The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of processes at (...) the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level. (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)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

In this paper I offer an analysis of causation based upon a theory of mechanisms-complex systems whose internal parts interact to produce a system's external behavior. I argue that all but the fundamental laws of physics can be explained by reference to mechanisms. Mechanisms provide an epistemologically unproblematic way to explain the necessity which is often taken to distinguish laws from other generalizations. This account of necessity leads to a theory of causation according to which events are causally related when (...) there is a mechanism that connects them. I present reasons why the lack of an account of fundamental physical causation does not undermine the mechanical account. (shrink)

This book provides a comprehensive guide to the conceptual methodological, and epistemological problems of biology, and treats in depth the major developments in molecular biology and evolutionary theory that have transformed both biology and its philosophy in recent decades. At the same time the work is a sustained argument for a particular philosophy of biology that unifies disparate issues and offers a framework for expectations about the future directions of the life sciences. The argument explores differences between autonomist and anti-autonomist (...) views of biology. The result is a vindication of reductionism, but one that is unexpectedly hollow. For it leaves the exponents of the autonomy of biology from physical science with as much as their view of biology really requires - and rather more than the reductionist might comfortably concede. Professor Rosenberg shows how the problems of the philosophy of biology are interconnected and how their solutions are interdependent, However, this book focuses more on the direct concerns of biologists, rather than the traditional agenda of philosophers' problems about biology. This departure from earlier books on the subject results both in greater understanding and relevance of the philosophy of science to biology as a whole. (shrink)

Do the sciences aim to uncover the structure of nature, or are they ultimately a practical means of controlling our environment? In Instrumental Biology, or the Disunity of Science, Alexander Rosenberg argues that while physics and chemistry can develop laws that reveal the structure of natural phenomena, biology is fated to be a practical, instrumental discipline. Because of the complexity produced by natural selection, and because of the limits on human cognition, scientists are prevented from uncovering the basic structure of (...) biological phenomena. Consequently, biology and all of the disciplines that rest upon it--psychology and the other human sciences--must aim at most to provide practical tools for coping with the natural world rather than a complete theoretical understanding of it. (shrink)

Functionalists in philosophy of mind traditionally raise two major arguments against the type identity theory: (1) psychological states are _multiply realizable_ so that there are no one-to-one mappings of psychological states onto neural states and (2) the most that evidence could ever establish is the _correlation_ of psychological and neural states, not their identity. We defend a variant on the traditional type identity theory which we call _heuristic identity theory_ (HIT) against both of these objections. Drawing its inspiration from scientific (...) practice, heuristic identity theory construes identity claims as hypotheses that guide subsequent inquiry, not as conclusions of the research. (shrink)