David Chalmers

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that computation can specify general patterns of causal organization, and mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science.

*[[I wrote this paper in January of 1990, but did not publish it because I was never entirely happy with it. My ideas on consciousness were in a state of flux, ultimately evolving into those represented in my book _The Conscious Mind_ (Oxford University Press, 1996). I now think that some parts of this paper are unsatisfactory, especially the positive theory outlined at the end, although a successor to that theory is laid out in the book. Nevertheless, I think the paper raises issues that need to be addressed. ]].

This paper is a response to the 26 commentaries on my paper "Facing Up to the Problem of Consciousness". First, I respond to deflationary critiques, including those that argue that there is no "hard" problem of consciousness or that it can be accommodated within a materialist framework. Second, I respond to nonreductive critiques, including those that argue that the problems of consciousness are harder than I have suggested, or that my framework for addressing them is flawed. Third, I address positive proposals for addressing the problem of consciousness, including those based in neuroscience and cognitive science, phenomenology, physics, and fundamental psychophysical theories. Reply to: Baars, Bilodeau, Churchland, Clark, Clarke, Crick & Koch, Dennett, Hameroff & Penrose, Hardcastle, Hodgson, Hut & Shepard, Libet, Lowe, MacLennan, McGinn, Mills, O'Hara & Scutt, Price, Robinson, Rosenberg, Seager, Shear, Stapp, Varela, Velmans.

This paper explores how an evolutionary process can produce systems that learn. A general framework for the evolution of learning is outlined, and is applied to the task of evolving mechanisms suitable for supervised learning in single-layer neural networks. Dynamic properties of a network’s information-processing capacity are encoded genetically, and these properties are subjected to selective pressure based on their success in producing adaptive behavior in diverse environments. As a result of selection and genetic recombination, various successful learning mechanisms evolve, including the well-known delta rule. The effect of environmental diversity on the evolution of learning is investigated, and the role of different kinds of emergent phenomena in genetic and connectionist systems is discussed.