Mark Sprevak

Kanaan and McGuire elegantly describe three challenges facing the use of fMRI to uncover cognitive mechanisms. They shows how these challenges ramify in the case of identifying the mechanisms responsible for psychiatric disorders. In this commentary, I would like to raise another difficulty for fMRI that also appears to ramify in similar cases. This is that there are good reasons for doubting one of the assumptions on which many fMRI studies are based: that neural mechanisms are always and everywhere sufficient for cognition. I suggest that in the case of the mechanisms underlying psychiatric disorders, this assumption should be doubted. I do not dispute that a malfunctioning neural mechanism is likely to be a necessary component of a psychiatric disorder—as Kanaan and McGuire say, the experimental evidence from cognitive neuropsychiatry gives us excellent reasons to think that this is so. My question is whether a story only in terms of these neural mechanisms is sufficient to explain the mechanism of a psychiatric disorder. Is the reduction, projected by cognitive neuropsychiatry, of psychiatric disorders to disorders in neural functioning even in principle possible? Drawing on recent concerns about the location of mental states, I argue that such a reduction is likely to fail. Even if the considerable problems raised by Kanaan and McGuire for fMRI could be addressed, we have no reason to think that the mechanisms involved in psychiatric disorders are entirely neural, and that fMRI, or even a perfect science-fiction brain-scanner, would be capable of uncovering them. Psychiatric disorders, like numerous other cognitive processes, are liable to cross the brain–world boundary in such a promiscuous way as to be resistant to neural reduction.

Kripke (1982, Wittgenstein on rules and private language. Cambridge, MA: MIT Press) presents a rule-following paradox in terms of what we meant by our past use of “plus”, but the same paradox can be applied to any other term in natural language. Many responses to the paradox concentrate on fixing determinate meaning for “plus”, or for a small class of other natural language terms. This raises a problem: how can these particular responses be generalised to the whole of natural language? In this paper, I propose a solution. I argue that if natural language is computable in a sense defined below, and the Church–Turing thesis is accepted, then this auxiliary problem can be solved

In contrast to many areas of contemporary philosophy, something like a carnival atmosphere surrounds Searle’s Chinese room argument. Not many recent philosophical arguments have exerted such a pull on the popular imagination, or have produced such strong reactions. People from a wide range of fields have expressed their views on the argument. The argument has appeared in Scientific American, television shows, newspapers, and popular science books. Preston and Bishop’s recent volume of essays reflects this interdisciplinary atmosphere. The volume includes essays from computer science, neuroscience, artificial intelligence, cognitive science, sociology, science studies, physics, mathematics, and philosophy. There are two sides to this interdisciplinary mix. On the one hand, it makes for interesting and fun reading for anyone interested in the Chinese room argument, but on the other, it raises the threat that the Chinese room argument might be left in some kind of interdisciplinary no man’s land. The Chinese room argument (CRA) is an argument against the possibility of Strong artificial intelligence (Strong AI). The thesis of Strong AI is that running a program is sufficient for, or constitutive of, understanding: it is merely in virtue of running a 1 particular program that a system understands. Searle appreciates that understanding is a complex notion, and so he has a particular form of understanding in mind: the understanding of simple stories. It seems intuitively obvious that when I read a simple story in English, I understand that story. One could say that somewhere in my head there is understanding going on. However, if I read a simple story written in Chinese (a language I do not speak), then there is no understanding going on. What makes the difference between these two cases? The advocate of Strong AI says that the difference..