Showing papers in "Cognitive Science in 1990"

TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.

TL;DR: This paper suggests that there are constraints on learning required to explain the acquisition of language, in particular, mului ultonol constraints, and suggests that language learning abilities decline because of the expansion of nonlinguisftc cognitive abilities.

TL;DR: Findings suggest that a general representation of object unity and boundaries is interposed between representations of surfaces and representations of objects of familiar kinds, related to processes of physical reasoning.

TL;DR: Three possible constraints on word meanings are considered: the whole object assumption which leads children to interpret novel terms as labels for objects—not parts, substances, or other properties of objects; the taxonomic assumption which leading children to consider labels as referring to objects of like kind, rather than to objects that are thematically related.

TL;DR: Early cognitive development benefits from nonilnguistic representations of skeietai sets of domain-specific principles and complementary domain-relevant doto obstroction processes that outline the domain, identify relevant inputs, and structure coherently what is learned.

TL;DR: It is argued that the particular and efficient knowledge organization of DC challenges current theories of skill acquisition as it presents an end-state of learning that is difficult to explain within such theories.

TL;DR: In this paper, the authors show that Connectionist representations can be compositional without being classical, and further, that Fodor and Pylyshyn's supposedly conclusive arguments in favor of the classical approach do not in fact support that approach over the Connectionist alternative.

TL;DR: The authors argue that children learn and transfer readily, even in traditional laboratory settings, if they are required to extend their knowledge about causal mechanisms that they already understand, and that a search for causal explanations is the basis of broad understanding, of wide patterns of generalization, and of flexible transfer and creative inferential projections.

TL;DR: In this paper, two probability languages, the Bayesian language and the language of belief functions, are compared, and the semantics (i.e., the meaning of the scale) and syntax of these languages are compared.

TL;DR: A view of planning as a task supported by a dynamic memory is presented, which attempts to ingegrate models of memory, learning, and planning into a single system that learns about planning by creating new plans and analyzing how they interact with the world.

TL;DR: Causal reasoning appeared to be most pervasive for combinations viewed as more surprising, suggesting that surprise may have triggered the generation of causal accounts.

TL;DR: The experiment demonstrates the feasibility of examining some of the processes of scientific discovery by recreating, in the laboratory, discovery situations of substantial historical relevance, and demonstrates that under conditions rather similar to those of the original discoverer, a law can be rediscovered by persons of ordinary intelligence.

TL;DR: An empirical study of elementary algebra errors, conducted in three separate schools, uncovers that it is impossible to make a clear distinction between slips and mistakes and that most errors depend on properties of the knowledge base and the cognitive architecture.

TL;DR: It is argued that adopting causal notions can be viewed as filling a computational need: They allow reasoning with incomplete information, facilitate economical representations, and afford relatively efficient methods for reasoning about those representations.

TL;DR: A computer model called EUREKA is presented that begins with novice-like strategies and knowledge organizations for solving physics word problems and acquires features of knowledge organizations and basic approaches that characterize experts in this domain, and also suggests a number of empirically testable assumptions regarding problem-solving strategies and the representation of problem-Solving knowledge.

TL;DR: The authors presented a difficult medical decision to three expert physicians in a combined "thinking aloud" and "cross examination" experiment using script analysis to observe the process of constructing and making the decision, and using referring phrase analysis to determine the representation of knowledge of likelihoods.

TL;DR: Brown, Gelman, Markman, Newport, and Spelke as mentioned in this paper proposed to use four types of constraints on learning as an interpretative framework within which to better understand the nature of current research and allow the exploration of alternative models of learning related phenomena, and see more clearly needs for further research.

TL;DR: L'auteur presente les articles du numero special sur les contraintes structurales du developpement cognitif de laissez-vous d'être enregistrés dans les années prises d'universitaire.

TL;DR: Results indicate that cognitive structures that connect the equation ΣF=ma and the concept of choosing appropriate systems are at least part of what constitutes understanding of the principle of Newton's second law.

TL;DR: A processing model that integrates same important psychological claims about the human sentence-parsing mechanism: namely, that processing is influenced by limitations an working memory and by various syntactic preferences is presented.

TL;DR: How explanation-based learning of plan schemata from observation can improve performance of plan recognition is discussed, which allows GENESIS to use schema- based understanding techniques when interpreting events and thereby avoid the expensive search associated with plan-based understanding.

TL;DR: In this article, the authors use an analysis of constraints to trace the course of development and propose common origins of cognitive abilities that were previously thought to be unrelated, such as safe takeoffs and soft landings.