The dominant neuroscientific theory of spatial memory is, like many theories in neuroscience, a multilevel description of a mechanism. The theory links the activities of molecules, cells, brain regions, and whole organisms into an integrated sketch of an explanation for the ability of organisms to navigate novel environments. Here I develop a taxonomy of interlevel experimental strategies for integrating the levels in such multilevel mechanisms. These experimental strategies include activation strategies, interference strategies, and additive strategies. These strategies are mutually reinforcing, providing a kind of interlevel and intratheoretic robustness that has not previously been recognized.

A condition is formulated in terms of the probabilities of two pairs of correlated events in a classical probability space which is necessary for the two correlations to have a single (Reichenbachian) common-cause and it is shown that there exists pairs of correlated events, probabilities of which violate the necessary condition. It is concluded that different correlations do not in general have a common common-cause. It is also shown that this conclusion remains valid even if one weakens slightly Reichenbach's definition of common-cause. The significance of the difference between common-causes and common common-causes is emphasized from the perspective of Reichenbach's Common Cause Principle.

In a recent article Humphreys has developed an intriguing proposal for making sense of emergence. The crucial notion for this purpose is what he calls “fusion” and his paradigm for it is quantum nonseparability. In what follows, we will develop this position in more detail, and then discuss its ramifications and limitations. Its ramifications are quite radical; its limitations are substantial. An alternative approach to emergence that involves quantum physics is then proposed.

The availability of unitarily inequivalent representations of the canonical commutation relations constituting a quantization of a classical field theory raises questions about how to formulate and pursue quantum field theory. In a minimally technical way, I explain how these questions arise and how advocates of the Hilbert space and of the algebraic approaches to quantum theory might answer them. Where these answers differ, I sketch considerations for and against each approach, as well as considerations which might temper their apparent rivalry.

In Epistemic Cultures (1999), Karin Knorr Cetina argues that different scientific fields exhibit different epistemic cultures. She claims that in high energy physics (HEP) individual persons are displaced as epistemic subjects in favor of experiments themselves. In molecular biology (MB), by contrast, individual persons remain the primary epistemic subjects. Using Ed Hutchins’ (1995) account of navigation aboard a traditional US Navy ship as a prototype, I argue that both HEP and MB exhibit forms of distributed cognition. That is, in both fields cognition is distributed across individual persons and complex artifacts. The cognitive system producing the knowledge is heterogeneous. Nevertheless, in both fields we can reserve epistemic agency for the human components of these systems. We do not need to postulate new distributed cognitive agents, let alone ones exhibiting new forms of consciousness.

This essay reviews two recent books commenting on, and contributing to, the “science wars.” In Who Rules in Science? James Robert Brown respectfully but firmly rejects the “nihilist” and the “naturalist” wings of social constructivism. He rejects attempts to debunk science in the name of a relativist or anarchist epistemology. He also criticizes the “strong programme” in the sociology of knowledge and its implied contrast between reasons and causes. In Prometheus Bedeviled Norman Levitt examines the cultural roots of current discontent with science. Levitt's analysis—and polemic—charges contemporary culture with a pervasive cheapening of intellectual standards.

We show how Richard Jeffrey's The Logic of Decision provides the proper formalism for calculating expected fitness for correlated encounters in general. As an illustration, some puzzles about kin selection are resolved.

Akaike's framework for thinking about model selection in terms of the goal of predictive accuracy and his criterion for model selection have important philosophical implications. Scientists often test models whose truth values they already know, and they often decline to reject models that they know full well are false. Instrumentalism helps explain this pervasive feature of scientific practice, and Akaike's framework helps provide instrumentalism with the epistemology it needs. Akaike's criterion for model selection also throws light on the role of parsimony considerations in hypothesis evaluation. I explain the basic ideas behind Akaike's framework and criterion; several biological examples, including the use of maximum likelihood methods in phylogenetic inference, are considered.

What has science actually achieved? A theory of achievement should (1) define what has been achieved, (2) describe the means or methods used in science, and (3) explain how such methods lead to such achievements. Predictive accuracy is one truth-related achievement of science, and there is an explanation of why common scientific practices (of trading off simplicity and fit) tend to increase predictive accuracy. Akaike's explanation for the success of AIC is limited to interpolative predictive accuracy. But therein lies the strength of the general framework, for it also provides a clear formulation of many open problems of research.

The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, agreeing measurements of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation.