Tim Fuller

Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis of artificial intelligence research as confounded by an inability to circumscribe the amount of information relevant to inferential processes. This diagnosis is particularly inapposite with respect to Bayes nets, since one of their strengths as machine learning systems has been their capacity to reason probabilistically about large data sets whose size overwhelms the capacities of individual human reasoners. A general moral follows from these criticisms: Insights into artificial and human cognitive systems are likely to be cultivated by focusing greater attention on the structure and density of connections among items of information that are available to them.

Do accounts of scientific theory formation and revision have implications for theories of everyday cognition? We maintain that failing to distinguish between importantly different types of theories of scientific inference has led to fundamental misunderstandings of the relationship between science and everyday cognition. In this article, we focus on one influential manifestation of this phenomenon which is found in Fodor's well-known critique of theories of cognitive architecture. We argue that in developing his critique, Fodor confounds a variety of distinct claims about the holistic nature of scientific inference. Having done so, we outline more promising relations that hold between theories of scientific inference and ordinary cognition

In this paper I argue that a principal argument in favor of the existence of non-conceptual content (henceforth NCC) fails. That is, I do not accept that considerations regarding the richness of our perceptual experiences support the existence of NCC. I argue instead that the existence of NCC is empirically motivated. Here is an outline of the paper. First, I set out the distinction between conceptual content and NCC as we understand it. Second, I consider the richness argument (RA), and argue that it fails. I argue in particular that RA (or RA-style arguments) are either self-defeating or confl ict with reasonably established accounts of early perceptual processing. Third, I tackle a residual phenomenological puzzle and offer a solution to it. Fourth, I argue that the existence of NCC enjoys empirical support. I argue in particular that states associated with early stages of visual perceptual processing have NCC.