Samuli Reijula (né Pöyhönen)

In this article we focus on the concept of concept in conceptual change. We argue that (1) theories of higher learning must often employ two different notions of concept that should not be conflated: psychological and scientific concepts. The usages for these two notions are partly distinct and thus straightforward identification between them is unwarranted. Hence, the strong analogy between scientific theory change and individual learning should be approached with caution. In addition, we argue that (2) research in psychology and cognitive science provides a promising theoretical basis for developing explanatory mechanistic models of conceptual change. Moreover, we argue that (3) arguments against deeper integration between the fields of psychology and conceptual change are not convincing, and that recent theoretical developments in the cognitive sciences might prove indispensable in filling in the details in mechanisms of conceptual change.

Theories of natural kinds can be seen to face a twofold task: First, they should provide an ontological account of what kinds of (fundamental) things there are, what exists. The second task is an epistemological one, accounting for the inductive reliability of acceptable scientific concepts. In this chapter I examine whether concepts and categories used in the cognitive sciences should be understood as natural kinds. By using examples from human memory research to illustrate my argument, I critically examine some of the main contenders for a theory of natural kinds. I show that when applied to complex target domains – such as human cognition – both essentialist theories and more liberal accounts of natural kindhood (such as HPC theory) fail to simultaneously satisfy the ontological and epistemological desiderata for a theory of natural kinds. I argue, however, that natural kindhood of a category in a metaphysical sense is not necessary for its inductive reliability, and that HPC theory provides an analytically useful account of the grounds of category-based inductive inference.