Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are in general neither predictive nor explanatory and introduce avoidable conceptual confusions. I conclude that a correct understanding of natural selection requires explicitly causal models of reproductive success.

A common presupposition in the concepts literature is that concepts constitute a singular natural kind. If, on the contrary, concepts split into more than one kind, this literature needs to be recast in terms of other kinds of mental representation. We offer two new arguments that concepts, in fact, divide into different kinds: (a) concepts split because different kinds of mental representation, processed independently, must be posited to explain different sets of relevant phenomena; (b) concepts split because different kinds of mental representation, processed independently, must be posited to explain responses to different kinds of category. Whether these arguments are sound remains an open empirical question, to be resolved by future empirical and theoretical work.

In “Concepts Are Not a Natural Kind” (2005), I argued that the notion of concept in psychology and in neuropsychology fails to pick out a natural kind. Piccinini and Scott (2006, in this issue) have criticized the argument I used to support this conclusion. They also proposed two alternative arguments for a similar conclusion. In this reply, I rebut Piccinini and Scott's main objection against the argument proposed in “Concepts Are Not a Natural Kind.” Moreover, I show that the two alternative arguments developed by Piccinini and Scott are not promising for supporting the conclusion that concepts are not a natural kind.

Hermann Weyl succeeded in presenting a consistent overarching analysis that accounts for symmetry in (1) material artifacts, (2) natural phenomena, and (3) physical theories. Weyl showed that group theory is the underlying mathematical structure for symmetry in all three domains. But in this study Weyl did not include appeals to symmetry arguments which, for example, Einstein expressed as “for reasons of symmetry” (wegen der Symmetrie; aus Symmetriegründen). An argument typically takes the form of a set of premises and rules of inference that lead to a conclusion. Symmetry may enter an argument both in the premises and the rules of inference, and the resulting conclusion may also exhibit symmetrical properties. Taking our cue from Pierre Curie, we distinguish two categories of symmetry arguments, axiomatic and heuristic; they will be defined and then illustrated by historical cases.

In this paper, I offer a new defense of scientific realism, tailored for the Akaikean paradigm of statistical hypothesis testing. After proposing definitions of verisimilitude and predictive success, I use computer simulations to show how the latter depends on the former, even in the kind of case featured in a recent argument for instrumentalism.

David Malament has recently responded to David Albert's argument that classical electrodynamics is not time-reversal invariant by introducing a novel conception of time reversal, which supports the conventional view that under time reversal the magnetic field changes sign but the electric field remains unchanged. I will argue here that Malament's transformation has both passive and active versions. I will claim that the passive version is not relevant to Albert's argument, and the active version does not lead to the conventional transformation.