We provide a taxonomy of the two most important debates in the philosophy of the cognitive and neural sciences. The first debate is over methodological individualism: is the object of the cognitive and neural sciences the brain, the whole animal, or the animal—environment system? The second is over explanatory style: should explanation in cognitive and neural science be reductionist-mechanistic, interlevel mechanistic, or dynamical? After setting out the debates, we discuss the ways in which they are interconnected. Finally, we make some recommendations that we hope will help philosophers interested in the cognitive and neural sciences to avoid dead ends.

Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code’. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of graphs for expressing our theory of biological codes and discuss its utility in understanding biological systems.

This article shows that a slight variation of the argument in Milne 1996 yields the log-likelihood ratio l rather than the log-ratio measure r as “the one true measure of confirmation.”

Most models of generational succession in sexually reproducing populations necessarily move back and forth between genic and genotypic spaces. We show that transitions between and within these spaces are usually hidden by unstated assumptions about processes in these spaces. We also examine a widely endorsed claim regarding the mathematical equivalence of kin-, group-, individual-, and allelic-selection models made by Lee Dugatkin and Kern Reeve. We show that the claimed mathematical equivalence of the models does not hold.

Ecologists have proposed several incompatible definitions of ecological stability. Emulating physicists, mathematical ecologists commonly define it as Lyapunov stability. This formalizes the problematic concept by integrating it into a well-developed mathematical theory. The formalization also seems to capture the intuition that ecological stability depends on how ecological systems respond to perturbation. Despite these advantages, this definition is flawed. Although Lyapunov stability adequately characterizes perturbation responses of many systems studied in physics, it does not for ecological systems. This failure reveals a limitation of its underlying mathematical theory, and an important difference between dynamic systems modeling in physics and ecology.

According to a standard view of the second law of thermodynamics, our belief in the second law can be justified by pointing out that low-entropy macrostates are less probable than high-entropy macrostates, and then noting that a system in an improbable state will tend to evolve toward a more probable state. I would like to argue that this justification of the second law is unhelpful at best and wrong at worst, and will argue that certain puzzles sometimes associated with the second law are merely artifacts of this questionable justification.

This article suggests a ‘best alternative’ justification of induction (in the sense of Reichenbach) which is based on meta-induction. The meta-inductivist applies the principle of induction to all competing prediction methods which are accessible to her. It is demonstrated, and illustrated by computer simulations, that there exist meta-inductivistic prediction strategies whose success is approximately optimal among all accessible prediction methods in arbitrary possible worlds, and which dominate the success of every noninductive prediction strategy. The proposed justification of meta-induction is mathematically analytical. It implies, however, an a posteriori justification of object-induction based on the experiences in our world.

Skipper and Millstein (2005) argued that existing conceptions of mechanisms failed to “get at” natural selection but left open the possibility that a refined conception of mechanisms could resolve the problems that they identified. I respond to Skipper and Millstein, and argue that while many of their points have merit, their objections can be overcome and that natural selection can be characterized as a mechanism. In making this argument, I discuss the role of regularity in mechanisms, and develop an account of stochastic (i.e., probabilistic) mechanisms. Explaining the phenomenon of adaptation through the mechanism of natural selection illustrates the power and flexibility of using mechanistic strategies to explain natural phenomena.

This article investigates several consequences of a recent trend in philosophy of mind to shift the relata of realization from mental state–physical state to function-mechanism. It is shown, by applying both frameworks to the neuroscientific case study of memory consolidation, that, although this shift can be used to avoid the immediate antireductionist consequences of the traditional argument from multiple realizability, what is gained is a far more modest form of reductionism than recent philosophical accounts have intimated and neuroscientists themselves have claimed.

The epistemic state of complete ignorance is not a probability distribution. In it, we assign the same, unique, ignorance degree of belief to any contingent outcome and each of its contingent, disjunctive parts. That this is the appropriate way to represent complete ignorance is established by two instruments, each individually strong enough to identify this state. They are the principle of indifference (PI) and the notion that ignorance is invariant under certain redescriptions of the outcome space, here developed into the ‘principle of invariance of ignorance’ (PII). Both instruments are so innocuous as almost to be platitudes. Yet the literature in probabilistic epistemology has misdiagnosed them as paradoxical or defective since they generate inconsistencies when conjoined with the assumption that an epistemic state must be a probability distribution. To underscore the need to drop this assumption, I express PII in its most defensible form as relating symmetric descriptions and show that paradoxes still arise if we assume the ignorance state to be a probability distribution.

Evolutionary psychologists, among others, have used a method called “reverse engineering” to uncover (a) whether a trait was selected for, and (b) if so, why that trait was selected for. In this paper I argue that reverse engineering cannot deliver on either (a) or (b), and tends to pervert, rather than enhance, our knowledge of natural history. In particular, I expose as false a fundamental assumption of reverse engineering—namely, that all traits selected for a particular function will share some nontrivial properties.

The partial cooperation displayed by subjects in the Centipede Game deviates radically from the predictions of traditional game theory. Even standard, infinite population, evolutionary settings have failed to provide an explanation for this behavior. However, recent work in finite population evolutionary models has shown that such settings can produce radically different results from the standard models. This paper examines the evolution of partial cooperation in finite populations. The results reveal a new possible explanation that is not open to the standard models and gives us reason to be cautious when employing these otherwise helpful idealizations.

The structure-mapping theory has become the de facto standard account of analogies in cognitive science and philosophy of science. In this paper I propose a distinction between two kinds of domains and I show how the account of analogies based on structure-preserving mappings fails in certain (object-rich) domains, which are very common in mathematics, and how the axiomatic approach to analogies, which is based on a common linguistic description of the analogs in terms of laws or axioms, can be used successfully to explicate analogies of this kind. Thus, the two accounts of analogies should be regarded as complementary, since each of them is adequate for explicating analogies that are drawn between different kinds of domains. In addition, I illustrate how the account of analogies based on axioms has also considerable practical advantages, for example, for the discovery of new analogies.

After reviewing several failed arguments that laws cannot change, I use the laws’ special relation to counterfactuals to show how temporary laws would have to differ from eternal but time-dependent laws. Then I argue that temporary laws are impossible and that neither Lewis's nor Armstrong's analyses of law nicely accounts for the laws’ immutability.

When conceived as an empirical claim, it is natural to wonder how one might test the hypothesis of multiple realization. I consider general issues of testability, show how they apply specifically to the hypothesis of multiple realization, and propose an auxiliary assumption that, I argue, must be conjoined to the hypothesis of multiple realization to ensure its testability. I argue further that Bechtel and Mundale (1999) go astray because they fail to appreciate the need for this auxiliary assumption.

This article identifies a fundamental distinction in scientific practice: the mismatch between what scientists do and what they state they did when they communicate their findings in their publications. The insight that such a mismatch exists is not new. It was already implied in Hans Reichenbach's distinction between the contexts of discovery and justification, and it is taken for granted across the board in philosophy of science and science studies. But while there is general agreement that the mismatch exists, the epistemological implications of that mismatch are not at all clear. Philosophers, historians, and sociologists of different stripes have expressed widely different views about how one should understand and interpret the relation between what scientists do and what they state they did. This article surveys a number of approaches to the mismatch. Based on this survey, I offer an assessment of the epistemological significance of the mismatch and identify the major meta-epistemological challenges that it poses for the analysis of scientific practice.

There are two opposing traditions in contemporary quantum field theory (QFT). Mainstream Lagrangian QFT led to and supports the standard model of particle interactions. Algebraic QFT seeks to provide a rigorous consistent mathematical foundation for field theory, but cannot accommodate the local gauge interactions of the standard model. Interested philosophers face a choice. They can accept algebraic QFT on the grounds of mathematical consistency and general accord with the semantic conception of theory interpretation. This suggests a rejection of particle ontology. Or they can accept the standard model on the grounds of its established success. This alternative, which I defend, suggests revising philosophical accounts of scientific theory and finding some way of accommodating particles.