This essay explores some of the issues raised as regards the relations of philosophy and sociology of science in the work of Rudolf Carnap and Hans Reichenbach. It argues that Hans Reichenbach's distinction between the contexts of discovery and justification should not be seen as erecting a principled normative/descriptive distinction that demarcates philosophy of science from sociology of science. The essay also raises certain issues about the role of volition, decision, and the limits of epistemological concern in the work of Carnap and Reichenbach and in some recent sociology of science. The relations of philosophy of science to sociology of science are seen to be more deeply rooted and more interesting than the Science Warriors would have us believe.

This paper explores how data serve as evidence for phenomena. In contrast to standard philosophical models which invite us to think of evidential relationships as logical relationships, I argue that evidential relationships in the context of data-to-phenomena reasoning are empirical relationships that depend on holding the right sort of pattern of counterfactual dependence between the data and the conclusions investigators reach on the phenomena themselves.

There are two reasons, I claim, scientists do and should ignore standard philosophical theories of objective evidence: (1) Such theories propose concepts that are far too weak to give scientists what they want from evidence, viz., a good reason to believe a hypothesis; and (2) They provide concepts that make the evidential relationship a priori, whereas typically establishing an evidential claim requires empirical investigation.

In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a new and more adequate philosophy of evidence can move.

In this paper we examine the pattern of conceptual change during scientific revolutions by using methods from cognitive psychology. We show that the changes characteristic of scientific revolutions, especially taxonomic changes, can occur in a continuous manner. Using the frame model of concept representation to capture structural relations within concepts and the direct links between concept and taxonomy, we develop an account of conceptual change in science that more adequately reflects the current understanding that episodes like the Copernican revolution are not always abrupt. When concepts are represented by frames, the transformation from one taxonomy to another can be achieved in a piecemeal fashion not preconditioned by a crisis stage, and a new taxonomy can arise naturally out of the old frame instead of emerging separately from the existing conceptual system. This cognitive mechanism of continuous change demonstrates the constructive roles of anomaly and incommensurability in promoting the progress of science.

Thomas Kuhn's The Structure of Scientific Revolutions was published at the beginning of what has come to be known as “the cognitive revolution.” With hindsight one can construct significant parallels between the problems of knowledge, perception, and learning with which Kuhn and cognitive scientists were grappling and between the accounts developed by each. However, by and large Kuhn never utilized the research in cognitive science—especially in cognitive psychology—that we believe would have furthered his own paradigm. This is puzzling since he did not have the traditional philosophical aversion to “psychologizing” and in fact drew on insights from psychology to support the most radical claims in Structure, such as the “Gestalt switch” nature of conceptual change. Indeed, the research program outlined there seems intrinsically historical, philosophical, and psychological and Kuhn's work has had considerable influence on research in cognitive science.

Looking at Thomas Kuhn's work from a cognitive science perspective helps to articulate and to legitimize, to some degree, his rejection of traditional views of concepts, categorization, theory structure, and rule-based problem solving. Whereas my colleagues focus on the later Kuhn of the MIT years, I study the early Kuhn as an anticipation of case-based reasoning and schema theory. These recent developments in cognitive psychology and artificial intelligence may point toward a more computational version of Kuhn's ideas, but they also expose ambiguities in his work, notably in his understanding of exemplars.