The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Competitive Learning: From Interactive Activation to Adaptive Resonance
Abstract
Functional and mechanistic comparisons are made between several network models of cognitive processing: competitive learning, interactive activation, adaptive resonance, and back propagation. The starting point of this comparison is the article of Rumelhart and Zipser (1985) on feature discovery through competitive learning. All the models which Rumelhart and Zipser (1985) have described were shown in Grossberg (1976b) to exhibit a type of learning which is temporally unstable. Competitive learning mechanisms can be stabilized in response to an arbitrary input environment by being supplemented with mechanisms for learning top‐down expectancies, or templates; for matching bottom‐up input patterns with the top‐down expectancies; and for releasing orienting reactions in a mismatch situation, thereby updating short‐term memory and searching for another internal representation. Network architectures which embody all of these mechanisms were called adaptive resonance models by Grossberg (1976c). Self‐stabilizing learning models are candidates for use in real‐world applications where unpredictable changes can occur in complex input environments. Competitive learning postulates are inconsistent with the postulates of the interactive activation model of McClelland and Rumelhart (1981), and suggest different levels of processing and interaction rules for the analysis of word recognition. Adaptive resonance models use these alternative levels and interaction rules. The self‐organizing learning of an adaptive resonance model is compared and contrasted with the teacher‐directed learning of a back propagation model. A number of criteria for evaluating real‐time network models of cognitive processing are described and applied.
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“Unsupervised learning algorithms, such as implemented in Self-Organizing Maps (e.g., Kohonen, 1982; Dufau et al., 2010) or Adaptive Resonance Theory (e.g., Grossberg, 1987; Glotin et al., 2010), provide specific mechanisms for performing orthographic chunking on the basis of a parallel letter input.”
From A Dual-Route Approach to Orthographic Processing (2011) · cites this paper as Grossberg, 1987
In the citing paper
“Action regulation in the pursuit of goals and plans faces a basic dilemma: It must be sufficiently stable and closed to stay focused on the goal and resist distractive influences; at the same time, it has to be open and flexible enough so that plans and priorities can be adjusted to new and unexpected circumstances.”
From The Life-Course Dynamics of Goal Pursuit and Goal Adjustment: A Two-Process Framework (2002) · cites this paper as Grossberg, 1987
Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740
McClelland, J. L., & Rumelhart, D. E. (1981) · Psychological Review · doi:10.1037/0033-295x.88.5.375 · Cited by 4,889
Self-Organization and Associative Memory
Kohonen, T. (1988) · Springer series in information sciences · doi:10.1007/978-3-662-00784-6 · Cited by 3,843
Bienenstock, E., Cooper, L., & Munro, P. (1982) · The Journal of Neuroscience · doi:10.1523/jneurosci.02-01-00032.1982 · Cited by 2,896
A massively parallel architecture for a self-organizing neural pattern recognition machine
Carpenter, G. A., & Grossberg, S. (1987) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(87)80014-2 · Cited by 2,525
Self-citationMullin, A., & Rosenblatt, F. F. (1963) · American Mathematical Monthly · doi:10.2307/2312103 · Cited by 2,211
On the genesis of abstract ideas.
Posner, M. I., & Keele, S. W. (1968) · Journal of Experimental Psychology · doi:10.1037/h0025953 · Cited by 1,983
Grossberg, S. (1976) · Biological Cybernetics · doi:10.1007/bf00344744 · Cited by 1,910
Self-citation
Research works printed but not linked: 4
- 10.1007/BF00344744 · DOI
- Grossberg S., 1982, Competition and cooperation in neural networks, 263
- Grossberg S., 1987, Pattern recognition by humans and machines, Vol. 1: Speech perception
- Rumelhart D.E., 1985, Institute for Cognitive Science Report 8506
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Barsalou, L. W. (1999) · Behavioral and Brain Sciences · doi:10.1017/s0140525x99002149 · Cited by 6,887
McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256
Is the P300 component a manifestation of context updating?
Donchin, E., & Coles, M. G. H. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058027 · Cited by 3,585
Tononi, G., & Cirelli, C. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.025 · Cited by 2,356
The time course of perceptual choice: The leaky, competing accumulator model.
Usher, M., & McClelland, J. L. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.550 · Cited by 2,152
Churchland, P. S., & Sejnowski, T. J. (1992) · doi:10.7551/mitpress/2010.001.0001 · Cited by 1,757
Learning in the Presence of Concept Drift and Hidden Contexts
Widmer, G., & Kubat, M. (1996) · Machine Learning · doi:10.1023/a:1018046501280 · Cited by 1,857
Forward models: supervised learning with a distal teacher
Jordan, M. I., & Rumelhart, D. E. (1992) · Cognitive Science · doi:10.1207/s15516709cog1603_1 · Cited by 1,451
Higher-order factors of the Big Five in a multi-informant sample.
DeYoung, C. G. (2006) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.91.6.1138 · Cited by 933
How schema and novelty augment memory formation
van Kesteren, M. T. R., Ruiter, D. J., Fernández, G., & Henson, R. N. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.02.001 · Cited by 860
Catastrophic forgetting, rehearsal and pseudorehearsal
ROBINS, A. (1995) · Connection Science · doi:10.1080/09540099550039318 · Cited by 833
The Life-Course Dynamics of Goal Pursuit and Goal Adjustment: A Two-Process Framework
Brandtstädter, J., & Rothermund, K. (2002) · Developmental Review · doi:10.1006/drev.2001.0539 · Cited by 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
Chapter 2 Design for a Working Memory
Oberauer, K. (2009) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(09)51002-x · Cited by 636
Exploration in deep reinforcement learning: A survey
Ladosz, P., Weng, L., Kim, M., & Oh, H. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.003 · Cited by 525
“An approach using the adaptive resonance theorem (ART) [150] was presented in [151] and was later extended in [152].”
MethodA Dual-Route Approach to Orthographic Processing
Grainger, J., & Ziegler, J. C. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00054 · Cited by 459
Bayesian just-so stories in psychology and neuroscience.
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436
Theories of Error Back-Propagation in the Brain
Whittington, J. C. R., & Bogacz, R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.12.005 · Cited by 420
The Hebbian paradigm reintegrated: local reverberations as internal representations
Amit, D. J. (1995) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00040164 · Cited by 388
Memory retention – the synaptic stability versus plasticity dilemma
Abraham, W. C., & Robins, A. (2005) · Trends in Neurosciences · doi:10.1016/j.tins.2004.12.003 · Cited by 353
A ballistic model of choice response time
Brown, S., & Heathcote, A. (2005) · Psychological Review · doi:10.1037/0033-295x.112.1.117 · Cited by 228
Brandtstädter, J. (2009) · Advances in Life Course Research · doi:10.1016/j.alcr.2009.03.002 · Cited by 205
Lee, T. S., Yang, C. F., Romero, R. D., & Mumford, D. (2002) · Nature Neuroscience · doi:10.1038/nn0602-860 · Cited by 205
Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking
Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1076-4 · Cited by 156
Statistical computations underlying the dynamics of memory updating
Gershman, S. J., Radulescu, A., Norman, K. A., & Niv, Y. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003939 · Cited by 101
AHaH Computing–From Metastable Switches to Attractors to Machine Learning
Nugent, M. A., & Molter, T. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085175 · Cited by 55
Metzinger, T. (2014) · Cognitive Neuroscience · doi:10.1080/17588928.2014.905519 · Cited by 37
The context repetition effect: Predicted events are remembered better, even when they don’t happen
Smith, T. A., Hasinski, A. E., & Sederberg, P. B. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0034067 · Cited by 33
Long-Term Memory Stabilized by Noise-Induced Rehearsal
Wei, Y., & Koulakov, A. A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3929-12.2014 · Cited by 29
Metadata
- Authors
- Grossberg, S.
- Affiliations
- Boston University (US)
- Corresponding author
- Grossberg, Stephen
- ORCID
- Grossberg, Stephen
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1987-01-03
- Volume
- 11
- Issue
- 1
- Pages
- 23-63
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2159110831
- Cited by
- 1,471
- References
- 58
- Citations by year
- 2026: 21 · 2025: 47 · 2024: 33 · 2023: 25 · 2022: 34 · 2021: 43 · 2020: 56 · 2019: 49 · 2018: 32 · 2017: 25 · 2016: 19 · 2015: 24 · 2014: 34 · 2013: 35 · 2012: 31
- FWCI
- 71.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Keywords
- competitive learning, adaptive resonance theory, interactive activation model, backpropagation, feature discovery, short-term memory, word recognition, levels of processing, cognitive processing models
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