Prominent evolutionary psychologists have argued that our innate psychological endowment consists of numerous domainspecific cognitive resources, rather than a few domaingeneral ones. In the light of some conceptual clarification, we examine the central inprinciple arguments that evolutionary psychologists mount against domaingeneral cognition. We conclude (a) that the fundamental logic of Darwinism, as advanced within evolutionary psychology, does not entail that the innate mind consists exclusively, or even massively, of domainspecific features, and (b) that a mixed innate cognitive economy of domainspecific (...) and domaingeneral resources remains a genuine conceptual possibility. However, an examination of evolutionary psychology's 'grain problem' reveals that there is no way of establishing a principled and robust distinction between domainspecific and domaingeneral features. Nevertheless, we show that evolutionary psychologists can and do live with this grain problem without their whole enterprise being undermined. (shrink)

Cognitive science's basic premises are under attack. In particular, its focus on internal cognitive processes is a target. Intelligence is increasingly interpreted, not as a matter of reclusive thought, but as successful agent-environment interaction. The critics claim that a major reorientation of the field is necessary. However, this will only occur when there is a distinct alternative conceptual framework to replace the old one. Whether or not a serious alternative is provided is not clear. Among the critics there is some (...) consensus, however, that this role could be fulfilled by the concept of a 'behavioral system'. This integrates agent and environment into one encompassing general system. We will discuss two contexts in which the behavioral systems idea is being developed. Autonomous Agents Research is the enterprise of building behavior-based robots. Dynamical Systems Theory provides a mathematical framework well suited for describing the interactions between complex systems. We will conclude that both enterprises provide important contributions to the behavioral systems idea. But neither turns it into a full conceptual alternative which will initiate a major paradigm switch in cognitive science. The concept will need a lot of fleshing out before it can assume that role. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

A discussion is going on in cognitive science about the use of representations to explain how intelligent behavior is generated. In the traditional view, an organism is thought to incorporate representations. These provide an internal model that is used by the organism to instruct the motor apparatus so that the adaptive and anticipatory characteristics of behavior come about. So-called interactionists claim that this representational specification of behavior raises more problems than it solves. In their view, the notion of internal representational (...) models is to be dispensed with. Instead, behavior is to be explained as the intricate interaction between an embodied organism and the specific make up of an environment. The problem with a non-representational interactive account is that it has severe difficulties with anticipatory, future oriented behavior. The present paper extends the interactionist conceptual framework by drawing on ideas derived from the study of morphogenesis. This extended interactionist framework is based on an analysis of anticipatory behavior as a process which involves multiple spatio-temporal scales of neural, bodily and environmental dynamics. This extended conceptual framework provides the outlines for an explanation of anticipatory behavior without involving a representational specification of future goal states. (shrink)

Evolutionary psychology presupposes relations between theories of different domains that the two traditional models, reduction and autonomy, cannot properly account for. We aim to construct a model of relations between theories that succeeds where traditional models fail. We show that the multiple realizability argument, on which the autonomist model is thought to rest, is compatible with reductionism and, following Kim, that an autonomist reading of the argument deprives psychology of its scientific status. We therefore opt for a reductionist model compatible (...) with functionalism and multiple realizability, but show that, within evolutionary psychology at least, the very application of the conditions of reduction requires strong interactions between psychology and various other adjacent disciplines. We also show that reduction must be preceded in evolutionary psychology by an of the reduction base, which brings still other disciplines into play. Finally, we present a model of the interaction between disciplines we believe accounts best for these relations and discuss the problems facing this kind of enterprise. (shrink)

Many of the mathematicians and scientists who guided the development of digital computers in the late 1940s, such as Alan Turing and John von Neumann, saw these new devices not just as tools for calculation but as devices that might employ the same principles as are exhibited in rational human thought. Thus, a subfield of what came to be called computer science assumed the label artificial intelligence. The idea of building artificial systems which could exhibit intelligent behavior comparable to that (...) of humans was a heady prospect, and the claims made on behalf of AI during the 1950s and 1960s were impossibly ambitious. Despite some theoretical and applied successes within the field, serious problems soon became evident. Instead of fulfilling the goal of quickly producing artificial intelligent agents which could compete with or outperform human beings, by the 1970s and 1980s AI had settled into a pattern of slower but real progress in modeling or simulating aspects of human intelligence. (shrink)