. . . Unlike Dewey, he has provided detailed incisive argumentation, and has shown just where the dogmas and dualisms break down." -- Richard Rorty, The Yale Review.

The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical (...) signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy. (shrink)

This paper challenges two orthodox theses: (a) that computational processes must be algorithmic; and (b) that all computed functions must be Turing-computable. Section 2 advances the claim that the works in computability theory, including Turing's analysis of the effective computable functions, do not substantiate the two theses. It is then shown (Section 3) that we can describe a system that computes a number-theoretic function which is not Turing-computable. The argument against the first thesis proceeds in two stages. It is first (...) shown (Section 4) that whether a process is algorithmic depends on the way we describe the process. It is then argued (Section 5) that systems compute even if their processes are not described as algorithmic. The paper concludes with a suggestion for a semantic approach to computation. (shrink)

We discuss at some length evidence from the cognitive science suggesting that the representations of objects based on spatiotemporal information and featural information retrieved bottomup from a visual scene precede representations of objects that include conceptual information. We argue that a distinction can be drawn between representations with conceptual and nonconceptual content. The distinction is based on perceptual mechanisms that retrieve information in conceptually unmediated ways. The representational contents of the states induced by these mechanisms that are available to a (...) type of awareness called phenomenal awareness constitute the phenomenal content of experience. The phenomenal content of perception contains the existence of objects as separate things that persist in time and time, spatiotemporal information, and information regarding relative spatial relations, motion, surface properties, shape, size, orientation, color, and their functional properties. (shrink)

The paper argues that the reference of perceptual demonstratives is fixed in a causal nondescriptive way through the nonconceptual content of perception. That content consists first in spatiotemporal information establishing the existence of a separate persistent object retrieved from a visual scene by the perceptual object segmentation processes that open an object-file for that object. Nonconceptual content also consists in other transducable information, that is, information that is retrieved directly in a bottom-up way from the scene (motion, shape, etc). The (...) nonconceptual content of the mental states induced when one uses a perceptual demonstrative constitutes the mode of presentation of the perceptual demonstrative that individuates but does not identify the object of perceptual awareness and allows reference to it. On that account, perceptual demonstratives put us in a de re relationship with objects in the world through the non-conceptual information retrieved directly from the objects in the environment. (shrink)

Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in (...) computability theory), (ii) a technique that inspired the notion of logic design (a fundamental part of modern computer design), (iii) the first use of computation to address the mind–body problem, and (iv) the first modern computational theory of mind and brain. (shrink)

Ruth Millikan is well known for having developed a strikingly original way for philosophers to seek understanding of mind and language, which she sees as biological phenomena. She now draws together a series of groundbreaking essays which set out her approach to language. Guiding the work of most linguists and philosophers of language today is the assumption that language is governed by prescriptive normative rules. Millikan offers a fundamentally different way of viewing the partial regularities that language displays, comparing them (...) to biological norms that emerge from natural selection. This yields novel and quite radical consequences for our understanding of the nature of public linguistic meaning, the process of language understanding, how children learn language, and the semantics/pragmatics distinction. (shrink)

The use of “levels of abstraction” in philosophical analysis (levelism) has recently come under attack. In this paper, I argue that a refined version of epistemological levelism should be retained as a fundamental method, called the method of levels of abstraction. After a brief introduction, in section “Some Definitions and Preliminary Examples” the nature and applicability of the epistemological method of levels of abstraction is clarified. In section “A Classic Application of the Method ofion”, the philosophical fruitfulness of the new (...) method is shown by using Kant’s classic discussion of the “antinomies of pure reason” as an example. In section “The Philosophy of the Method of Abstraction”, the method is further specified and supported by distinguishing it from three other forms of “levelism”: (i) levels of organisation; (ii) levels of explanation and (iii) conceptual schemes. In that context, the problems of relativism and antirealism are also briefly addressed. The conclusion discusses some of the work that lies ahead, two potential limitations of the method and some results that have already been obtained by applying the method to some long-standing philosophical problems. (shrink)

Relativist and constructivist conceptions of knowledge have become orthodoxy in vast stretches of the academic world in recent times. This book critically examines such views and argues that they are fundamentally flawed. The book focuses on three different ways of reading the claim that knowledge is socially constructed, one about facts and two about justification. All three are rejected. The intuitive, common sense view is that there is a way things are that is independent of human opinion, and that we (...) are capable of arriving at belief about how things are that is objectively reasonable, and is binding on anyone capable of appreciating the relevant evidence, regardless of their social or cultural perspective. Difficult as these notions may be, it is a mistake to think that recent philosophy has uncovered powerful reasons for rejecting them. (shrink)

In Mind: An Introduction, for the first time John Searle offers a general introduction to the philosophy of the mind. Giving a broad survey of all the major issues under discussion in the field, including philosophical issues in cognitive science and neurobiology, Searle argues for his own distinctive point of view. He leads the reader through the variety of theories that reduce the mind to aspects that can be fully explained by physics, and then concludes with his own view that (...) such reductionist theories ignore or cannot adequately explain the most fundamental aspect of the mind - subjective consciousness. (shrink)

_Minds, Brains, Computers_ serves as both an historical and interdisciplinary introduction to the foundations of cognitive science.

Cognitive science is the project of understanding the mind by modelling its workings. Its development is one of the most remarkable and fascinating intellectual achievements of the modern era. Mind as Machine is a masterful history of cognitive science, told by one of its most eminent practitioners.

"The philosophy of mind is unique among contemporary philosophical subjects," writes John Searle, "in that all of the most famous and influential theories are false." In Mind, Searle dismantles these famous and influential theories as he presents a vividly written, comprehensive introduction to the mind. Here readers will find one of the world's most eminent thinkers shedding light on the central concern of modern philosophy. Searle begins with a look at the twelve problems of philosophy of mind--which he calls "Descartes (...) and Other Disasters"--problems which he returns to throughout the volume, as he illuminates such topics as the freedom of the will, the actual operation of mental causation, the nature and functioning of the unconscious, the analysis of perception, and the concept of the self. One of the key chapters is on the mind-body problem, which Searle analyzes brilliantly. He argues that all forms of consciousness--from feeling thirsty to wondering how to translate Mallarme--are caused by the behavior of neurons and are realized in the brain system, which is itself composed of neurons. But this does not mean that consciousness is nothing but neuronal behavior. The main point of having the concept of consciousness, Searle points out, is to capture the first person subjective features of the phenomenon and this point is lost if we redefine consciousness in third person objective terms. Described as a "dragonslayer by temperament," John Searle offers here a refreshingly direct and open discussion of philosophy, one that skewers accepted wisdom even as it offers striking new insights into the nature of consciousness and the mind. (shrink)

This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 The Psychological Reality (...) of Programs: Strong Equivalence 5 Constraining Functional Architecture 6 The Bridge from Physical to Symbolic: Transduction 7 Functional Architecture and Analogue Processes 8 Mental Imagery and Functional Architecture 9 Epilogue: What is Cognitive Science the Science of? (shrink)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be grounded (...) in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is –- or at least on its.