David Chalmers

A natural way to think about epistemic possibility is as follows. When it is epistemically possible (for a subject) that p, there is an epistemically possible scenario (for that subject) in which p. The epistemic scenarios together constitute epistemic space. It is surprisingly difficult to make the intuitive picture precise. What sort of possibilities are we dealing with here? In particular, what is a scenario? And what is the relationship between scenarios and items of knowledge and belief? This chapter tries to make sense of epistemic space. It explores different ways of making sense of scenarios and of their relationship to thought and language. It discusses some issues that arise and outlines some applications to the analysis of the content of thought and the meaning of language.

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 (a) computation can specify general patterns of causal organization, and (b) 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.

What follows are compressed versions of three lectures on the subject of "Mind and Modality", given at Princeton University the week of October 12-16, 1998. The first two form a series; the third stands alone to some extent. All are philosophically technical, and probably of interest mainly to philosophers. I hope that they make sense, at least to those familiar with my book _The Conscious Mind_. Lecture 1 recapitulates some of the material in the book in a somewhat different form, and adds some further material on conditionals and on Kripke. Note that section has a more or less definitive formalization of the anti-materialist argument from the book. Lecture 2 pushes deeper into the heart of modality, further investigating the conceivability/possibility relationship and the epistemology of modality, and arguing for a sort of modal rationalism. Lecture 3 gives an analysis of the content of beliefs about experiences, and applies this to a number of epistemological issues, including incorrigibility and the dialectic on "The Myth of the Given".

The book is an extended study of the problem of consciousness. After setting up the problem, I argue that reductive explanation of consciousness is impossible, and that if one takes consciousness seriously, one has to go beyond a strict materialist framework. In the second half of the book, I move toward a positive theory of consciousness with fundamental laws linking the physical and the experiential in a systematic way. Finally, I use the ideas and arguments developed earlier to defend a form of strong artificial intelligence and to analyze some problems in the foundations of quantum mechanics.