This paper describes an alternative to the common view that explanation in the special sciences involves subsumption under laws. According to this alternative, whether or not a generalization can be used to explain has to do with whether it is invariant rather than with whether it is lawful. A generalization is invariant if it is stable or robust in the sense that it would continue to hold under a relevant if it is stable or robust in the sense that it (...) would continue to hold under a relevant class of changes. Unlike lawfulness, invariance comes in degrees and has other features that are well suited to capture the characteristics of explanatory generalizations in the special sciences. For example, a generalization can be invariant even if it has exceptions or holds only over a limited spatio-temporal interval. The notion of invariance can be used to resolve a number of dilemmas that arise in standard treatments of explanatory generalizations in the special sciences. (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)

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.

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections (...) and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence and systems from The Cognitive Science Society. (shrink)

An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...) explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

In this powerful work of conceptual and analytical originality, the author argues for the primacy of the material arrangements of the laboratory in the dynamics of modern molecular biology. In a post-Kuhnian move away from the hegemony of theory, he develops a new epistemology of experimentation in which research is treated as a process for producing epistemic things. A central concern of the book is the basic question of how novelty is generated in the empirical sciences. In addressing this question, (...) the author brings French poststructuralist thinking—notably Jacques Derrida’s concepts of “différance” and “historiality”—to bear on the construction of epistemic things. Historiographical perspective shifts from the actors’ minds to their objects of manipulation. These epistemological and historical issues are illuminated in a detailed case study of a particular laboratory, that of the oncologist and biochemist Paul C. Zamecnik and his colleagues, located in a specific setting—the Collis P. Huntington Memorial Hospital of Harvard University at the Massachusetts General Hospital of Boston. The author traces how, between 1945 and 1965, this group developed an experimental system for synthesizing proteins in the test tube that put Zamecnik’s research team at the forefront of those who led biochemistry into the era of molecular biology. (shrink)

Kenneth F. Schaffner compares the practice of biological and medical research and shows how traditional topics in philosophy of science—such as the nature of theories and of explanation—can illuminate the life sciences. While Schaffner pays some attention to the conceptual questions of evolutionary biology, his chief focus is on the examples that immunology, human genetics, neuroscience, and internal medicine provide for examinations of the way scientists develop, examine, test, and apply theories. Although traditional philosophy of science has regarded scientific discovery—the (...) questions of creativity in science—as a subject for psychological rather than philosophical study, Schaffner argues that recent work in cognitive science and artificial intelligence enables researchers to rationally analyze the nature of discovery. As a philosopher of science who holds an M.D., he has examined biomedical work from the inside and uses detailed examples from the entire range of the life sciences to support the semantic approach to scientific theories, addressing whether there are "laws" in the life sciences as there are in the physical sciences. Schaffner's novel use of philosophical tools to deal with scientific research in all of its complexity provides a distinctive angle on basic questions of scientific evaluation and explanation. (shrink)

Rasmussen (1993) argues that, because electron microscopists did not use robustness and would not have been warranted in using it as a criterion for the reality or the artifactuality of mesosomes, the bacterial mesosome serves as a test case for robustness that it fails. I respond by arguing that a more complete reading of the research literature on the mesosome shows that ultimately the more robust body of data did not support the mesosome and that electron microscopists used and were (...) warranted in using robustness as a criterion for the artifactuality of mesosomes. (shrink)

This innovative book focuses on the development of the gene theory as a case study in scientific creativity.

Contemporary cognitive neuropsychology attempts to infer unobserved features of normal human cognition, or ‘cognitive architecture’, from experiments with normals and with brain-damaged subjects in whom certain normal cognitive capacities are altered, diminished, or absent. Fundamental methodological issues about the enterprise of cognitive neuropsychology concern the characterization of methods by which features of normal cognitive architecture can be identified from such data, the assumptions upon which the reliability of such methods are premised, and the limits of such methods—even granting their assumptions—in (...) resolving uncertainties about that architecture. With some idealization, the question of the capacities of various experimental designs in cognitive neuropsychology to uncover cognitive architecture can be reduced to comparatively simple questions about the prior assumptions investigators are willing to make. This paper presents some of simplest of those reductions. (shrink)

New research tools such as PET can produce dramatic results. But they can also produce dramatic artifacts. Why is PET to be trusted? We examine both the rationale that justifies interpreting PET as measuring brain activity and the strategies for interpreting PET results functionally. We show that functional ascriptions with PET make important assumptions and depend critically on relating PET results to those secured through other research techniques.

The aim of cognitive neuropsychology is to articulate the functional architecture underlying normal cognition, on the basis of congnitive performance data involving brain-damaged subjects. Throughout the history of the subject, questions have been raised as to whether the methods of neuropsychology are adequate to its goals. The question has been reopened by Glymour [1994], who formulates a discovery problem for cognitive neuropsychology, in the sense of formal learning theory, concerning the existence of a reliable methodology. It appears that the discovery (...) problem may be insoluble in principle! I propose a modified formulation of Glymour's discovery problem and argue that a sceptical conclusion about the possiblity of cognitive neuropsychology as an empirical science is not warranted. (shrink)

New research tools such as PET can produce dramatic results. But they can also produce dramatic artifacts. Why is PET to be trusted? We examine both the rationale that justifies interpreting PET as measuring brain activity and the strategies for interpreting PET results functionally. We show that functional ascriptions with PET make important assumptions and depend critically on relating PET results to those secured through other research techniques.

The unifying theme of these thirteen essays is understanding.

Reference to mechanisms is virtually ubiquitous in science and its philosophy. Yet, the concept of a mechanism remains largely unanalyzed; So too for its possible applications in thinking about scientific explanation, experimental practice, and theory structure. This dissertation investigates these issues in the context of contemporary neurobiology. ;The theories of neurobiology are hierarchically organized descriptions of mechanisms that explain functions. Mechanisms are the coordinated activities of entities by virtue of which that function is performed. Since the activities composing mechanisms are (...) often susceptible to mechanical redescription themselves, theories in neurobiology have a characteristic hierarchical structure. The activities of entities at one level are the sub-activities of those at a higher level. ;This hierarchy reveals a fundamental symmetry of functional and mechanical descriptions. Functions are privileged activities of entities; they are privileged because they constitute a stage in some higher-level mechanism. The privileged activities of entities, in turn, are explained by detailing the stages of activity in the lower-level mechanism. Functional and mechanical descriptions are different tools for situating activities, properties, and entities into a hierarchy of activities. They are not competing kinds of description. Experimental techniques for testing such descriptions reflect this symmetry. ;Philosophical discussions of inter-level explanatory relationships have traditionally been framed by reference to inter-theoretic reduction models. The representational strictures of first order predicate calculus and the epistemological strictures logical empiricism combine in this reduction model to focus attention upon issues of identity and deriveability; these are entirely peripheral to the explanatory aims of mechanical explanation. Mechanical explanation is causal. Derivational models of explanation do not adequately reflect the importance of activities in rendering phenomena intelligible. ;Activities are kinds of change. "Bonding," "diffusing," "transcribing," "opening," and "attracting" all describe different kinds of transformation. Salmon's modified process theory is helpful in understanding the role of entities and properties in causal interactions; but it ultimately makes no room for kinds of change in the explanatory cupboard. We make change intelligible by identifying and characterizing its different kinds and relating these to activities that are taken to be fundamental for a science at a time. (shrink)

We argue in this paper that so-called new wave reductionism fails to capture the nature of the interlevel relations between psychology and neuroscience. Bickle (1995, Psychoneural reduction of the genuinely cognitive: some accomplished facts, Philosophical Psychology, 8, 265-285; 1998, Psychoneural reduction: the new wave, Cambridge, MA: MIT Press) has claimed that a (bottom-up) reduction of the psychological concepts of learning and memory to the concepts of neuroscience has in fact already been accomplished. An investigation of current research on the phenomenon (...) of long-term potentiation reveals that this claim overstates the facts. Both the psychological and the neural concepts involved have not yet stabilized and face further correction under the influence of both bottom-up and top-down selection pressures. In addition, psychological concepts often refer to functions, and functions are indispensable and irreducible. Function ascriptions pick out objective patterns involving historical factors and distal goals. This view of functions implies that psychological facts cannot be simply read off from the neurophysiological facts. Although psychological theorizing is constrained by neurophysiology (and vice versa), psychology remains distinct at least to some degree. (shrink)