The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Semantic Cognition: A Parallel Distributed Processing Approach
Abstract
This groundbreaking monograph offers a mechanistic theory of the representation and use of semantic knowledge, integrating the strengths and overcoming many of the weaknesses of hierarchical, categorization-based approaches, similarity-based approaches, and the approach often called "theory theory." Building on earlier models by Geoffrey Hinton in the 1980s and David Rumelhart in the early 1990s, the authors propose that performance in semantic tasks arises through the propagation of graded signals in a system of interconnected processing units. The representations used in performing these tasks are patterns of activation across units, governed by weighted connections among them. Semantic knowledge is acquired through the gradual adjustment of the strengths of these connections in the course of day-to-day experience. The authors show how a simple computational model proposed by Rumelhart exhibits a progressive differentiation of conceptual knowledge, paralleling aspects of cognitive development seen in the work of Frank Keil and Jean Mandler. The authors extend the model to address aspects of conceptual knowledge acquisition in infancy, disintegration of conceptual knowledge in dementia, "basic-level" effects and their interaction with expertise, and many findings introduced to support the idea that semantic cognition is guided by naive, domain-specific theories. Bradford Books imprint
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Cites
Learning representations by back-propagating errors
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Self-citationLearning to predict by the methods of temporal differences
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Cited by
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Where do you know what you know? The representation of semantic knowledge in the human brain
Patterson, K., Nestor, P. J., & Rogers, T. T. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2277 · Cited by 2,706
Self-citationThe neural and computational bases of semantic cognition
Ralph, M. A. L., Jefferies, E., Patterson, K., & Rogers, T. T. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.150 · Cited by 1,864
Self-citationHow to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
Kumaran, D., Hassabis, D., & McClelland, J. L. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.05.004 · Cited by 759
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Metadata
- Authors
- Rogers, T. T., & McClelland, J. L.
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2004-06-18
- OpenAlex
- W4249882522
- Cited by
- 536
- References
- 12
- FWCI
- 5.78
- Type
- book
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.