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James L. Mcclelland - One of the best experts on this subject based on the ideXlab platform.
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precis of semantic cognition a parallel Distributed Processing approach
Behavioral and Brain Sciences, 2008Co-Authors: Timothy T Rogers, James L. McclellandAbstract:In this precis of our recent book, Semantic Cognition: A Parallel Distributed Processing Approach (Rogers & McClelland 2004), we present a parallel Distributed Processing theory of the acquisition, representation, and use of human semantic knowledge. The theory proposes that semantic abilities arise from the flow of activation among simple, neuron-like Processing units, as governed by the strengths of interconnecting weights; and that acquisition of new semantic information involves the gradual adjustment of weights in the system in response to experience. These simple ideas explain a wide range of empirical phenomena from studies of categorization, lexical acquisition, and disordered semantic cognition. In this precis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the "theory-theory" approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form "good" or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed- forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed.
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the parallel Distributed Processing approach to semantic cognition
Nature Reviews Neuroscience, 2003Co-Authors: James L. Mcclelland, Timothy T RogersAbstract:How do we know what properties something has, and which of its properties should be generalized to other objects? How is the knowledge underlying these abilities acquired, and how is it affected by brain disorders? Our approach to these issues is based on the idea that cognitive processes arise from the interactions of neurons through synaptic connections. The knowledge in such interactive and Distributed Processing systems is stored in the strengths of the connections and is acquired gradually through experience. Degradation of semantic knowledge occurs through degradation of the patterns of neural activity that probe the knowledge stored in the connections. Simulation models based on these ideas capture semantic cognitive processes and their development and disintegration, encompassing domain-specific patterns of generalization in young children, and the restructuring of conceptual knowledge as a function of experience.
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The parallel Distributed Processing approach to semantic cognition
Nature Reviews Neuroscience, 2003Co-Authors: James L. Mcclelland, Timothy T RogersAbstract:Semantic cognition encompasses human performance based on knowledge about the properties of objects, relations among objects and word meanings. One approach to semantic cognition has arisen within the parallel Distributed Processing (PDP) framework, in which cognitive processes arise from interactions of neurons through synaptic connections. The knowledge that governs Processing is stored in the strengths of the connections and is acquired gradually through experience, simulating conceptual development in childhood. These ideas have been explored in a simulated neural network model that learns propositions about objects and their properties. The model is trained with propositions about several different plant and animal concepts, including trees, flowers, fish, birds and land animals. The model contains 'hidden' units between its inputs and outputs, over which it learns internal representations that capture semantic relationships between concepts. Learning is influenced by coherent covariation of properties — that is, by co-occurrence of the same ensemble of properties (has wings, has feathers, can fly) in a number of different items (in this case, all the birds). The model explains the tendency towards progressive differentiation of concepts observed in development and the reverse fine-to-coarse deterioration observed in a progressive neuropathological condition called semantic dementia. With appropriate assumptions about covariation of properties, and about the relative frequencies of concepts and of the words used to name them, the model also addresses many further findings in development, dementia and normal adult cognition. Like other, similarity-based theories, the model accounts for the influence of graded category membership on semantic task performance, and for frequency and typicality effects. It also provides a means of addressing some of the criticisms of these other theories. Specifically, it indicates how some properties of objects, including causal properties, come to be more important than other properties; why some groups of items seem to form natural or coherent categories; how domain-specific patterns of generalization and differentiation might arise; and how conceptual knowledge structures might reorganize over the course of development. The PDP approach might provide a mechanistic framework that can address many of the phenomena emphasized in an alternative approach based on naive domain theories specifying causal relations between objects and their properties. Some of the relevant phenomena have yet to be addressed by PDP models, leaving this as a task for the future. How do we know what properties something has, and which of its properties should be generalized to other objects? How is the knowledge underlying these abilities acquired, and how is it affected by brain disorders? Our approach to these issues is based on the idea that cognitive processes arise from the interactions of neurons through synaptic connections. The knowledge in such interactive and Distributed Processing systems is stored in the strengths of the connections and is acquired gradually through experience. Degradation of semantic knowledge occurs through degradation of the patterns of neural activity that probe the knowledge stored in the connections. Simulation models based on these ideas capture semantic cognitive processes and their development and disintegration, encompassing domain-specific patterns of generalization in young children, and the restructuring of conceptual knowledge as a function of experience.
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The Appeal of Parallel Distributed Processing
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations, 1Co-Authors: David E. Rumelhart, James L. McclellandAbstract:This chapter contains sections titled: Parallel Distributed Processing, Examples Of PDP Models, Representation and Learning in PDP Models, Origins of Parallel Distributed Processing, Acknowledgments
Timothy T Rogers - One of the best experts on this subject based on the ideXlab platform.
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precis of semantic cognition a parallel Distributed Processing approach
Behavioral and Brain Sciences, 2008Co-Authors: Timothy T Rogers, James L. McclellandAbstract:In this precis of our recent book, Semantic Cognition: A Parallel Distributed Processing Approach (Rogers & McClelland 2004), we present a parallel Distributed Processing theory of the acquisition, representation, and use of human semantic knowledge. The theory proposes that semantic abilities arise from the flow of activation among simple, neuron-like Processing units, as governed by the strengths of interconnecting weights; and that acquisition of new semantic information involves the gradual adjustment of weights in the system in response to experience. These simple ideas explain a wide range of empirical phenomena from studies of categorization, lexical acquisition, and disordered semantic cognition. In this precis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the "theory-theory" approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form "good" or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed- forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed.
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the parallel Distributed Processing approach to semantic cognition
Nature Reviews Neuroscience, 2003Co-Authors: James L. Mcclelland, Timothy T RogersAbstract:How do we know what properties something has, and which of its properties should be generalized to other objects? How is the knowledge underlying these abilities acquired, and how is it affected by brain disorders? Our approach to these issues is based on the idea that cognitive processes arise from the interactions of neurons through synaptic connections. The knowledge in such interactive and Distributed Processing systems is stored in the strengths of the connections and is acquired gradually through experience. Degradation of semantic knowledge occurs through degradation of the patterns of neural activity that probe the knowledge stored in the connections. Simulation models based on these ideas capture semantic cognitive processes and their development and disintegration, encompassing domain-specific patterns of generalization in young children, and the restructuring of conceptual knowledge as a function of experience.
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The parallel Distributed Processing approach to semantic cognition
Nature Reviews Neuroscience, 2003Co-Authors: James L. Mcclelland, Timothy T RogersAbstract:Semantic cognition encompasses human performance based on knowledge about the properties of objects, relations among objects and word meanings. One approach to semantic cognition has arisen within the parallel Distributed Processing (PDP) framework, in which cognitive processes arise from interactions of neurons through synaptic connections. The knowledge that governs Processing is stored in the strengths of the connections and is acquired gradually through experience, simulating conceptual development in childhood. These ideas have been explored in a simulated neural network model that learns propositions about objects and their properties. The model is trained with propositions about several different plant and animal concepts, including trees, flowers, fish, birds and land animals. The model contains 'hidden' units between its inputs and outputs, over which it learns internal representations that capture semantic relationships between concepts. Learning is influenced by coherent covariation of properties — that is, by co-occurrence of the same ensemble of properties (has wings, has feathers, can fly) in a number of different items (in this case, all the birds). The model explains the tendency towards progressive differentiation of concepts observed in development and the reverse fine-to-coarse deterioration observed in a progressive neuropathological condition called semantic dementia. With appropriate assumptions about covariation of properties, and about the relative frequencies of concepts and of the words used to name them, the model also addresses many further findings in development, dementia and normal adult cognition. Like other, similarity-based theories, the model accounts for the influence of graded category membership on semantic task performance, and for frequency and typicality effects. It also provides a means of addressing some of the criticisms of these other theories. Specifically, it indicates how some properties of objects, including causal properties, come to be more important than other properties; why some groups of items seem to form natural or coherent categories; how domain-specific patterns of generalization and differentiation might arise; and how conceptual knowledge structures might reorganize over the course of development. The PDP approach might provide a mechanistic framework that can address many of the phenomena emphasized in an alternative approach based on naive domain theories specifying causal relations between objects and their properties. Some of the relevant phenomena have yet to be addressed by PDP models, leaving this as a task for the future. How do we know what properties something has, and which of its properties should be generalized to other objects? How is the knowledge underlying these abilities acquired, and how is it affected by brain disorders? Our approach to these issues is based on the idea that cognitive processes arise from the interactions of neurons through synaptic connections. The knowledge in such interactive and Distributed Processing systems is stored in the strengths of the connections and is acquired gradually through experience. Degradation of semantic knowledge occurs through degradation of the patterns of neural activity that probe the knowledge stored in the connections. Simulation models based on these ideas capture semantic cognitive processes and their development and disintegration, encompassing domain-specific patterns of generalization in young children, and the restructuring of conceptual knowledge as a function of experience.
Yoshikazu Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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A perspective of OSI standardization: object-oriented architecture for Distributed Processing
Future Generation Computer Systems, 1992Co-Authors: Yoshikazu KobayashiAbstract:Abstract After more than ten years of efforts, OSI has become real. Many service and protocol standards had been developed based on a single “communication” architecture, known as OSI Basic Reference Model. Now, the focus in ISO/IEC JTC 1/SC 21 is the development of a “Distributed Processing” architecture, a long-term project known as Reference Model of Open Distributed Processing. This paper presents a perspective of the OSI standardization based on an object-oriented approach for Distributed application Processing.
Sylvan Kornblum - One of the best experts on this subject based on the ideXlab platform.
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a parallel Distributed Processing model of stimulus stimulus and stimulus response compatibility
Cognitive Psychology, 1999Co-Authors: Huazhong Zhang, Jun Zhang, Sylvan KornblumAbstract:Abstract A parallel Distributed Processing (PDP) model is proposed to account for choice reaction time (RT) performance in diverse cognitive and perceptual tasks such as the Stroop task, the Simon task, the Eriksen flanker task, and the stimulus–response compatibility task that are interrelated in terms of stimulus–stimulus and stimulus–response overlap (Kornblum, 1992). In multilayered (input–intermediate–output) networks, neuron-like nodes that represent stimulus and response features are grouped into mutually inhibitory modules that represent stimulus and response dimensions. The stimulus–stimulus overlap is implemented by a convergence of two input modules onto a common intermediate module, and the stimulus–response overlap by direct pathways representing automatic priming of outputs. Mean RTs are simulated in various simple tasks and, furthermore, predictions are generated for complex tasks based on performance in simpler tasks. The match between simulated and experimental results lends strong support for our PDP model of compatibility.
Ya-xin Han - One of the best experts on this subject based on the ideXlab platform.
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Architecture of a training simulator based on Distributed Processing
IEEE Aerospace and Electronic Systems Magazine, 1996Co-Authors: Shi-yu Gong, Liang-cai Liao, Ya-xin HanAbstract:The potential abilities of micro-computers make it possible that low cost training simulators are available. Distributed Processing is an effective way for implementation of this kind of simulator. This article presents a training simulator architecture based on Distributed Processing and discusses its role in the training simulation support system.
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The architecture of a training simulator based on Distributed Processing
Proceedings of the IEEE 1996 National Aerospace and Electronics Conference NAECON 1996, 1Co-Authors: Shi-yu Gong, Liang-cai Liao, Ya-xin HanAbstract:The potential abilities of microcomputers make it possible that training simulator at low cost is available. Distributed Processing is an effective way for the implementation of this kind of simulator. The article presents an architecture of training simulator based on Distributed Processing and discusses its role in training simulation supporting system.