The use of informational terms is widespread in molecular and developmental biology. The usage dates back to Weismann. In both protein synthesis and in later development, genes are symbols, in that there is no necessary connection between their form (sequence) and their effects. The sequence of a gene has been determined, by past natural selection, because of the effects it produces. In biology, the use of informational terms implies intentionality, in that both the form of the signal, and the response to it, have evolved by selection. Where an engineer sees design, a biologist sees natural selection.

Several arguments are considered which purport to demonstrate the impossibility of theory-neutral observation. The most important of these infers the continuity of observation with theory from the presumed continuity of perception with cognition, a doctrine widely espoused in recent cognitive psychology. An alternative psychological account of the relation between cognition and perception is proposed and its epistemological consequences for the observation/theory distinction are then explored.

I defend a view of the species category, pluralistic realism, which is designed to do justice to the insights of many different groups of systematists. After arguing that species are sets and not individuals, I proceed to outline briefly some defects of the biological species concept. I draw the general moral that similar shortcomings arise for other popular views of the nature of species. These shortcomings arise because the legitimate interests of biology are diverse, and these diverse interests are reflected in different legitimate approaches to the classification of organisms. In the final section, I show briefly how the pluralistic approach can help to illuminate some areas of biological and philosophical dispute.

Ernst Mayr has argued that Darwinian theory discredited essentialist modes of thought and replaced them with what he has called “population thinking”. In this paper, I characterize essentialism as embodying a certain conception of how variation in nature is to be explained, and show how this conception was undermined by evolutionary theory. The Darwinian doctrine of evolutionary gradualism makes it impossible to say exactly where one species ends and another begins; such line-drawing problems are often taken to be the decisive reason for thinking that essentialism is untenable. However, according to the view of essentialism I suggest, this familiar objection is not fatal to essentialism. It is rather the essentialist's use of what I call the natural state model for explaining variation which clashes with evolutionary theory. This model implemented the essentialist's requirement that properties of populations be defined in terms of properties of member organisms. Requiring such constituent definitions is reductionistic in spirit; additionally, evolutionary theory shows that such definitions are not available, and, moreover, that they are not needed to legitimize population-level concepts. Population thinking involves the thesis that population concepts may be legitimized by showing their connections with each other, even when they are not reducible to concepts applying at lower levels of organization. In the paper, I develop these points by describing Aristotle's ideas on the origins of biological variation; they are a classic formulation of the natural state model. I also describe how the development of statistical ideas in the 19th century involved an abandoning of the natural state model.

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.

The concept of “fitness” is a notion of central importance to evolutionary theory. Yet the interpretation of this concept and its role in explanations of evolutionary phenomena have remained obscure. We provide a propensity interpretation of fitness, which we argue captures the intended reference of this term as it is used by evolutionary theorists. Using the propensity interpretation of fitness, we provide a Hempelian reconstruction of explanations of evolutionary phenomena, and we show why charges of circularity which have been levelled against explanations in evolutionary theory are mistaken. Finally, we provide a definition of natural selection which follows from the propensity interpretation of fitness, and which handles all the types of selection discussed by biologists, thus improving on extant definitions.

The intention and the result of a scientific inquiry is to obtain an understanding and a control of some part of the universe. This statement implies a dualistic attitude on the part of scientists. Indeed, science does and should proceed from this dualistic basis. But even though the scientist behaves dualistically, his dualism is operational and does not necessarily imply strict dualistic metaphysics.

Four current accounts of theory reduction are presented, first informally and then formally: (1) an account of direct theory reduction that is based on the contributions of Nagel, Woodger, and Quine, (2) an indirect reduction paradigm due to Kemeny and Oppenheim, (3) an “isomorphic model” schema traceable to Suppes, and (4) a theory of reduction that is based on the work of Popper, Feyerabend, and Kuhn. Reference is made, in an attempt to choose between these schemas, to the explanation of physical optics by Maxwell's electromagnetic theory, and to the revisions of genetics necessitated by partial biochemical reductions of genetics. A more general reduction schema is proposed which: (1) yields as special cases the four reduction paradigms considered above, (2) seems to be in better accord with both the canons of logic and actual scientific practice, and (3) clarifies the problems of meaning variance and ontological reduction.

Experimental research is commonly held up as the paradigm of “good” science. Although experiment plays many roles in science, its classical role is testing hypotheses in controlled laboratory settings. Historical science (which includes work in geology, biology, and astronomy, as well as paleontology and archaeology) is sometimes held to be inferior on the grounds that its hypothesis cannot be tested by controlled laboratory experiments. Using contemporary examples from diverse scientific disciplines, this paper explores differences in practice between historical and experimental research vis-à-vis the testing of hypotheses. It rejects the claim that historical research is epistemically inferior. For as I argue, scientists engage in two very different patterns of evidential reasoning and, although there is overlap, one pattern predominates in historical research and the other pattern predominates in classical experimental research. I show that these different patterns of reasoning are grounded in an objective and remarkably pervasive time asymmetry of nature.

This paper defends an inferential conception of scientific representation. It approaches the notion of representation in a deflationary spirit, and minimally characterizes the concept as it appears in science by means of two necessary conditions: its essential directionality and its capacity to allow surrogate reasoning and inference. The conception is defended by showing that it successfully meets the objections that make its competitors, such as isomorphism and similarity, untenable. In addition the inferential conception captures the objectivity of the cognitive representations used by science, it sheds light on their truth and completeness, and it explains the source of the analogy between scientific and artistic modes of representation.

I analyze a number of widespread misconceptions concerning species. The species category, defined by a concept, denotes the rank of a species taxon in the Linnaean hierarchy. Biological species are reproducing isolated from each other, which protects the integrity of their genotypes. Degree of morphological difference is not an appropriate species definition. Unequal rates of evolution of different characters and lack of information on the mating potential of isolated populations are the major difficulties in the demarcation of species taxa.

The contention that psychological states are multiply realizable in different substrates has been used to support the contention that neuroscience is not likely to be very useful in guiding an understanding how cognition works. But in the context of scientific research, how seriously should we really take this threat of multiple realizability? By examining how brain areas are identified in neuroscience (where the approach is comparative and employs functional criteria), we show that the skepticism about neuroscience's role in understanding cognition is misguided and that the apparent success of multiple realizability is based on methodological error. With respect to the former point, we develop and analyze a representative case in which the organization of the brain provides crucial information about the organization of the cognitive system. Then we show that the initial plausibility of claims to multiple realizability rest on (a) mismatching a broad-grained criterion (to show sameness of psychological states) with a finegrained criterion (to differentiate brain states), and (b) a failure to attend to the purposes for which taxonomies of brain and psychological states are developed.

The doctrine that the character of our perceptual knowledge is plastic, and can vary substantially with the theories embraced by the perceiver, has been criticized in a recent paper by Fodor. His arguments are based on certain experimental facts and theoretical approaches in cognitive psychology. My aim in this paper is threefold: (1) to show that Fodor's views on the impenetrability of perceptual processing do not secure a theory-neutral foundation for knowledge; (2) to show that his views on impenetrability are almost certainly false; and (3) to provide some additional arguments for, and illustrations of, the theoretical character of all observation judgments.

This paper examines the relationship between simulation and experiment. Many discussions of simulation, and indeed the term “numerical experiments,” invoke a strong metaphor of experimentation. On the other hand, many simulations begin as attempts to apply scientific theories. This has lead many to characterize simulation as lying between theory and experiment. The aim of the paper is to try to reconcile these two points of view—to understand what methodological and epistemological features simulation has in common with experimentation, while at the same time keeping a keen eye on simulation's ancestry as a form of scientific theorizing. In so doing, it seeks to apply some of the insights of recent work on the philosophy of experiment to an aspect of theorizing that is of growing philosophical interest: the construction of local models.

Evidence-Based Medicine is a relatively new movement that seeks to put clinical medicine on a firmer scientific footing. I take it as uncontroversial that medical practice should be based on best evidence—the interesting questions concern the details. This paper tries to move towards a coherent and unified account of best evidence in medicine, by exploring in particular the EBM position on RCTs (randomized controlled trials).

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems.

We argue that dynamical and mathematical models in systems and cognitive neuroscience explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phenomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are explored.

The ultimate source of explanation in biology is the principle of natural selection. Natural selection means differential reproduction of genes and gene combinations. It is a mechanistic process which accounts for the existence in living organisms of end-directed structures and processes. It is argued that teleological explanations in biology are not only acceptable but indeed indispensable. There are at least three categories of biological phenomena where teleological explanations are appropriate.

Through a study of a sophisticated contemporary scientific experiment, it is shown how and why use of the term ‘observation’ in reference to that experiment departs from ordinary and philosophical usages which associate observation epistemically with perception. The role of “background information” is examined, and general conclusions are arrived at regarding the use of descriptive language in and in talking about science. These conclusions bring out the reasoning by which science builds on what it has learned, and, further, how that process of building consists not only in adding to our substantive knowledge, but also in increasing our ability to learn about nature, by extending our ability to observe it in new ways. The argument of this paper is thus a step toward understanding how it is that all our knowledge of nature rests on observation.

Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice. The practice has also been severely criticized by Steven Orzack and Elliott Sober, who claim that it is a nonempirical form of confirmation, effective only under unusual circumstances. This paper addresses Orzack and Sober's criticisms by giving a new account of robustness analysis and showing how the practice can identify robust theorems. Once the structure of robust theorems is clearly articulated, it can be shown that such theorems have a degree of confirmation, despite the lack of direct empirical evidence for their truth.