David Chalmers

What are the philosophical views of contemporary professional philosophers? We surveyed many professional philosophers in order to help determine their views on 30 central philosophical issues. This article documents the results. It also reveals correlations among philosophical views and between these views and factors such as age, gender, and nationality. A factor analysis suggests that an individual's views on these issues factor into a few underlying components that predict much of the variation in those views. The results of a metasurvey also suggest that many of the results of the survey are surprising: philosophers as a whole have quite inaccurate beliefs about the distribution of philosophical views in the profession.

Scott Soames’ Reference and Description contains arguments against a number of different versions of two-dimensional semantics. After early chapters on descriptivism and on Kripke’s anti-descriptivist arguments, a chapter each is devoted to the roots of twodimensionalism in “slips, errors, or misleading suggestions” by Kripke and Kaplan, and to the two-dimensional approaches developed by Stalnaker (1978) and by Davies and Humberstone (1981). The bulk of the book (about 200 pages) is devoted to “ambitious twodimensionalism”, attributed to Frank Jackson, David Lewis, and me. After a quick overview of two-dimensional approaches, I will focus on Soames’ discussion of ambitious twodimensionalism. I will then turn to a system advocated by Soames that is itself strikingly reminiscent of a two-dimensional approach. Two-dimensional semantic theories are varieties of possible-worlds semantics on which linguistic items can be evaluated relative to possibilities in two different ways, yielding two sorts of intensional semantic values, which can be seen as two “dimensions” of meaning. The second dimension is the familiar sort of Kripkean evaluation in metaphysically possible worlds, so that necessarily coextensive terms (such as ‘Hesperus’ and ‘Phosphorus’ or ‘water’ and ‘H2O’) always have the same semantic value. The first dimension behaves differently, so that there are typically at least some cases where necessarily coextensive terms have different semantic values on the first dimension. For this reason, the two-dimensional framework is sometimes seen as a way of granting many of the insights of a Kripkean approach to meaning (on the second dimension), while retaining elements of a Fregean approach to meaning (on the first dimension)

How does conscious experience emerge from a physical basis? At a first glance, this is the question about the mind that most needs answering. So it is curious that those who study the mind professionally have often avoided the question entirely. In psychology, the cognitive revolution did not make consciousness respectable: most cognitive psychologists have stuck to subjects such as learning, memory, and perception instead. Neuroscientists have been known to speculate on the topic, but usually only late at night, after a few drinks. Even philosophers have been curiously diffident. Some have been exercised by the fact that there is a problem, others have been concerned to deny the problem entirely, but the focus of inquiry has remained elsewhere. As in all these fields, serious theories of consciousness have been hard to come by.

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.

A month ago, I bought an iPhone. The iPhone has already taken over some of the central functions of my brain. It has replaced part of my memory, storing phone numbers and addresses that I once would have taxed my brain with. It harbors my desires: I call up a memo with the names of my favorite dishes when I need to order at a local restaurant. I use it to calculate, when I need to figure out bills and tips. It is a tremendous resource in an argument, with Google ever present to help settle disputes. I make plans with it, using its calendar to help determine what I can and can’t do in the coming months. I even daydream on the iPhone, idly calling up words and images when my concentration slips. Friends joke that I should get the iPhone implanted into my brain. But if Andy Clark is right, all this would do is speed up the processing, and free up my hands. The iPhone is part of my mind already. Clark is a connoisseur of the myriad ways in which the mind relies on the world to get its work done. The first part of this marvelous book explores some of these ways: the extension of our bodies, the extension of our senses, and crucially, the use of language as a tool to extend our thought. The second part of the book defends the thesis that in at least some of these cases, the world is not serving as a mere instrument for the mind. Rather, the relevant parts of the world have become parts of my mind. My iPhone is not my tool, or at least it is not wholly my tool. Parts of it have become parts of me. This is the thesis of the extended mind: when parts of the environment are coupled to the brain in the right way, they become parts of the mind. The thesis has a long history: I am told that there are hints of it in Dewey, Heidegger, and Wittgenstein. But no-one has done as much to give life to the idea as Andy Clark. In a series of important books and articles—Being There, Natural-Born Cyborgs, “Magic words: How language augments human computation”, and many others—he has explored the many ways in which the boundaries between mind and world are far more flexible than one might have thought..

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power.

1. I think that by emphasizing theoretical spaces of representations, Andy has put his finger on an issue that is key to connectionism's success, and whose investigation will be a key determinant of the field's further progress. I also think that if we look at representational spaces in the right way, we can see that they are deeply related to classical phenomenon of systematicity in representation. I want to argue that the key to understanding representational spaces, and in particular their ability to capture the deep organization underlying various problems, lies in the idea of what I will call.

To clarify the notion of computation and its role in cognitive science, we need an account of implementation, the nexus between abstract computations and physical systems. I provide such an account, based on the idea that a physical system implements a computation if the causal structure of the system mirrors the formal structure of the computation. The account is developed for the class of combinatorial-state automata, but is sufficiently general to cover all other discrete computational formalisms. The implementation relation is non-vacuous, so that criticisms by Searle and others fail. This account of computation can be extended to justify the foundational role of computation in artificial intelligence and cognitive science.

Graeme Forbes (2011) raises some problems for two-dimensional semantic theories. The problems concern nested environments: linguistic environments where sentences are nested under both modal and epistemic operators. Closely related problems involving nested environments have been raised by Scott Soames (2005) and Josh Dever (2007). Soames goes so far as to say that nested environments pose the “chief technical problem” for strong two-dimensionalism. We call the problem of handling nested environments within two-dimensional semantics “the nesting problem”. We show that the two-dimensional semantics for attitude ascriptions developed in Chalmers (2011a) has no trouble accommodating certain forms of the nesting problem that involve factive verbs such as “know” or “establish”. A certain form of the nesting problem involving apriority and necessity operators does raise an interesting puzzle, but we show how a generalized version of the nesting problem arises independently of two-dimensional semantics—it arises, in fact, for anyone who accepts the contingent a priori. We, then, provide a two-dimensional treatment of the apriority operator that fits the two-dimensional treatment of attitude verbs and apply it to the generalized nesting problem. We conclude that two-dimensionalism is not seriously threatened by cases involving the nesting of epistemic and modal operators.

It is fairly well-known that certain hard computational problems (that is, 'difficult' problems for a digital processor to solve) can in fact be solved much more easily with an analog machine. This raises questions about the true nature of the distinction between analog and digital computation (if such a distinction exists). I try to analyze the source of the observed difference in terms of (1) expanding parallelism and (2) more generally, infinite-state Turing machines. The issue of discreteness vs continuity will also be touched upon, although it is not so important for analyzing these particular problems.

In Section 5 of his interesting article, Goldman suggests that the consideration of imaginary cases can be valuable in the analysis of our psychological concepts. In particular, he argues that we can imagine a system that is isomorphic to us under any functional description, but which lacks qualitative mental states, such as pains and color sensations. Whether or not such a being is empirically possible, it certainly seems to be logically possible, or conceptually coherent. Goldman argues from this possibility to the conclusion that our concepts of qualitative mental states cannot be analyzed entirely in functional terms.

First, I should clarify the notion of "taking consciousness seriously", which serves as a premise in my work. Mulhauser characterizes this as the assumption that no cognitive theory of consciousness will suffice. The latter assumption would indeed beg some crucial questions, but it is not the assumption that I make. I make an assumption about the problem of consciousness, not about any solution. To quote (p. xii): Throughout the book, I have assumed that consciousness exists, and that to redefine the problem as that of explaining how certain cognitive and behavioral functions are performed is unacceptable. This is what I mean by taking consciousness seriously. That is, the premise is simply that there is a phenomenon to be explained, and that the problems of explaining such functions as discrimination, integration, self monitoring, reportability, and so on do not exhaust all the problems in the vicinity. The deepest problem of consciousness, as I understand it, is not the problem of how all these functions are performed, but rather the problem of explaining how and why all this activity supports states of subjective experience.

Where does the mind stop and the rest of the world begin? The question invites two standard replies. Some accept the demarcations of skin and skull, and say that what is outside the body is outside the mind. Others are impressed by arguments suggesting that the meaning of our words "just ain't in the head", and hold that this externalism about meaning carries over into an externalism about mind. We propose to pursue a third position. We advocate a very different sort of externalism: an _active externalism_, based on the active role of the environment in driving cognitive processes.