Richard Samuels

What are the elements from which the human mind is composed? What structures make up our _cognitive architecture?_ One of the most recent and intriguing answers to this question comes from the newly emerging interdisciplinary field of evolutionary psychology. Evolutionary psychologists defend a _massively modular_ conception of mental architecture which views the mind –including those parts responsible for such ‘central processes’ as belief revision and reasoning— as composed largely or perhaps even entirely of innate, special-purpose computational mechanisms or ‘modules’ that have been shaped by natural selection to handle the sorts of recurrent information processing problems that confronted our hunter-gatherer forebears (Cosmides and Tooby,192; Sperber, 1994; Samuels, 1998a)

In this paper I defend the classical computational account of reasoning against a range of highly influential objections, sometimes called relevance problems. Such problems are closely associated with the frame problem in artificial intelligence and, to a first approximation, concern the issue of how humans are able to determine which of a range of representations are relevant to the performance of a given cognitive task. Though many critics maintain that the nature and existence of such problems provide grounds for rejecting classical computationalism, I show that this is not so. Some of these putative problems are a cause for concern only on highly implausible assumptions about the extent of our cognitive capacities, whilst others are a cause for concern only on similarly implausible views about the commitments of classical computationalism. Finally, some versions of the relevance problem are not really objections but hard research issues that any satisfactory account of cognition needs to address. I conclude by considering the diagnostic issue of why accounts of cognition in general—and classical computational accounts, in particular—have faired so poorly in addressing such research issues.Keywords: Computationalism; Frame problem; Relevance