According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, and only sustained empirical research in cognitive science will determine the extent to which that is true.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Journal of Philosophy, Inc. is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Philosophy. W hat is cognition? Contemporary orthodoxy maintains that it is computation: the mind is a special kind of computer, and cognitive processes are the rule-governed manipulation of internal symbolic representations. This broad idea has dominated the philosophy and the rhetoric of cognitive science-and even, to a large extent, its practice-ever since the field emerged from the postwar cybernetic melee. It has provided the general framework for much of the most well-developed and insightful research into the nature of mental operation. Yet, over the last decade or more, the computational vision has lost much of its lustre. Although work within it continues apace, a variety of difficulties and limitations have become increasingly apparent, and researchers across cognitive science and related disciplines have been casting around for other ways to understand cognitive processes. Partly as a result, there are now many research programs which, one way or another, stand opposed to the traditional computational approach; these include connectionism, neurocomputational approaches, ecological psychology, situated robotics, synergetics, and artificial life.These approaches appear to offer a variety of differing and even conflicting conceptions of the nature of cognition. It is therefore an appropriate time to step back and reconsider the question: What general arguments are there in favor of the idea that cognitive processes must be specifically comnputational in nature? In order prop-* Criticism and advice from numerous people helped improve this paper, but special acknowledgement is due to Robert Port, , and the University of Skuivde were suitably and helpfully critical of earlier versions. 0022-362X/95/9207/345-81 ? 1995 TheJournal of Philosophy, Inc. 345 This content downloaded from 128.235.251.160 on Mon, 1 Dec 2014 20:03:43 PM All use subject to JSTOR Terms and Conditions THEJOURNAL OF PHILOSOPHY erly to address this question, however, we must first address another: What are the alternatives? What could cognition be, if it were not computation of some form or other?There are at least two reasons why this second question is important. First, arguments in favor of some broad hypothesis are rarely, if ever, completely general. They tend to be arguments not for A alone, but rather in favor of A as opposed to B, and such arguments often fail to support A as opposed to C. For example, one of the most powerful early conrsiderations raised in favor of the computational conception of cognition was the idea that intelligent behavior requires sophisticated internal represent...

No abstract

No abstract

People generally develop some degree of competence in general informal reasoning and argument skills, but how do they go beyond this to attain higher expertise? Ericsson has proposed that high-level expertise in a variety of domains is cultivated through a specific type of practice, referred to as "deliberate practice." Applying this framework yields the empirical hypothesis that high-level expertise in informal reasoning is the outcome of extensive, deliberate practice. This paper reports results from two studies evaluating the hypothesis. University student participants completed 12 weeks of deliberate practice in informal reasoning. Quantity of practice was recorded by computer, and additionally assessed via self-report. The hypothesis was supported: Students in both studies showed a large improvement, and practice, as measured by computer, was related to amount of improvement in informal reasoning. These findings support adopting a deliberate practice approach when attempting to teach or learn expertise in informal reasoning.