Connectionist models of cognition are all the rage these days. They are said to provide better explanations than traditional symbolic computational models in a wide array of cognitive areas, from perception to memory to language to reasoning to motor action. But what does it actually mean to say that they "explain" cognition at all? In what sense do the dozens of nodes and hundreds of connections in a typical connectionist network explain anything? It is the purpose of this paper to explore this question in light of traditional accounts of what it is to be an explanation. We start with an impossibly brief review of some historically important theories of explanation. We then discuss several currently-popular approaches to the question of how connectionist models explain cognition. Third, we describe a theory of causation by philosopher Stephen Yablo that solves some of the problems on which we think many accounts of connectionist explanation founder. Finally, we apply Yablo's theory to these accounts, and show how several important issues surrounding them seem to disappear into thin air in its presence.
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