A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...) is therefore not about discovering the unknown, but rather "a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education". (shrink)

Drawing on the results of modem psychology and cognitive science we suggest that the traditional theory of concepts is no longer tenable, and that the alternative account proposed by Kuhn may now be seen to have independent empirical support quite apart from its success as part of an account of scientific change. We suggest that these mechanisms can also be understood as special cases of general cognitive structures revealed by cognitive science. Against this background, incommensurability is not an insurmountable obstacle (...) to accepting Kuhn's position, as many philosophers of science still believe. Rather it becomes a natural consequence of cognitive structures that appear in all human beings. (shrink)

This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...) e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation. (shrink)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

Thomas S. Kuhn's classic book is now available with a new index.

The alleged problem of "incommensurability" is examined, and attempts to explain scientific change in terms of concepts of meaning and reference are analyzed and rejected. A way of understanding scientific change through a properly developed concept of "reasons" is presented, and the issues of reasons, meaning, and reference are placed in the context of this broader interpretation of scientific change.

In this paper we examine the pattern of conceptual change during scientific revolutions by using methods from cognitive psychology. We show that the changes characteristic of scientific revolutions, especially taxonomic changes, can occur in a continuous manner. Using the frame model of concept representation to capture structural relations within concepts and the direct links between concept and taxonomy, we develop an account of conceptual change in science that more adequately reflects the current understanding that episodes like the Copernican revolution are (...) not always abrupt. When concepts are represented by frames, the transformation from one taxonomy to another can be achieved in a piecemeal fashion not preconditioned by a crisis stage, and a new taxonomy can arise naturally out of the old frame instead of emerging separately from the existing conceptual system. This cognitive mechanism of continuous change demonstrates the constructive roles of anomaly and incommensurability in promoting the progress of science. (shrink)

In a previous article we have shown that Kuhn's theory of concepts is independently supported by recent research in cognitive psychology. In this paper we propose a cognitive re-reading of Kuhn's cyclical model of scientific revolutions: all of the important features of the model may now be seen as consequences of a more fundamental account of the nature of concepts and their dynamics. We begin by examining incommensurability, the central theme of Kuhn's theory of scientific revolutions, according to two different (...) cognitive models of concept representation. We provide new support for Kuhn 's mature views that incommensurability can be caused by changes in only a few concepts, that even incommensurable conceptual systems can be rationally compared, and that scientific change of the most radical sort—the type labeled revolutionary in earlier studies—does not have to occur holistically and abruptly, but can be achieved by a historically more plausible accumulation of smaller changes. We go on to suggest that the parallel accounts of concepts found in Kuhn and in cognitive science lead to a new understanding of the nature of normal science, of the transition from normal science to crisis, and of scientific revolutions. The same account enables us to understand how scientific communities split to create groups supporting new paradigms, and to resolve various outstanding problems. In particular, we can identify the kind of change needed to create a revolution rather precisely. This new analysis also suggests reasons for the unidirectionality of scientific change. (shrink)

In this paper we examine the pattern of conceptual change during scientific revolutions by using methods from cognitive psychology. We show that the changes characteristic of scientific revolutions, especially taxonomic changes, can occur in a continuous manner. Using the frame model of concept representation to capture structural relations within concepts and the direct links between concept and taxonomy, we develop an account of conceptual change in science that more adequately reflects the current understanding that episodes like the Copernican revolution are (...) not always abrupt. When concepts are represented by frames, the transformation from one taxonomy to another can be achieved in a piecemeal fashion not preconditioned by a crisis stage, and a new taxonomy can arise naturally out of the old frame instead of emerging separately from the existing conceptual system. This cognitive mechanism of continuous change demonstrates the constructive roles of anomaly and incommensurability in promoting the progress of science. (shrink)