Showing papers in "Cognitive Science in 1988"

TL;DR: It is suggested that a major reason for the ineffectiveness of problem solving as a learning device, is that the cognitive processes required by the two activities overlap insufficiently, and that conventional problem solving in the form of means-ends analysis requires a relatively large amount of cognitive processing capacity which is consequently unavailable for schema acquisition.

TL;DR: A general model of Scientific Discovery as Dual Search (SDDS) is presented that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses.

TL;DR: This article approaches this issue by asking how people comprehend modified noun phrases of this sort, and suggests that the combination process does require reference to world knowledge.

TL;DR: In this article, the authors describe a program, KEKADA, which models the heuristics Hans Krebs used in the urea cycle discovery, in the same manner as the evidence in the form of laboratory notebooks and interviews indicates Hans Krebes did.

TL;DR: The authors proposed a model that accounts for how people construct prototypes for composite concepts out of prototypes for simple concepts, such as fruit, which is consistent with previous findings about prototypes in general, and with specific findings about typicality judgments for adjective-noun conjunctions.

TL;DR: The simulation of DCPS is intended as a detailed demonstration of the feasibility of certain ideas and should not be viewed as a full implementation of production systems.

TL;DR: Evidence from videotapes of experts thinking aloud is presented which documents the spontaneous use of analogies in scientific problem solving, and evidence was found for three different methods of analogy generation.

TL;DR: The paper presents a connectionist realization of semantic networks, that is, it describes how knowledge about concepts, their properties, and the hierarchical relationship between them may be encoded as an interpreter-free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast—in time proportional to the depth of the conceptual hierarchy.

TL;DR: This paper describes a method for causal attribution that can produce the analyses of examples that the generalization methods require, in the domain of simple procedures in human-computer interaction, and argues that none of the current analysis-based generalized methods fully captures Wertheimer s notion of understanding.

TL;DR: This work evaluated whether and when focused sampling benefits observational learning, investigated the effects of different distributions of systematic and unsystematic features, and compared observational leorning to learning with feedback.

TL;DR: The Sparse, Distributed Memory model (Kanerva, 1984) is compared to Hopfield-type, neural-network models, and it is shown that this proportionality constant is the same for the SDM, the Hopfield model, and higher-order models.

TL;DR: The principled decomposition of knowledge according to type and level of specificity yields both power and cross-doman generality, as demonstrated in FERMI's ability to apply the same principles of invariance and decomposition to solve problems in fluid statics, DC-circuits, and centroid location.

TL;DR: In this paper, the authors provide a guide to how much acquired knowledge needs to be accounted for in the mechanisms they propose for human memory and how much should be assumed as a basis for the memorydependent skills and processes they seek to explain.

TL;DR: Thomas K. Landauer’s (1986) estimate of the capacity of normal human memory is deeply flawed and his goal seems to be to estimate the number of bits it would take to duplicate the store of experiences of a human being in a way that subserves the same functions as human memory.