In this dissertation, I develop a probabilistic theory of mental content that accounts for fundamental properties of mental representation. The theory that I develop falls under the category of causal/informational approaches. In contemporary philosophy, causal/informational approaches for explaining mental representation have been around since the 1950s. The main success of these approaches is to explain the link between the external world and mental entities. On the other hand, it turns out that accounting for misrepresentation cases presents an insurmountable problem for these approaches. The probabilistic theory that I develop keeps the positive features of causal/informational approaches and provides grounds for solving the problem of misrepresentation. The theory that I offer heavily draws from Dretske's 1981 framework. His framework borrows some notions such as entropy from Shannon & Weaver's Mathematical Theory of Communication which is a very successful formalism for engineering purposes. Dretske tries to explain mental representation, belief and knowledge by using the notion of informational content. Despite all of its appeals, however, the problem of misrepresentation also afflicts his framework. In this dissertation, I identify the reasons that cause problems in Dretske's framework. Identifying these reasons provides enough grounds for solving the problem of misrepresentation in the theory that I construct. I claim that the theory that I offer not only solves the problem of misrepresentation but also provides a research program for Cognitive Science and Neuroscience.
展开▼
