes to pushingmachine cognition up the 'evolutionary' scale.

Objections

Since it can be easy to misinterpret some of the statements abovesummarising the more careful and extended treatment of self modelsoffered in Mind Out of Matter , a separate short paper covers someof the simpler objections.

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