Bayesian Cue Integration is a computational framework within multisensory integration that combines sensory cues in a manner optimal under uncertainty, using Bayes' theorem to weight each cue according to its reliability.
52
Publications
6K
Citations
143
Authors
94
Institutions
Publications per year
2017–2026
17
Parents
Publications
52
Bayesian integration in sensorimotor learning
Konrad P. Körding, Daniel M. Wolpert · Nature · 2004 · 2.1K citations
Learning to integrate arbitrary signals from vision and touch
Marc O. Ernst · Journal of Vision · 2007 · 339 citations · Full text
1–5 of 52
Authors
143
- DE
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- WJ
3
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- MN
3
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- RB
2
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- WR
2
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1–5 of 143
Institutions
94
University of Rochester
Rochester, United States
6 h-index
13 publications
2.3K citations
Durham University
Durham, United Kingdom
4 h-index
10 publications
253 citations
Baylor College of Medicine
Houston, United States
3 h-index
5 publications
457 citations
University of California, Davis
Davis, United States
2 h-index
5 publications
95 citations
University of Edinburgh
Edinburgh, United Kingdom
2 h-index
3 publications
163 citations
Max Planck Institute for Biological Cybernetics
Tübingen, Germany
2 h-index
4 publications
876 citations
University College London
London, United Kingdom
2 h-index
3 publications
4.2K citations
New York University
New York, United States
2 h-index
4 publications
264 citations
1–8 of 94
Venues
- Journal of Vision
11 publications · 994 citations
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- PLoS ONE
4 publications · 317 citations
4
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- Proceedings of the Royal Society B Biological Sciences
3 publications · 238 citations
3
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- Scientific Reports
2 publications · 51 citations
2
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- Journal of Neuroscience
2 publications · 590 citations
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