Showing papers in "Cognitive Science in 1994"

TL;DR: This article showed that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text, and that prompted students who generated o large number of self-explaining (the high explainers) learned with greater understanding than low explainers.

TL;DR: The theoretical framework and methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem and the nature of external representations is discussed.

TL;DR: It is shown that in Tetris-a real-time, interactive video game-certain cognitive and perceptual problems are more quicktv, easily, and reliably solved by performingactions in the world than by performing computational actions in the head atone.

TL;DR: A model of similarity-based retrieval that attempts to capture three seemingly contradictory psychological phenomena, showing that structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory and that MAC/FAC can model patterns of access found in psychological data.

TL;DR: In this paper, the authors investigated elementary school children's explanations of the day/night cycle and found that the majority of the children in their sample used in a consistent fashion a small number of relatively well-defined mental models of the earth, the sun, and the moon to explain the day and night cycle.

TL;DR: This article shows how these theories of analogy can be unified within a common metatheoretical framework that distinguishes among levels of informational, behavioral, and hardware constraints.

TL;DR: This article explored the role of people's prior knowledge or intuitive theories on category learning by manipulating the labels associated with the category and found that when categories are meaningfully labeled, people bring intuitive theories to the learning context.

TL;DR: Computer simulations showed that differences in receptive field sizes can promote organization in artificial neural net-works, and using a novel modular architecture, networks were not preassigned a task, but rather competed to perform the different tasks.

TL;DR: It is suggested that knowledge acquisition can be managed as a transition from general expertise to specific expertise and an implementation for game playing is described that raises interesting issues about the organization and modification of conflicting expertise, and the role that experience plays in such learning.

TL;DR: In four experiments, testing both short-term and long-term memory for shape, this work replicated the previous failures of orientation invariance using wire forms, but found relatively good or perfect orientation invariant with equivalently shaped surfaces.

TL;DR: In this paper, the authors present another view of representation in connectionist networks and respond to statements about ALVINN by both Vera and Simon and Greeno and Moore (1993).

TL;DR: This article presents counter evidence against Smolensky's theory that human intuitive/nonconscious congnitive processes can only be accurately explained in terms of subsymbolic computations carried out in artificial neural networks, and presents symbolic learning models of two well-studied, complicated cognitive tasks involving nonconscious acquisition of information.

TL;DR: A new image-based method of reasoning, called ISR for indeterminacy in spatial reasoning, which dynamically constructs and inspects multiple images to reason about spatial prepositional phrases is proposed, which demonstrates that, with the help of specialized procedures, imagery can be more accurate for reasoning about spatially indeterminate descriptions.

TL;DR: A highly interactive model is described in which an expectation-driven parser generates multiple question candidates, including partially-specified candidates, and three experiments on human question answering provide evidence that working memory load during question reading is affected by processes related to answer retrieval.

TL;DR: The goal was to identify process models that could account for previously reported data on the interaction between how a learner encounters category variance across a series of training samples and whether the task instructions suggested an active, hypothesis-testing approach, or a more passive learning mode.