Rob Stufflebeam

Most cognitive scientists believe that cognitive processing (e.g., thought, speech, perception, and sensori‐motor processing) is the hallmark of intelligent systems. Aside from modeling such processes, cognitive science is in the business of mechanistically explaining how minds and other intelligent systems work. As one might expect, mechanistic explanations appeal to the causal‐functional interactions among a system's component structures. Good explanations are the ones that get the causal story right. But getting the causal story right requires positing structures that are really in the system. After all, within the context of a mechanistic explanation, to posit X is to claim not only that X exists but that X is doing some causal labor. Because most cognitive scientists believe that cognitive processing requires the use, manipulation, and storage of internal representations, they characteristically posit internal representations to explain how intelligent systems work.

Aron Gurwitsch (1900-73) was one of the most important figures in the phenomenological movement between the 1920s and the 1970s. Through his introduction of Gestalt theoretical concepts into phenomenology, he exerted a powerful influence on Maurice Merleau-Ponty and others. The contributions to this memorial volume, most written by friends and students of Gurwitsch, contain critical studies of the work of Aron Gurwitsch and attempts to extend his philosophical analyses to new problems and fields. Ranging from formal ontology through the philosophy of the social sciences to the interpretation of Kant, the essays assembled here are both a tribute to and a continuation of the philosophical legacy of Aron Gurwitsch. The contributions will be of interest to advanced undergraduates and graduate students, and to specialists in a wide range of areas.

Turing's analysis of the concept of computation is indisputably the foundation of computationalism, which is, in turn, the foundation of cognitive science. What is disputed is whether computationalism is explanatorily bankrupt. For Turing, all computers are digital computers and something becomes a computer just in case its 'behavior' is interpreted as implementing, executing, or satisfying some function 'f'. As 'computer' names a nonnatural kind, almost everyone agrees that a computational interpretation of this sort is necessary for something to be a computer. But because everything in the universe satisfies at least one function, it is the sufficiency of such interpretations that is the problem. If, as anticomputationalists are fond of pointing out, computationalists are wedded to the view that a computational interpretation is sufficient for something to be a computer, then everything becomes a digital computer. This not only renders computer-talk vacuous, it strips computationalism of any empirical or explanatory import. My aim is to defend computationalism against charges that it is explanatorily bankrupt. I reexamine several fundamental questions about computers. One effect of this computation-related soul-searching will be a framework within which 'Is the brain a computer?' will be meaningful. Another effect will be a fracture in the supposed link between computationalism and symbolic-digital processing.

C. I. Lewis' action-oriented notion of cognition is consistent with a minimally representational picture of mind. I aim to show why. Toward this end, I explore some of the tensions between Lewis' theory of knowledge and his theory of mind. At face value, the former renders the latter implausible. Among other problems, no agent could act if she were required to entertain the myriad beliefs that Lewis claims figures in the guidance of action. But rather than abandon Lewis' story, I attempt to rehabilitate it. Rehabilitation is possible, I argue, because Lewis isn't claiming that his epistemology describes actual justificatory practices, but rather what an agent could do; the social character of concepts [and meaning] considerably reduces the need for appealing to internal concepts when explaining why an agent does what she does; and among his paradigm cases of cognitive behavior are paradigm cases of nonreflective action. Here's the rub: not only do such actions account for most of our behavior [as Lewis himself notes], nonreflective actions, though cognitive, don't require conceptualization

Because computationalism and representationalism lay among the foundations of cognitive science, most cognitive scientists are convinced that without internal representations over which to operate, no intelligent system could do what it does. This view has recently come under attack by anticomputationalists of various stripes. Consequently, with the aim of understanding how brains work, my purpose for this dissertation is to determine whether it is appropriate, on computational grounds, to posit internal representations when explaining how intelligent systems work. ;As it is the ontological status of internal representations that is at issue here, I begin by defending the view that something is a representation just in case it is an "entity" in a stands-for relation to something else. In short, every representation is a content-bearer. Adopting the vocabulary found in the literature on intentionality, I draw a distinction between extrinsic and intrinsic representations. Whereas the former bear content just in virtue of our interpretations, the latter do so because they were produced by a mechanistic process of the "right sort," namely, one whose designed or evolved function is to produce content-bearers. The task then becomes determining whether computational systems operate over intrinsic representations. ;After exploring both the nature of computation and the interest-relative way by which something receives a computational interpretation, I argue that there are at least two types of actual computational processing--symbolic-digital processing and nonsymbolic-analog processing. As each of these types of processing corresponds to a different class of computer, the task then becomes exploring the role and status of internal representations in both digital computers and analog computers. Whereas symbolic-digital processing is mediated by internal representations, nonsymbolic-analog processing is not. Or so I argue. ;By focusing on vision, the final task is to show that brains implement nonsymbolic-analog processing. Yet because brains do produce representations , such processing is not always representation-free. Thus, given the analog nature of biological computational processing, mechanistic explanations of how brains work requires only a minimal commitment to internal representations