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
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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
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- Keywords
- forward model, internal models, supervised learning, adaptive systems, learned models, multilayer networks, dynamical systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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