Architectural Requirements for Consciousness Ron Chrisley Aaron Sloman Centre for Cognitive Science (COGS), Department of Computer Science Sackler Centre for Consciousness Science, University of Birmingham and Department of Informatics Birmingham, United Kingdom University of Sussex Email: axs@cs.bham.ac.uk Brighton, United Kingdom Email: ronc@sussex.ac.uk Abstract—This paper develops, in sections I-III, the virtual Our emphasis on beliefs concerning these four properties machine architecture approach to explaining certain features of (immediacy, privacy, intrinsicness and ineffability), follows consciousness first proposed in [1] and elaborated in [2], in which the analysis in [5] in taking these properties to be central particular qualitative aspects of experiences (qualia) are proposed to be particular kinds of properties of components of virtual to the concept of quale or qualia. But whereas [5] under- machine states of a cognitive architecture. Specifically, they are stands this centrality to imply that the properties themselves those properties of components of virtual machine states of an are conditions for falling under the concept, we understand agent that make that agent prone to believe the kinds of things their centrality only in their role of causally determining the that are typically believed to be true of qualia (e.g., that they are reference of the concept. Roughly, qualia are not whatever has ineffable, immediate, intrinsic, and private). Section IV aims to make it intelligible how the requirements identified in sections II those four properties; rather, qualia are whatever is (or was) and III could be realised in a grounded, sensorimotor, cognitive the cause of our qualia talk. And if we do know anything robotic architecture. about the cause of our qualia talk, it is this: it makes us prone to believe that we are in states that have those four properties. I. I NTRODUCTION A crucial component of our explanation, which we call the Those who resist the idea of a computational, functional, Virtual Machine Functionalism (VMF) account of qualia, is or architectural explanation of consciousness will most likely that the propositions 1-4 need not be true in order for qualia concede that many aspects surrounding consciousness are so to make A prone to believe those propositions. In fact, it is explicable (the so-called “easy problems” of consciousness arguable that nothing could possibly render all of 1-4 true [3]), but maintain that there are core aspects of conscious- simultaneously [5]. But on our view, this would not imply ness having to do with phenomenality, subjectivity, etc. for that there are no qualia, since for qualia to exist it is only which it is Hard to see how a computational explanation required that that agents that have them be prone to believe could proceed. A typical way of characterising this “Hard 1-4, which can be the case even when some or all of 1-4 are core” of consciousness employs the concept of qualia: “the false. introspectively accessible, phenomenal aspects of our mental It is an open empirical question whether, in some or all lives” [4]. Surely there can be no computational explanation humans, the properties underlying the dispositions to believe of qualia? 1-4 have a unified, systematic structure that would make them This paper develops the virtual machine architecture ap- a single cause, and that would thereby make reference to proach to explaining certain features of consciousness first pro- them a useful move in providing a causal explanation of posed in [1] and elaborated in [2], in which qualia, understood such beliefs. Is “qualia” more like “gold”, for which there as particular qualitative aspects of experiences, are proposed was a well-defined substance that was the source of mistaken, to be particular kinds of properties of components of virtual alchemical talk and beliefs about gold? Or is “qualia” more machine states of a cognitive architecture. Specifically, they like “phlogiston”, in that there is no element that can be iden- are those properties of components of virtual machine states tified as the cause of the alchemists’ mistaken talk and beliefs of agent A that make A prone to believe: that they expressed using the world “phlogiston”? These are 1) That A is in a state S, the aspects of which are knowable empirical questions; thus, according to the VMF account of by A directly, without further evidence (immediacy); qualia, it is an open empirical question whether qualia exist 2) That A’s knowledge of these aspects is of a kind such in any particular human. By the same token, however, it is that only A could have such knowledge of those aspects an open engineering question whether, independently of the (privacy); human case, it is possible or feasible to design an artificial 3) That these states have these aspects intrinsically, not by system that a) is also prone to believe 1-4 and b) is so virtue of, e.g., their functional role (intrinsicness); disposed because of a unified, single cause. Thus, it is an 4) That these aspects of S cannot be completely commu- open engineering question whether an artificial system can be nicated to an agent that is not A (ineffability). constructed to have qualia. This paper goes some way toward Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 31 getting clear on how one would determine the answer to that propensity to believe it; some alternative explanations engineering question. must be offered, but it is not possible to do so here. Section II notes the general requirements that must be IV. C OGNITIVE A RCHITECTURE , in place for a system to believe 1-4, and then sketches NOT C OGNITIVIST A RCHITECTURE ? very briefly, in section III, an abstract design in which the propensities to believe 1-4 can be traced to a unified virtual Given the anti-cognitivist, anti-representational, anti- machine structure, underwriting talk of such a system having symbolic, embodied, enactivist, etc. inclinations of many in the qualia. EUCognition community, the foregoing may be hard to accept given its free use of representational and computational notions II. G ENERAL ARCHITECTURAL REQUIREMENTS FOR such as belief, deliberation, justification, etc. The rest of this HAVING QUALIA paper, then, is an attempt at an in-principle sketch of how General requirements for meeting constraints 1-4 include one can have a grounded, dynamic, embodied, enactive(ish) being a system that can be said to have beliefs and propensities cognitive architecture that nevertheless supports the notions to believe, as well as what those properties themselves require. of belief, inference, meta-belief, etc. that this paper has just Further, having the propensities to believe 1-4 in particu- maintained are necessary for the subjective, qualia aspect of lar requires the possibility of having beliefs about oneself, consciousness, if not all aspects of consciousness. one’s knowledge, about possibility/impossibility, and other This motivation is not strictly (that is, philosophically) minds. At a minimum, such constraints require a cognitive required, for two reasons: architecture with reactive, deliberative and meta-management • First, our self-appointed philosophical opponents do not components [1], with at least two layers of meta-cognition: claim that the “easy problems” of consciousness can- (i) detection and use of various states of internal virtual not be solved physicalistically, or even computationally. machine components; and (ii) holding beliefs/theories about Thus, in giving our explanation of the “Hard core” of those components. consciousness, qualia, we can help ourselves to any of the capacities that are considered to fall under the “easy III. A QUALIA - SUPPORTING DESIGN problems”, which is the case for all of the requirements A little more can be said about the requirements that 1-4 we identified in sections II and III. might impose on a cognitive architecture. • Second, an aspect a of a cognitive architecture A can be 1) A propensity to believe in immediacy (1) can be ex- of the same kind as an aspect b of a distinct cognitive plained in part as the result of the meta-management architecture B, even if B is capable of the sorts of layer of a deliberating/justifying but resource-bounded beliefs mentioned in 1-4 because of possessing b, and architecture needing a basis for terminating delibera- A, despite having a, is not capable of having those sorts tion/justification in a way that doesn’t itself prompt of beliefs. On our account, A might still have qualia by further deliberation or justification. virtue of having a; this is why our account does not, 2) A propensity to believe in privacy (2) can be explained in despite appearances, over-intellectualize qualia, and is part as the result of a propensity to believe in immediacy instead consistent with, e.g., the empirical possibility that (1), along with a policy of normally conceiving of the animals and infants have qualia. beliefs of others as making evidential and justificatory However, showing how architectures that do have the kinds impact on one’s own beliefs. To permit the termination of beliefs mentioned in 1-4 can be constructed out of grounded of deliberation and justification, some means must be sensorimotor components is required if we are to achieve any found to discount, at some point, the relevance of understanding of what a system that is incapable of having others’ beliefs, and privacy provides prima facie rational those beliefs would have to be like for it to nevertheless grounds for doing this. warrant ascription of qualia. 3) A propensity to believe in intrinsicness (3) can also be This section (that is, the rest of this paper) will not explained in part as the result of a propensity to believe have much to say about consciousness or qualia per se. in immediacy, since states having the relevant aspects Furthermore, the sketched architectures are likely not optimal, non-intrinsically (i.e., by virtue of relational or systemic feasible, or even original. That there is some better way to facts) would be difficult to rectify with the belief that solve the task that we use for illustrative purposes below is one’s knowledge of these aspects does not require any not to the point. The architectures and task are intended merely (further) evidence. to act as a proof-of-concept, as a bridge between the kind of 4) An account of a propensity to believe in ineffability robotic systems that many in the EUCognition community are (4) requires some nuance, since unlike 1-3, 4 is in familiar or comfortable with, and the kind of robotic cognitive a sense true, given the causally indexical nature of architecture that we have argued is required for qualia. some virtual machine states and their properties, as explained in [2]. However, properly appreciating the A. Robotic architecture, environment and task truth of 4 requires philosophical sophistication, and so Consider a robot that is static except that it can move its its truth alone cannot explain the conceptually primitive single camera to fixate on points in a 2D field. The result Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 32 R of fixating on point (x, y) is that the sensors take on a could employ a kind of content-addressable search (following particular value s out of a range of possible values S. That is, ideas first presented in [6]). The difference between cue and R(x, y) = s ∈ S. E(x, y) (or between M (cue) and M (E(x, y)); see below) can The visual environment is populated by simple coloured be used as a differentiable error signal, permitting gradient polygons, at most one (but perhaps none) at each fixation point descent reduction of error not in weight w space, but in visual (x, y). This visual environment is static during trials, although space (which is here the same as fixation space and action it may change from trial to trial. space). That is (hereafter re-writing (x, y) as u), the robot can The robot has learned a map M that is a discrete partition of apply the Delta rule, changing u proportionally to the partial S into a set of categories or features F (e.g., a self-organising derivative of the error with respect to u: feature map): M (s) = fi ∈ F . In general, M is always applied to the current sensory input s, thus activating one of the feature nodes or vectors. For example, f1 might be active in those ∂[ 12 (cue−E(u))2 ] ∆u = µ ∂u . situations in which the robot is fixating on a green circle, f2 might be active in those situations in which the robot is fixating on a red triangle, etc. Since the task question is primarily about matching one Suppose also that the robot has the ability to detect the of the cue categories fi and not the cue itself, this process occurrence of a particular auditory tone. After the tone is requires changing the robot’s virtual fixation point u according heard, a varying visual cue (for example, a green circle) to the above equation, and then checking to see if M (E(u)) = appears in some designated area of the field (the upper left M (cue)). If not, u is again updated according to the Delta rule. corner say). The robot’s task (for which it will be rewarded) Alternatively, one could measure the error directly in terms of is to perform some designated action (e.g. say “yes”) if and differences in feature map (M ) output; then the Delta rule only if there is something in the current visual environment would prescribe: (other than in the designated cue area) whose feature map classification matches that of the cue, that is: say “yes” iff ∃(x, y) : M (R(x, y)) = M (cue). ∂[ 12 (M (cue)−M (E(u)))2 ] ∆u = µ ∂u . There are, of course, many strategies the robot could use to perform this task. For illustrative reasons, we will consider three. In either case, this process should eventually arrive at a value u0 that is a minimum in error space, although the number B. Strategy One: Exhaustive search of action space of iterations of changes to u required to do so will depend on The first strategy is an exhaustive search of action space. a number of factors, including µ, which itself is constrained The robot performs a serial exhaustive search of the ac- by the “spikiness” of the error space with respect to fixation tion space R(x, y), stopping to say “yes” if at any point points. This could result in many changes to u, but as such M (R(x, y)) = M (cue). This requires motor activity, and is changes are virtual, rather than actual changes in robot fixation likely to take a relatively long time to perform, although it point, they can be performed much faster than real-time. requires no “offline” preparation time. It is a “knowledge-free” Standard problems with local minima apply: the fixed point solution. in u/error space where the derivative is zero may not only not be a point for which actual error is zero (that is, where C. Strategy Two: Exhaustive search of virtual action space M (R(u0 )) = M (cue)); it may not even be a point for which The second strategy is to perform an exhaustive search of expected error is zero (that is, where M (E(u0 )) = M (cue)). a virtual action space. Nonetheless, u0 can serve as a plausible candidate solution, 1) Strategy Two, Version 1: Prior to hearing the tone, the which can be checked by having the robot fixate on u0 via robot learns a forward model Ew from points of fixation R(u0 ). If a match (M (R(u0 )) = M (cue)) is not achieved, (x, y) to expected sensory input s at the fixated location: standard neural network methods for handling local minima Ew (x, y) = s ∈ S. After the tone and presentation of can be applied, if desired, to see if a better result can be the cue, the robot then performs a serial exhaustive search obtained. of the expectation space Ew (x, y), stopping if at any point This second version of the second strategy may in some M (Ew (i, j)) = M (cue). The robot then fixates on (i, j), and cases be more efficient than the first variation, in that it is if M (R(i, j)) = M (cue), then it says “yes”. Otherwise, the non-exhaustive. But both verisons of the second strategy buy search of the expectation space resumes. As this search is online performance at the price of prior “offline” exploration for the most part virtual, only occasionally requiring action of the action space, and the computational costs of learning (assuming E is reasonably accurate), this will be much faster and memory. than the first strategy. As an aside, we note that the second version of strategy two 2) Strategy Two, Version 2: If the idea of an exhaustive se- can be used in conjunction with strategy one (or even the first rial search of the expectation space is not considered neurally verison of strategy two), in that it can suggest a heuristically- plausible enough, a a second version of the second strategy derived first guess for a real-world (or virtual) search of points Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 33 in the vicinity of that guess. In the case of failure, it wouldn’t is not too spiky (nearby values for w should, in general, be useful as it stands, it seems; since E is deterministic, imply nearby values for M (E(u))). Nevertheless, the the third when asked for a second guess after the failure of the first, strategy would be useful for situations in which immediate, strategy two would give the same recommendation again. gist-based action is required. However, it should be noted that the gradient descent method E. Metamappings as metacognition is dependent on an initial guess u, and derives candidate solutions as modifications to u. Therefore, it will give different As explained at the beginning of this section, we have u0 answers if a different initial u is selected to seed the taken these efforts to incrementally motivate the architecture gradient descent process, with the new u0 corresponding to in strategy three in order to illustrate how a grounded, sensori- the local error minimum that is closest to the new u seed motor based system can merit ascription of the kinds of chosen. Thus, search of the entire virtual (or actual) fixation metacognitive abilities that we have proposed are necessary point (u) space can be reduced, in theory, to a virtual search of for crediting a system with qualia: the much smaller space of error basins in u-space. To prevent • In effect, the forward model E confers on the the system wasteful duplication of effort, there would have to be some belief-like states, in the form of expectations of what way for the network to consider only previously-unconsidered sensor values will result from performing a given action. seeds; perhaps inhibition of previously-considered seeds could These (object, not meta) belief-like states are total in that achieve this. a given state vector w yields an Ew that manifests a range of such expectational beliefs, each concerning a different D. Strategy Three: Learning a mapping from mappings to cues action or point of fixation. A third strategy builds on the second strategy by employing • Similarly, the forward model F confers on the the system a form of reflection or meta-cognition to guide search more meta-belief-like states, in that they indicate which total, efficiently. As with the second strategy, an expectational, object belief states have a particular content property. forward model Ew is used. Note that for any given kind of cue (Note that the meta beliefs are not of the form, for some (node or reference vector in the range of the feature map M ), particular w, u and cue: w manifests the belief that (or we can define the set Pcue to be all those parameter (weight) represents that) M (R(u)) = M (cue). Rather, they are of sets w for E that yield a forward model that contains at least the form, for some particular w and cue: ∃u : w manifests one expectation to see that cue. That is, Pcue = ∀w : ∃(x, y) : the belief that M (R(u)) = M (cue).) M (Ew (x, y)) = cue. Meta-belief is not only an explicit requirement for the kind With a network distinct from the one realising E, the robot of qualia-supporting architecture outlined in section II and III; can learn an approximation of Pcue . That is, the robot can it also opens to door to the further requirements of inference, learn a mapping Fcue from weight sets for E to {1,0}, such deliberation and sensitivity to logical relations. To see how, that Fcue (w) = 1 iff w ∈ Pcue . Generalising, the robot can consider one more addition to the architecture we arrived at learn a mapping F from cues and weight sets for E to {1,0}, when discussing strategy three. As with the individual nodes such that F (cue, w) = 1 iff w ∈ Pcue . That is, F is a in the feature map, we can define the set Pc1 ,c2 to be all those network that, given a vector w and a cue, outputs a 1 only parameter sets w that yield a forward model that contains at if w parameterises a forward model Ew for which there is at least one expectation to see c1 and one expectation to see c2 ; least one fixation point (x, y) such that Ew “expects” cue as that is, Pc1 ,c2 = ∀w : ∃(u1 )(u2 ) such that: input after performing R(x, y). • M (Ew (u1 )) = c1 ; and Given this, a third strategy for performing the task is to • M (Ew (u2 )) = c2 simply input the current E parameter configuration w and With another network G distinct from E (and F ), the robot the cue into F , and say “yes” iff F (w, cue) = 1 (or, if one can learn an approximation of Pc1 ,c2 : G(w, c1 , c2 ) = 1 iff prefers, make the probability of saying “yes” proportional to w ∈ Pc1 ,c2 . That is, G is a network that: F (w, cue)). • takes the parameters w of E as input Like strategy two, strategy three spends considerable “of- • outputs a 1 only if those parameters realise a forward fline”, pre-task resources for substantial reductions in the time expected to complete the online task. However, unlike both model Ew for which: strategy one and strategy two, this third strategy answers the – ∃u1 : M (Ew (u1 )) = c1 ; and task question directly: it determines whether the existential – ∃u2 : M (Ew (u2 )) = c2 ; condition of the task question holds without first finding a Note that it is a logical truth that w ∈ Pc1 ,c2 → w ∈ Pc1 . particular fixation point that satisfies the property that the task It follows that there is a logical relation between G and condition (existentially) quantifies over. A drawback of this F ; specifically, it should be true that G(w, c1 , c2 ) = 1 → is that the robot cannot, unlike with strategy two, check its F (w, c1 ) = 1. Assuming F and G are themselves reasonably answer in the real world (except by essentially performing accurate, the robot could observe and learn this regularity. strategy one). But as it is essentially a lookup computation, it But because F and G are only approximations, there might is very fast: no search, even virtual, is required. Admittedly, actually be cases (values of w) where they are inconsistent this is only useful if F can be learned, and if the space (where G(w, c1 , c2 ) = 1 but F (w, c1 ) = 0). That such a Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 34 mismatch constitutes error could be built into the architecture, 1 only if either one or the other of its associated cues c1 and yielding an error signal not between expected and empirical c2 is in the range of Ew . Such a network having output of 1 object-level states of affairs, but between a logical norm and for w, in the face of F (w, c1 ) = 0, would allow the network the empirical relation between meta-belief states that should to infer that F (w, c2 ) should be 1, and use that in place of respect that norm. computing F (w, c2 ) explicitly, or to generate an error signal How should the robot respond to this error signal, which if F (w, c2 ) 6= 1, etc. indicates the violation of a logical norm? In the case of Further sophistication, conferring even more of the kinds empirical, object-level error, the direction of fit is from model of metacognitive abilities discussed in sections II and III, to world, so error should be reduced by changing the model could be added by not just allowing the robot to observe (pace Friston and active inference[7]). But in this case, the the holding or not of various logical relations in its own error is not between model and world, but between two models beliefs, but by giving it the ability to take action on the meta- of the world: should the robot modify F , or G, or both? level, and allow such actions to be guided, as on the object Although it seems unlikely that there is a general, situation- level, by expectations realized in forward models on the meta- independent answer to this question, one could certainly level. Such forward models would not manifest expectations imagine another iteration of reflection and complexity that about how sensory input would be transformed by performing would enable a robot to learn an effective way for handling this or that movement, but rather how object-level forward such situations. For example, F and G could be part of a models such as E would change, if one were to perform network of experts, in which a gating network learns the kinds this or that operation on their parameter sets w. To give a of situations in which any F /G mismatch should be resolved in trivial example, there might be a primitive operation N that F 0 s favour, and which in G0 s. But there is also the possibility could be performed on a forward model’s parameters that of a resolution due to implicit architectural features that do had the effect of normalizing those parameters. A network’s not constitute a semantic ascent. An interactive activation understanding of this might be manifested in a network J competition between F and G might, for example, always such that J(w1 , N ) = norm(w1 ), J(w2 , N ) = norm(w2 ), be resolved in F 0 s favour simply because F has fewer inputs etc., with J being consulted when normalization is being and parameters than G – or vice versa. Such a system could considered as a possible meta-action to perform. be understood as having a belief, albeit an implicit one, that V. C ONCLUSION object-level beliefs manifested in F are always more reliable, justified, etc. than beliefs manifested in G. And again, a The “Hard core” of consciousness is meant to be qualia, but sophisticated architecture, although continuous with the kinds sections I-III argue that qualia, understood as the underlying of systems considered so far, could observe instances of this phenomenon (if any) that explains qualia-talk and qualia- regularity, and thus learn the regularity itself. It could thus beliefs, might be explicable in terms of phenomena that are come to know (or at least believe) that it always takes F -based considered to fall under the “easy problems” of consciousness. judgements to be more reliable than (logically conflicting) G- The speculations of section IV fall short of closing the based ones. From the error signal that is produced whenever loop started in sections II and III, but they hopefully give they disagree the system could come to believe that G and one an idea how a grounded sensorimotor robotic cognitive F are logically related. The crucial point is that the robot architecture could merit attribution of such features as having has the essentials of a notion of logical justification and beliefs and having beliefs about beliefs. In particular, it is logical consistency of its own beliefs. It could use a systematic hoped that some substance has been given to the possibility mismatch between G and F as evidence that G requires more of such an architecture being able to employ concepts such as learning, or indeed use that mismatch as a further error signal justifcation, deliberation and consistency. to guide learning in G, or even E itself. ACKNOWLEDGMENT One could ask: why go to all this trouble? Couldn’t all of The authors would like to thank David Booth, Simon this have been motivated simply by considering a robot that Bowes, Simon McGregor, Jonny Lee, Matthew Jacquiery and contains two forward models, E and E 0 , that are meant to have other participants at the E-Intentionality seminar on December the same functionality, but which might contingently evolve in 1st, 2017 at the University of Sussex, and the participants at such a way that they disagree on some inputs? The answer is a workshop and lecture on these ideas held at the University yes, and no. Yes, an instance of being a logically-constrained of Vienna on December 4th and 5th, 2017, for their helpful cognizer is that one eschews believing P and ¬P . But no: to comments on the ideas expressed in the first three sections of start with such an architecturally unmotivated example would this paper. not serve to make a general case for how meta-beliefs as a whole could get going in a sensorimotor grounded architecture. 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