It is argued that we cannot understand the notion of proper functions of artefacts independently of social notions. Functions of artefacts are related to social facts via the use of artefacts. The arguments in this article can be used to improve existing function theories that look to the causal history of artefacts to determine the function. A view that takes the intentions of designers into account to determine the proper function is both natural and often correct, but it is shown (...) that there are exceptions to this. Taking a social constitutive element into account may amend these backwards looking theories. An improved theory may either have a disjunctive form—either the history or collective intentions determine the proper function—or, as is suggested in the article, be in the form of an encompassing account that views the designers’ intentions as social, in so far as they are accepted by the users. Designers have authority, which is a social fact. The views argued for here are applied to two existing theories of artefact functions, a causal historic approach and an action theoretic approach.Keywords: Function; Artefact; Evaluative judgement; Action; Collective intentionality. (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.

Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes a section (...) of accessible introductory pieces, as well as more advanced and technical pieces, and will make essential work in the philosophy of science readily available to both scholars and students. (shrink)

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.

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)

This collection of 17 essays by the author offers a comprehensive theory of mind, encompassing traditional issues of consciousness and free will.

In exploring the nature of psychological explanation, this book looks at how psychologists theorize about the human ability to calculate, to speak a language and the like. It shows how good theorizing explains or tries to explain such abilities as perception and cognition. It recasts the familiar explanations of "intelligence" and "cognitive capacity" as put forward by philosophers such as Fodor, Dennett, and others in terms of a theory of explanation that makes established doctrine more intelligible to professionals and their (...) students.In particular, the book shows that vestigial adherence to the positivists' D-N model has distorted the view of philosophers of science about what psychologists (and biologists) do and has masked the real nature of explanation. Major sections in the book cover Analysis and Subsumption; Functional Analysis; Understanding Cognitive Capacities; and Historical Reflections.Robert Cummins is Associate Professor of Philosophy at the University of Illinois, Chicago Circle. A Bradford Book. (shrink)

The purpose of this paper is to set forth a sense in which programs can and do explain behavior, and to distinguish from this a number of senses in which they do not. Once we are tolerably clear concerning the sort of explanatory strategy being employed, two rather interesting facts emerge; (1) though it is true that programs are "internally represented," this fact has no explanatory interest beyond the mere fact that the program is executed; (2) programs which are couched (...) in information processing terms may have an explanatory interest for a given range of behavior which is independent of physiological explanations of the same range of behavior. (shrink)

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)

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)

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)

Hempel's high probability requirement asserts that any rationally acceptable answer to the question 'Why did event X occur?' must offer information which shows that X was to be expected at least with reasonable probability. Salmon rejected this requirement in his S-R model. This led to a series of paradoxical consequences, such as the assertion that an explanation of an event can both lower its probability and make it arbitrarily low, and the assertion that the explanation of an outcome would have (...) qualified as an explanation of its non-occurrence as well. We argue that if inductive explanations are to be seen as generalizations of the causal-deterministic model, or if they are to be seen as satisfying the requirement--fulfilled by the D-N model--that explanations ought to identify certain features of the universe that are nomically responsible for the explanadum event, then the high probability requirement seems to be unacceptable. If this is so, a realistically inspired theory of inductive explanation will be committed to the paradoxes that follow from Salmon's model. (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)

Outlines the etiological theory of normative functionality. Analysis of the autonomous system; Function of systems-oriented approaches; Specifications of system identity.

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)

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)

Philosophers of neuroscience have traditionally described interfield integration using reduction models. Such models describe formal inferential relations between theories at different levels. I argue against reduction and for a mechanistic model of interfield integration. According to the mechanistic model, different fields integrate their research by adding constraints on a multilevel description of a mechanism. Mechanistic integration may occur at a given level or in the effort to build a theory that oscillates among several levels. I develop this alternative model using (...) a putative exemplar of reduction in contemporary neuroscience: the relationship between the psychological phenomena of learning and memory and the electrophysiological phenomenon known as Long-Term Potentiation. A new look at this historical episode reveals the relative virtues of the mechanistic model over reduction as an account of interfield integration. (shrink)

It is widely assumed that the explanatory states of scientific psychology are type-individuated by their semantic or intentional properties. First, I argue that this assumption is implausible for theories like David Marr's [1982] that seek to provide computational or syntactic explanations of psychological processes. Second, I examine the implications of this conclusion for the debate over psychological individualism. While most philosophers suppose that syntactic states supervene on the intrinsic physical states of information-processing systems, I contend they may not. Syntatic descriptions (...) must be adequately constrained, and the most plausible such constraints appeal to a system's teleological function or design and hence to its history. As a result, physical twins may not realize the same syntactic states. (shrink)

Machamer, Darden, and Craver argue that causal explanations explain effects by describing the operations of the mechanisms which produce them. One of this paper’s aims is to take advantage of neglected resources of Mechanism to rethink the traditional idea that actual or counterfactual natural regularities are essential to the distinction between causal and non-causal co-occurrences, and that generalizations describing natural regularities are essential components of causal explanations. I think that causal productivity and regularity are by no means the same thing, (...) and that the Regularists are mistaken about the roles generalizations play in causal explanation. Humean, logical empiricist, and other Regularist accounts of causal explanation have had the unfortunate effect of distracting philosophers’ from important non-explanatory scientific uses of laws and lesser generalizations which purport to describe natural regularities. My second aim is to characterize some of these uses, illustrating them with examples from neuroscientific research. (shrink)

In this note, I will discuss one issue concerning the main argument of Mind in a Physical World (Kim, 1998), the Causal Exclusion Argument. The issue is whether it is a consequence of the Causal Exclusion Argument that all macro level causation (that is, causation above the level of fundamental physics) is an illusion, with all of the apparent causal powers of mental and other macro properties draining into the bottom level of physics. I will argue that such a consequence (...) would give us reason to reject the Causal Exclusion Argument. But there is also a stronger challenge, the charge that, if there is no bottom level of physics, the Causal Exclusion Argument has the consequence that “causal powers would drain away into a bottomless pit and there wouldn’t be any causation anywhere.” (81--page numbers that are not attributed to other works are to Kim, 1998). (shrink)

The claim of the multiple realizability of mental states by brain states has been a major feature of the dominant philosophy of mind of the late 20th century. The claim is usually motivated by evidence that mental states are multiply realized, both within humans and between humans and other species. We challenge this contention by focusing on how neuroscientists differentiate brain areas. The fact that they rely centrally on psychological measures in mapping the brain and do so in a comparative (...) fashion undercuts the likelihood that, at least within organic life forms, we are likely to find cases of multiply realized psychological functions. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

An alarming number of philosophers and cognitive scientists have argued that mind extends beyond the brain and body. This book evaluates these arguments and suggests that, typically, it does not. A timely and relevant study that exposes the need to develop a more sophisticated theory of cognition, while pointing to a bold new direction in exploring the nature of cognition Articulates and defends the “mark of the cognitive”, a common sense theory used to distinguish between cognitive and non-cognitive processes Challenges (...) the current popularity of extended cognition theory through critical analysis and by pointing out fallacies and shortcoming in the literature Stimulates discussions that will advance debate about the nature of cognition in the cognitive sciences. (shrink)

This book, based on Jaegwon Kim's 1996 Townsend Lectures, presents the philosopher's current views on a variety of issues in the metaphysics of the mind...

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...) the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?". (shrink)

In this paper Lucas comes back to Gödelian argument against Mecanism to clarify some points. First of all, he explains his use of Gödel’s theorem instead of Turing’s theorem, showing how Gödel’ theorem, but not Turing’s theorem, raises questions concerning truth and reasoning that bear on the nature of mind and how Turing’s theorem suggests that there is something that cannot be done by any computers but not that it can be done by human minds. He considers moreover how Gödel’s (...) theorem can be interpreted as a sophisticated form of the Cretan paradox, posed by Epimenides, able to escape the viciously self-referential nature of the Cretan paradox, and how it can be used against Mechanism as a schema of disproof. Finally, Lucas suggests some answers to the most recurrent criticisms against his argument: criticisms about the implicit idealisation in the way he set up the context between mind and machine; questions concerning modality and finitude, issues of transfinite arithmetic; questions concerning the need of formalizing rational inference and some questions about consistency. (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.