The paper defends the doctrine that Linnaean taxa, including species, have essences that are, at least partly, underlying intrinsic, mostly genetic, properties. The consensus among philosophers of biology is that such essentialism is deeply wrong, indeed incompatible with Darwinism. I argue that biological generalizations about the morphology, physiology, and behavior of species require structural explanations that must advert to these essential properties. The paper concludes by summarizing my responses to the objection that, according to current “species concepts,” species are relational, and to the objection that essentialism cannot accommodate features of Darwinism associated with variation and change.

The architects of the modern synthesis banned essentialism from evolutionary theory. This rejection of essentialism was motivated by Darwin's theory of natural selection, and the continuity of evolutionary transformation. Contemporary evolutionary biology witnesses a renaissance of essentialism in three contexts: “origin essentialism” with respect to species and supraspecific taxa, the bar coding of species on the basis of discontinuities of DNA variation between populations, and the search for laws of evolutionary developmental biology. Such “new essentialism” in contemporary biology must be of a new kind that accommodates relational (extrinsic) properties as historical essences and cluster concepts of natural kinds.

The received view in the philosophy of biology is that biological taxa (species and higher taxa) do not have essences. Recently, some philosophers (Boyd, Devitt, Griffiths, LaPorte, Okasha, and Wilson) have suggested new forms of biological essentialism. They argue that according to these new forms of essentialism, biological taxa do have essences. This article critically evaluates the new biological essentialism. This article's thesis is that the costs of adopting the new biological essentialism are many, yet the benefits are none, so there is no compelling reason to resurrect essentialism concerning biological taxa.

Several authors have argued that higher taxa are monophyletic homeostatic property cluster natural kinds. On the traditional definition of monophyly, this will not work: the emergence of taxon-defining homeostatic property clusters would not always correspond to unique speciation events. An alternative conception of monophyly is developed and advocated, which can accommodate the homeostatic property cluster proposal. Recent work in philosophy of science shows that it meets appropriate standards of objectivity and precision.

Recently philosophers of biology have argued over whether or not Newtonian mechanics provides a useful analogy for thinking about evolutionary theory. For philosophers, the canonical presentation of this analogy is Sober's. Matthen and Ariew and Walsh, Lewins, and Ariew argue that this analogy is deeply wrong-headed. Here I argue that the analogy is indeed useful, however, not in the way it is usually interpreted. The Newtonian analogy depends on having the proper analogue of Newton's First Law. That analogue is what McShea and Brandon call the Zero Force Evolutionary Law (ZFEL). According to the ZFEL, change, not stasis, is the default state of evolutionary systems.

The traditional view of evolutionary theory asserts that we can usefully understand natural selection, drift, mutation, migration, and the system of mating as forces that cause evolutionary change. Recently, Denis Walsh and Robert Brandon have objected to this view. Walsh argues that the traditional view faces a fatal dilemma and that the force analogy must be rejected altogether. Brandon accepts the force analogy but argues that drift, rather than the Hardy-Weinberg law, is the best candidate for a zero-force law. Here I defend the traditional view against these objections.

I consider and reject William Whewell's attack on the inductivism of Isaac Newton and John Stuart Mill, as well as John Norton's attack on any universal system of inductive rules. I also explain how a system of inductive rules of the sort proposed by Newton and Mill should be understood.

This essay criticizes John Norton's 2010 defense of the thesis that “all induction is local.” Norton's local inductions are bound, if cogent, to involve general principles, and the need to accredit these general principles threatens to lead to all the usual problems associated with the ‘problem of induction’. Norton, in fact, recognizes this threat, but his responses are inadequate. The right response involves not induction but a sophisticated version of hypothetico-deduction. Norton's secondary thesis—that if there is a general account of cogent scientific reasoning, then it is certainly not the one supported by personalist Bayesians—is also criticized.

Does Hume's skeptical critique of inductive reasoning depend for its force on the assumption that such reasoning is a matter of following general rules? I explore this question in connection with John Norton's “Material Theory of Induction.”

In a material theory of induction, inductive inferences are warranted by facts that prevail locally. This approach, it is urged, is preferable to formal theories of induction in which the good inductive inferences are delineated as those conforming to universal schemas. An inductive inference problem concerning indeterministic, nonprobabilistic systems in physics is posed, and it is argued that Bayesians cannot responsibly analyze it, thereby demonstrating that the probability calculus is not the universal logic of induction.

This essay uses Györgi Buzsáki's use of EEG data to draw conclusions about brain function as an example to show that investigators sometimes draw conclusions from noisy data by analyzing the noise rather than by extracting a signal from it. The example makes vivid some important differences between McAllister's, Woodward's, and my ideas about how data are interpreted.

This essay attempts to provide additional motivation for the data/phenomena framework advocated in Bogen and Woodward, “Saving the Phenomena” (1988).

This article defends the following claims. First, for patterns exhibited in empirical data, there is no criterion on which to demarcate patterns that are physically significant and patterns that are not physically significant. I call a pattern physically significant if it corresponds to a structure in the world. Second, all patterns must be regarded as physically significant. Third, distinct patterns must be regarded as providing evidence for distinct structures in the world. Fourth, in consequence, the world must be conceived as showing all possible structures.

Bogen and Woodward argued the indirect connection between data and theory in terms of their conception of “phenomena.” I outline and elaborate on their presentation. To illuminate the connection with contemporary thinking in terms of models, I distinguish between phenomena tokens, representations of which can be identified with data models, and phenomena types that can be identified with relatively low-lying models or aspects of models in the model hierarchy. Throughout I stress the role of idealization in these considerations.

Taking Bogen and Woodward's discussion of data and phenomena as his starting point, McAllister presents a challenge to scientific realism. I discuss this challenge and offer a suggestion for how the scientific realist could respond to both its epistemic and ontological aspects. In so doing, I urge that the scientific realist should not reject ontological pluralism from the start, but should seek to explore versions of scientific realism that leave open the possibility of certain kinds of pluralist ontology. I investigate the available options (in terms of foundationalism, reductionism, and universalism) and use a law-constitutive approach to offer a strategy for the scientific realist who is open-minded about ontological pluralism.

What are the relative epistemic merits of building prosthetic models versus building nonprosthetic models and simulations? I argue that prosthetic models provide a sufficient test of affordance validity, that is, of whether the target system affords mechanisms that can be commandeered by a prosthesis. In other respects, prosthetic models are epistemically on par with nonprosthetic models. I focus on prosthetics in neuroscience, but the results are general. The goal of understanding how brain mechanisms work under ecologically and physiologically relevant conditions is narrow compared to the search for maker's knowledge about how the brain can be made to work for us.

Computationalism—the view that cognition is computation—has always been controversial. It faces two types of objection. According to insufficiency objections, computation is insufficient for some cognitive phenomenon X. According to objections from neural realization, cognitive processes are realized by neural processes, but neural processes have feature Y, and having Y is incompatible with being (or realizing) computations. In this article, I explain why computationalism has survived these objections. To adjudicate the dispute between computationalism and its foes, I will conclude that we need a better account of computation.

What does it mean to say that a physical system computes or, specifically, to say that the nervous system computes? One answer, endorsed here, is that computing is a sort of modeling. I trace this line of answer in the conceptual and philosophical work conducted over the last 3 decades by researchers associated with the University of California, San Diego (UCSD). The linkage between their work and the modeling notion is no coincidence: the modeling notion aims to account for the computational approach in neuroscience, and UCSD has been home to central studies in neurophilosophy, connectionism, and computational neuroscience.

What role does the concept of representation play in the contexts of experimentation and explanation in cognitive neurobiology? In this article, a distinction is drawn between minimal and substantive roles for representation. It is argued by appeal to a case study that representation currently plays a role in cognitive neurobiology somewhere in between minimal and substantive and that this is problematic given the ultimate explanatory goals of cognitive neurobiological research. It is suggested that what is needed is for representation to instead play a substantive role.