Andrew Richmond

I use Swampman to illuminate the role of thought experiments in philosophy of science. Against Millikan and others, I argue that even outlandish thought experiments can shed light on science and scientific kinds, so long as we understand them as illustrations of scientific reasoning, not as examples of scientific kinds. The logic of thought experiments, understood as illustrations, is analogous to the logic of common experimental paradigms in science. So, in reviving Swampman and showing how he survives teleosemantic objections, I also provide a framework for understanding how, why, and when thought experiments are informative about science and scientific kinds.

This paper asks how representational notions figure into cognitive science, especially neuroscience. Philosophers have a way of skipping over that question and going straight to another: _what is neural representation?_ What is the property or relation that representational notions pick out? I argue that this is a mistake. Our ultimate questions, as philosophers of cognitive science, are about the function and epistemology of cognitive scientific explanations—in this case, explanations that use representational notions. To answer those questions we must understand what representational notions contribute to science: what they enable scientists to do or explain, and how. But I show that we can do this without raising traditional and vexing questions about the definition of neural representation, or the nature of a property or relation that notion picks out. Taking this approach, I defend a realist account of representational explanation that underwrites important connections between philosophy and neuroscience.

Cognitive science gives computational explanations of the brain. Philosophers have treated these explanations as if they simply claim that the brain computes. We have therefore assumed that to understand how and why computational explanation works, we must understand what it is to compute. In contrast, I argue that we can understand computational explanation by describing the resources it brings to bear on the study of the brain. Specifically, I argue that it introduces concepts and formalisms that complement cognitive science's modeling goals. This allows us to understand computational explanation without having to debate what it is to compute.