This paper presents the leading idea of my doctoral dissertation and thus has been shaped by the reactions of all the members of my thesis committee: Charles Chastain, Walter Edelberg, W. Kent Wilson, Dorothy Grover, and Charles Marks. I am especially grateful for the help of Professors Chastain, Edelberg, and Wilson; each worked closely with me at one stage or another in the development of the ideas contained in the present work. Shorter versions of this paper were presented at the (...) 47th Annual Northwest Conference on Philosophy (1995), the 1996 Mid-South Philosophy Conference, the 1997 meeting of the Central Division of the American Philosophical Association, and at the University of Washington, Seattle; thanks to all audiences for their insightful comments and questions and also to my conference commentators, Eric Gampel, Jonathan Cohen, and Bruce Glymour, respectively, each of whom offered a thoughtful critique. Lastly, I extend my gratitude to anonymous referees, including two from Mind and Language, whose remarks led to significant improvements in the paper. Address for correspondence: 13416, 4th Ave. S., Seattle, WA 98168, USA E-mail: [email protected].. (shrink)

Naturalistically minded philosophers hope to identify a privileged nonsemantic relation that holds between a mental representation m and that which m represents, a relation whose privileged status underwrites the assignment of reference to m. The naturalist can accomplish this task only if she has in hand a nonsemantic criterion for individuating mental representations: it would be question-begging for the naturalist to characterize m, for the purpose of assigning content, as 'the representation with such and such content'. If we individuate mental (...) representations using the tools of dynamical systems theory, we find that a given mental representation, characterized nonsemantically, emerges in the cognitive system as the result of causal interactions between the subject and her environment. At least for the most basic of our mental representations, I argue that the dynamical systems-based approach to individuation increases the plausibility of a theory that assigns reference as a function of the subject's causal history. (shrink)

A key component of much current research in behavioral ecology, cognitive science, and economics is a model of the mind at least partly based on beliefs and desires. However, despite this prevalence, there are still many open questions concerning both the structure and the applicability of this model. This is especially so when it comes to its ‘desire’ part: in particular, it is not yet entirely clear when and why we should expect organisms to be desire-based—understood so as to imply (...) that they consult explicit tokenings of what they ought to do—as opposed to being drive-based—understood so as to imply that they react to the world using behavioral reflexes. In this paper, I present the beginnings of an answer to this question. To do this, I start by showing that an influential recent attempt to address these issues—due to Kim Sterelny—fails to be fully successful, as it does not make sufficiently clear what the relative benefits and disadvantages of drive-based and desire-based cognitive architectures are. I then present an alternative account of this matter based on the idea that organisms that can follow explicit behavioral rules avoid having to memorize a large set of state of the world–action connections—which can be adaptive. Finally, I apply this account to the question of what the cognitive value of mental representations should be seen to be; here, I conclude that—contrary to some recent claims—relying on mental representations can make decision making easier, not harder, but also that—in line with these recent claims—whether it does so depends on the details of the case. (shrink)

It is widely mooted that a plausible computational cognitive model should involve both symbolic and connectionist components. However, sound principles for combining these components within a hybrid system are currently lacking; the design of such systems is oftenad hoc. In an attempt to ameliorate this we provide a framework of types of hybrid systems and constraints therein, within which to explore the issues. In particular, we suggest the use of system independent constraints, whose source lies in general considerations about cognitive (...) systems, rather than in particular technological or task-based considerations. We illustrate this through a detailed examination of an interruptibility constraint: handling interruptions is a fundamental facet of cognition in a dynamic world. Aspects of interruptions are delineated, as are their precise expression in symbolic and connectionist systems. We illustrate the interaction of the various constraints from interruptibility in the different types of hybrid systems. The picture that emerges of the relationship between the connectionist and the symbolic within a hybrid system provides for sufficient flexibility and complexity to suggest interesting general implications for cognition, thus vindicating the utility of the framework. (shrink)

The paper considers the problems involved in getting neural networks to learn about highly structured task domains. A central problem concerns the tendency of networks to learn only a set of shallow (non-generalizable) representations for the task, i.e., to miss the deep organizing features of the domain. Various solutions are examined, including task specific network configuration and incremental learning. The latter strategy is the more attractive, since it holds out the promise of a task-independent solution to the problem. Once we (...) see exactly how the solution works, however, it becomes clear that it is limited to a special class of cases in which (1) statistically driven undersampling is (luckily) equivalent to task decomposition, and (2) the dangers of unlearning are somehow being minimized. The technique is suggestive nonetheless, for a variety of developmental factors may yield the functional equivalent of both statistical AND informed undersampling in early learning. (shrink)