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)

The title of The Rediscovery of the Mind suggests the question "When was the mind lost?" Since most people may not be aware that it ever was lost, we must also then ask "Who lost it?" It was lost, of course, only by philosophers, by certain philosophers. This passed unnoticed by society at large. The "rediscovery" is also likely to pass unnoticed. But has the mind been rediscovered by the same philosophers who "lost" it? Probably not. John Searle is an (...) analytic philosopher, with some of the same notions as the positivists and behaviorists who rejected consciousness and "lost" the mind in the first place, but he also does not sound like the kind of reductionist who would have joined that crowd. His views, indeed, are sensible enough, and some of his insights so important, that it is a shame to find his thought profoundly limited by some of the same mistakes and prejudices that ruined philosophy, and not just philosophy of mind, under the influence of those positivists and behaviorists. There is enough of genuine value in his treatment, that it can easily be taken up and, with relatively slight modification, added to what is of permanent value in the history of philosophy. (shrink)

In The Construction of Social Reality, John Searle argues that there are two kinds of facts--some that are independent of human observers, and some that require..

Carl F. Craver investigates what we are doing when we use neuroscience to explain what's going on in the brain. When does an explanation succeed and when does it fail? Craver offers explicit standards for successful explanation of the workings of the brain, on the basis of a systematic view about what neuroscientific explanations are.

This book offers a philosophy for error-prone humans trying to understand messy systems in the real world.

This collection of essays serves both as an introduction to Ruth Millikan’s much-discussed volume Language, Thought, and Other Biological Categories and as an extension and application of Millikan’s central themes, especially in the philosophy of psychology. The title essay discusses meaning rationalism and argues that rationality is not in the head, indeed, that there is no legitimate interpretation under which logical possibility and necessity are known a priori. In other essays, Millikan clarifies her views on the nature of mental representation, (...) explores whether human thought is a product of natural selection, examines the nature of behavior as studied by the behavioral sciences, and discusses the issues of individualism in psychology, psychological explanation, indexicality in thought, what knowledge is, and the realism/antirealism debate. Table of Contents Preface Acknowledgments Introduction 1 In Defense of Proper Functions 2 Propensities, Exaptations, and the Brain 3 Thoughts without Laws 4 Biosemantics 5 On Mentalese Orthography, Part 1 6 Compare and Contrast Dretske, Fodor, and Millikan on Teleosemantics 7 What Is Behavior? A Philosophical Essay on Ethology and Individualism in Psychology, Part 1 8 The Green Grass Growing All Around: A Philosophical Essay on Ethology and Individualism in Psychology, Part 2 9 Explanation in Biopsychology 10 Metaphysical Antirealism? 11 Truth Rules, Hoverflies, and the Kripke-Wittgenstein Paradox 12 Naturalist Reflections on Knowledge 13 The Myth of the Essential Indexical 14 White Queen Psychology; or, The Last Myth of the Given References Index. (shrink)

"Like Dewey, he has revolted against the empiricist dogma and the Kantian dualisms which have compartmentalized philosophical thought.... Unlike Dewey, he has provided detailed incisive argumentation, and has shown just where the dogmas and dualisms break down." --Richard Rorty, _The Yale Review_.

Looks at accounts by Locke, Fodor, Dretske, and Millikan concerning the nature of mental representation, and discusses connectionism and representation.

Hilary Putnam, who may have been the first philosopher to advance the notion that the computer is an apt model for the mind, takes a radically new view of his...

Completely revised and updated in its Second Edition, Language and Reality provides students, philosophers and cognitive scientists with a lucid and provocative introduction to the philosophy of language.

Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions (...) are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed. (shrink)

Hilary Putnam has argued that computational functionalism cannot serve as a foundation for the study of the mind, as every ordinary open physical system implements every finite-state automaton. I argue that Putnam's argument fails, but that it points out the need for a better understanding of the bridge between the theory of computation and the theory of physical systems: the relation of implementation. It also raises questions about the class of automata that can serve as a basis for understanding the (...) mind. I develop an account of implementation, linked to an appropriate class of automata, such that the requirement that a system implement a given automaton places a very strong constraint on the system. This clears the way for computation to play a central role in the analysis of mind. (shrink)

In this paper I defend an etiological theory of biological functions (according to which the proper function of a trait is the effect for which it was selected by natural selection) against three objections which have been influential. I argue, contrary to Millikan, that it is wrong to base our defense of the theory on a rejection of conceptual analysis, for conceptual analysis does have an important role in philosophy of science. I also argue that biology requires a normative notion (...) of a "proper function", and that a normative notion is not ahistorical. (shrink)

This book represents the views of one of the greatest mathematicians of the twentieth century on the analogies between computing machines and the living human brain.

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...) required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

Many areas of science develop by discovering mechanisms and role functions. Cummins' (1975) analysis of role functions-according to which an item's role function is a capacity of that item that appears in an analytic explanation of the capacity of some containing system-captures one important sense of "function" in the biological sciences and elsewhere. Here I synthesize Cummins' account with recent work on mechanisms and causal/mechanical explanation. The synthesis produces an analysis of specifically mechanistic role functions, one that uses the characteristic (...) active, spatial, temporal, and hierarchical organization of mechanisms to add precision and content to Cummins' original suggestion. This synthesis also shows why the discovery of role functions is a scientific achievement. Discovering a role function (i) contributes to the interlevel integration of multilevel mechanisms, and (ii) provides a unique, contextual variety of causal/mechanical explanation. (shrink)

Reasoning in Biological Discoveries brings together a series of essays, which focus on one of the most heavily debated topics of scientific discovery. Collected together and richly illustrated, Darden's essays represent a groundbreaking foray into one of the major problems facing scientists and philosophers of science. Divided into three sections, the essays focus on broad themes, notably historical and philosophical issues at play in discussions of biological mechanism; and the problem of developing and refining reasoning strategies, including interfield relations and (...) anomaly resolution. Darden summarizes the philosophy of discovery and elaborates on the role that mechanisms play in biological discovery. Throughout the book, she uses historical case studies to extract advisory reasoning strategies for discovery. Examples in genetics, molecular biology, biochemistry, immunology, neuroscience and evolutionary biology reveal the process of discovery in action. (shrink)

This volume provides a guide to the discussion among biologists and philosophersabout the role of concepts such as function and design in an evolutionary understanding oflife.

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. (shrink)

In the beginning, there was the DN (Deductive Nomological) model of explanation, articulated by Hempel and Oppenheim (1948). According to DN, scientific explanation is subsumption under natural law. Individual events are explained by deducing them from laws together with initial conditions (or boundary conditions), and laws are explained by deriving them from other more fundamental laws, as, for example, the simple pendulum law is derived from Newton's laws of motion.

1. The Naturalistic Turn in Philosophy of Science 2. The Framework of Mechanistic Explanation: Parts, Operations, and Organization 3. Representing and Reasoning About Mechanisms 4. Mental Mechanisms: Mechanisms that Process Information 5. Discovering Mental Mechanisms 6 . Summary.

But what are functions? Here, 15 leading scholars of philosophy of psychology and philosophy of biology present new essays on functions.

This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing (...) justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power. (shrink)

The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational (...) states are individuated within computability theory and computer science. A secondary purpose is to show that existing arguments for the semantic view are defective. (shrink)

To compute is to execute an algorithm. More precisely, to say that a device or organ computes is to say that there exists a modelling relationship of a certain kind between it and a formal specification of an algorithm and supporting architecture. The key issue is to delimit the phrase of a certain kind. I call this the problem of distinguishing between standard and nonstandard models of computation. The successful drawing of this distinction guards Turing's 1936 analysis of computation against (...) a difficulty that has persistently been raised against it, and undercuts various objections that have been made to the computational theory of mind. (shrink)

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)

For each positive n , two alternative axiomatizations of the theory of strings over n alphabetic characters are presented. One class of axiomatizations derives from Tarski's system of the Wahrheitsbegriff and uses the n characters and concatenation as primitives. The other class involves using n character-prefixing operators as primitives and derives from Hermes' Semiotik. All underlying logics are second order. It is shown that, for each n, the two theories are definitionally equivalent [or synonymous in the sense of deBouvere]. It (...) is further shown that each member of one class is synonymous with each member of the other class; thus that all of the theories are definitionally equivalent with each other and with Peano arithmetic. Categoricity of Peano arithmetic then implies categoricity of each of the above theories. (shrink)

NOW IN PAPERBACK"€"Starting from a collection of simple computer experiments"€"illustrated in the book by striking computer graphics"€"Stephen Wolfram shows how their unexpected results force a whole new way of looking at the operation of our universe.

According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.<br><br>.

The philosophy of artifacts is as marginal as it is one-sided. The majority of contributions to it are asides in works devoted to other subjects and focus on one characteristic feature: that artifacts are objects with functions. Indeed many artifacts, such as screwdrivers and toasters, come in functional kinds. Perhaps for this reason, philosophers elevated functions to the essences of artifacts or have developed general theories of function to describe artifacts along with their main subject: biological items. Most such theories (...) present one monolithic notion of function that applies to both. Dissident voices claim autonomy for artifact functions, or at least a small measure of conceptual independence, but it is seldom questioned that a theory of artifacts consists merely of a theory of artifact functions. (shrink)

One thing about technical artefacts that needs to be explained is how their physical make-up, or structure, enables them to fulfil the behaviour associated with their function, or, more colloquially, how they work. In this paper I develop an account of such explanations based on the familiar notion of mechanistic explanation. To accomplish this, I outline two explanatory strategies that provide two different types of insight into an artefact’s functioning, and show how human action inevitably plays a role in artefact (...) explanation. I then use my own account to criticize other recent work on mechanistic explanation and conclude with some general implications for the philosophy of explanation.Keywords: Artefact; Technical function; Explanation; Levels of explanation; Mechanisms. (shrink)

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions. # 2004 Elsevier Ltd. All rights reserved.

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions.

Many objects in the world have functions. Typewriters are for typing. Can-openers are for opening cans. Lawnmowers are for cutting grass. That is what these things are for. Every day around the world people attribute functions to objects. Some of the objects with functions are organs or parts of living organisms. Hearts are for pumping blood. Eyes are for seeing. Countless works in biology explain the “Form, Function, and Evolution of... ” everything from bee dances to elephant tusks to pandas’ (...) ‘thumbs’. Many scientific explanations, in areas as diverse as psychology, sociology, economics, medical research, and neuroscience, rest on appeals to the function and/or malfunction of things or systems. They talk of how humans and other organisms or their parts work, what their functions are, why they are present, and how different situations will affect them and how they will react. Philosophers, going back to Aristotle, used to make generous use of functions in describing objects, organisms, their interactions, and even as the basis of ethics and metaphysics. And yet, since the Enlightenment, talk of the function of natural objects, teleological function, began to be viewed with suspicion, as the mechanical model of the world replaced the old Aristotelian model. From a religious standpoint, it used to be easy to see how objects in the natural world could have natural functions, for God was said to instill functions by design throughout Creation. But philosophers became increasingly reluctant to invoke God to solve every difficult philosophical problem, and became unwilling to indulge in such religious explanations of teleology. (shrink)

Part of the distinction between artefacts, objects made by humans for particular purposes, and natural objects is that artefacts are subject to normative judgements. A drill, say, can be a good drill or a poor drill, it can function well or correctly or it can malfunction. In this paper I investigate how such judgements fit into the domain of the normative in general and what the grounds for their normativity are. Taking as a starting point a general characterization of normativity (...) proposed by Dancy, I argue how statements such as ‘this is a good drill’ or ‘this drill is malfunctioning’ can be seen to express normative facts, or the content of normative statements. What they say is that a user who has a desire to achieve a particular relevant outcome has a reason to use, or not to use, the artefact in question. Next this analysis is extended to show that not just statements that say that an artefact performs its function well or poorly, but all statements that ascribe a function to an artefact can be seen as expressing a normative fact. On this approach the normativity of artefacts is analyzed in terms of reasons on grounds of practical, and to a lesser extent theoretical, rationality. I close by investigating briefly to what extent reasons on moral grounds are, in the analysis adopted here, involved in the normativity of artefacts.Keywords: Artefact; Normativity; Instrumental reason; Practical rationality; Function; Use. (shrink)

Some have suggested that there is no fact to the matter as to whether or not a particular physical system relaizes a particular computational description. This suggestion has been taken to imply that computational states are not real, and cannot, for example, provide a foundation for the cognitive sciences. In particular, Putnam has argued that every ordinary open physical system realizes every abstract finite automaton, implying that the fact that a particular computational characterization applies to a physical system does not (...) tell oneanything about the nature of that system. Putnam''s argument is scrutinized, and found inadequate because, among other things, it employs a notion of causation that is too weak. I argue that if one''s view of computation involves embeddedness (inputs and outputs) and full causality, one can avoid the universal realizability results. Therefore, the fact that a particular system realizes a particular automaton is not a vacuous one, and is often explanatory. Furthermore, I claim that computation would not necessarily be an explanatorily vacuous notion even if it were universally realizable. (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)