The DOI matches its Crossref record; 52 of the 54 references it cites were found in a registry or an index.
Forward Models: Supervised Learning with a Distal Teacher
Abstract
Internal models of the environment have an important role to play in adaptive systems, in general, and are of particular importance for the supervised learning paradigm. In this article we demonstrate that certain classical problems associated with the notion of the “teacher” in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multilayer networks.
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“In turn, sensory prediction errors are used to control movements online, cancel sensory reafference due to self-generated movement, perform state estimation, guide motor learning, and update the forward model (Jordan and Rumelhart, 1992; Miall et al., 1993; Wolpert et al., 1995; Doya, 1999; Shadmehr et al., 2010).”
From Cerebellum, Predictions and Errors (2019) · cites this paper as Jordan and Rumelhart, 1992
In the citing paper
“With the motor command and current sensory information as its inputs, a forward internal model predicts the consequences of motor actions (Jordan and Rumelhart, 1992; Miall et al., 1993; Miall and Wolpert, 1996).”
From Cerebellum, Predictions and Errors (2019) · cites this paper as Jordan and Rumelhart, 1992
Citations
Cites
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-citationParallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
Self-citationSelf-organized formation of topologically correct feature maps
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Learning to predict by the methods of temporal differences
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Theory and Practice of Recursive Identification
Lennart Ljung, A., & Söderström, T. (1983) · Lund University Publications (Lund University) · Cited by 3,989
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Research works printed but not linked: 12
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- Gelfand I.M., 1963, Calculus of variations
- Hinton G.E., 1986, Parallel distributed processing
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Cited by
Sorted by citations
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
Introduction to the theory of neural computation
Hertz, J., Krogh, A., & Palmer, R. G. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,410
An internal model for sensorimotor integration
Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995) · Science · doi:10.1126/science.7569931 · Cited by 3,553
Self-citationUnderstanding normal and impaired word reading: Computational principles in quasi-regular domains.
Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996) · Psychological Review · doi:10.1037/0033-295x.103.1.56 · Cited by 2,698
Internal models for motor control and trajectory planning.
Kawato, M. (1999) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(99)00028-8 · Cited by 2,685
Metadata
- Authors
- Jordan, M. I., & Rumelhart, D. E.
- Affiliations
- Massachusetts Institute of Technology (US)
- Stanford University (US)
- Corresponding author
- Jordan, Michael I.
- ORCID
- Jordan, Michael I.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1992-07
- Volume
- 16
- Issue
- 3
- Pages
- 307-354
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2043968544
- Cited by
- 1,451
- References
- 54
- Citations by year
- 2026: 18 · 2025: 15 · 2024: 33 · 2023: 47 · 2022: 40 · 2021: 40 · 2020: 47 · 2019: 65 · 2018: 54 · 2017: 37 · 2016: 40 · 2015: 48 · 2014: 42 · 2013: 48 · 2012: 47
- FWCI
- 66.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- forward model, internal models, supervised learning, adaptive systems, learned models, multilayer networks, dynamical systems
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