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The connectionist vehicle theory of phenomenal experience in the target article identifies consciousness with the brain’s explicit representation of information in the form of stable patterns of neural activity. Commentators raise concerns about both the conceptual and empirical adequacy of this proposal. On the former front they worry about our reliance on vehicles, on representation, on stable patterns of activity, and on our identity claim. On the latter front their concerns range from the general plausibility of a vehicle theory to our specific attempts to deal with the dissociation studies. We address these concerns, and then finish by considering whether the vehicle theory we have defended has a coherent story to tell about the active, unified subject to whom conscious experiences belong.

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways.

One of the principal tasks Dennett sets himself in "Consciousness Explained" is to demolish the Cartesian theatre model of phenomenal consciousness, which in its contemporary garb takes the form of Cartesian materialism: the idea that conscious experience is a process of presentation realized in the physical materials of the brain. The now standard response to Dennett is that, in focusing on Cartesian materialism, he attacks an impossibly naive account of consciousness held by no one currently working in cognitive science or the philosophy of mind. Our response is quite different. We believe that, once properly formulated, Cartesian materialism is no straw man. Rather, it is an attractive hypothesis about the relationship between the computational architecture of the brain and phenomenal consciousness, and hence one that is worthy of further exploration. Consequently, our primary aim in this paper is to defend Cartesian materialism from Dennett's assault. We do this by showing that Dennett's argument against this position is founded on an implicit assumption (about the relationship between phenomenal experience and information coding in the brain), which while valid in the context of classical cognitive science, is not forced on connectionism.

It is commonplace for both philosophers and cognitive scientists to express their allegiance to the "unity of consciousness". This is the claim that a subject’s phenomenal consciousness, at any one moment in time, is a single thing. This view has had a major influence on computational theories of consciousness. In particular, what we call single-track theories dominate the literature, theories which contend that our conscious experience is the result of a single consciousness-making process or mechanism in the brain. We argue that the orthodox view is quite wrong: phenomenal experience is not a unity, in the sense of being a single thing at each instant. It is a multiplicity, an aggregate of phenomenal elements, each of which is the product of a distinct consciousness-making mechanism in the brain. Consequently, cognitive science is in need of a multi-track theory of consciousness; a computational model that acknowledges both the manifold nature of experience, and its distributed neural basis.

When it comes to applying computational theory to the problem of phenomenal consciousness, cognitive scientists appear to face a dilemma. The only strategy that seems to be available is one that explains consciousness in terms of special kinds of computational processes. But such theories, while they dominate the field, have counter-intuitive consequences; in particular, they force one to accept that phenomenal experience is composed of information processing effects. For cognitive scientists, therefore, it seems to come down to a choice between a counter-intuitive theory or no theory at all. We offer a way out of this dilemma. We argue that the computational theory of mind doesn't force cognitive scientists to explain consciousness in terms of computational processes, as there is an alternative strategy available: one that focuses on the representational vehicles that encode information in the brain. This alternative approach to consciousness allows us to do justice to the standard intuitions about phenomenal experience, yet remain within the confines of cognitive science.

Carruthers presents evidence concerning the cross-modular integration of information in human subjects which appears to support the 'cognitive conception of language.' According to this conception, language is not just a means of communication, but also a representational medium of thought. However, Carruthers overlooks the possibility that language, in both its communicative and cognitive roles, is a nonrepresentational system of conventional signals--that words are not a medium we think in, but a tool we think with. The evidence he cites is equivocal when it comes to choosing between the cognitive conception and this radical communicative conception of language.

We are sympathetic with the broad aims of Perruchet & Vinter's 'mentalistic' framework. But it is implausible to claim, as they do, that human cognition can be understood without recourse to unconsciously represented information. In our view, this strategy forsakes the only available mechanistic understanding of intelligent behaviour. Our purpose here is to plot a course midway between the classical unconscious and Perruchet & Vinter's own noncomputational associationism.

O'Regan & Noe (O&N) fail to address adequately the two most historically important reasons for seeking to explain visual experience in terms of internal representations. They are silent about the apparently inferential nature of perception, and mistaken about the significance of the phenomenology accompanying dreams, hallucinations, and mental imagery.

In their target article, Hutto and Satne eloquently articulate the failings of most current attempts to naturalize mental content. Furthermore, we think they are correct in their insistence that the only way forward is by drawing a distinction between two kinds of intentionality, one of which is considerably weaker than-and should be deployed to explain-the propositional variety most philosophers take for granted. The problem is that their own rendering of this weaker form of intentionality-contentless intentionality-is too weak. What's needed is a species of intentionality distinct from both the industrial-strength version beloved by philosophers and the intentionality lite recommended by Hutto and Satne. We briefly motivate and sketch this alternative, and say a few words about the account of cognition that it spawns. [ABSTRACT FROM AUTHOR]

Reformers urge that representation no longer earns its explanatory keep in cognitive science, and that it is time to discard this troublesome concept. In contrast, we hold that without representation cognitive science is utterly bereft of tools for explaining natural intelligence. In order to defend the latter position, we focus on the explanatory role of representation in computation. We examine how the methods of digital and analog computation are used to model a relatively simple target system, and show that representation plays an in-eliminable explanatory role in both cases. We conclude that, to the extent that biologic systems engage in computation, representation is destined to play an explanatory role in cognitive science.

Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its computational credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation-no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we examine what might be regarded as the "conventional" account of connectionist computation. We show why this account is inadequate and hence fosters the suspicion that connectionist networks are not genuinely computational. Lastly, we turn to the principal task of the paper: the development of a more robust portrait of connectionist computation. The basis of this portrait is an explanation of the representational capacities of connection weights, supported by an analysis of the weight configurations of a series of simulated neural networks.