Bayesian integration in sensorimotor learning

By Konrad Paul Kording, D. M. Wolpert. Nature, 2004

2,078 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f204c80c374772d66f28519795bd8929d0abf45a

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In Cognitive Neuroscience

Citations in the corpus

A computational neuroanatomy for motor control (2008, background): “Rather, our beliefs are a combination of what we predicted and what we observed (Kording and Wolpert 2004; Vaziri et al. 2006).”
Exemplar models as a mechanism for performing Bayesian inference (2010, background): “As a consequence, probabilistic models are becoming increasingly widespread in both cognitive science and neuroscience, providing explanations of behavior in domains as diverse as motor control ( Kording & Wolpert, 2004 ), reasoning (Oaksford & Chater, 1994), memory (Anderson & Milson, 1989), and pe”
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks (2014, background): “Behavioral and neuropsychophysical experiments [2, 3, 4] have suggested that the brain may indeed maintain such a representation and employ Bayesian inference and learning in a great variety of tasks in perception, sensori-motor integration, and sensory adaptation.”
Noise in the nervous system (2008, background): “Psychophysical experiments have confirmed that humans use these Bayesian inferences to allow them to cope with noise (and, more generally, with uncertainty) in both perception and actio”
Motor Control is Decision-Making (2012, neutral): “initiation, the display of hand position was extinguished [29].”
Does the brain have a baseline? Why we should be resisting a rest (2007, background): “Another suggestion for possible ‘intrinsic’ functionality of the brain was made recently by Raichle and Gusnard, in a reference to the literature on predictive coding and ‘top-down’ computational mechanisms in the brain (see e.g. Kording and Wolpert, 2004).”
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014, background): “Human behaviour is consistent with Bayesian inference in many sensory [1, 2, 3, 4], motor [5, 6] and cognitive [7, 8, 9] tasks.”
Free-energy and the brain (2007, background): “…machine that conforms to the same principles that govern the interrogation of scientific data (MacKay, 1956; Neisser, 1967; Ballard et al, 1983; Mumford, 1992; Kawato et al 1993; Rao and Ballard 1998; Dayan et al, 1995; Friston, 2003; Körding and Wolpert 2004; Kersten et al 2004; Friston 2005).”
Attention, Uncertainty, and Free-Energy (2010, background): “…of many accounts that emphasize the role of probabilistic inference in sensory processing; including sensorimotor integration (Wolpert et al., 1995; Körding and Wolpert, 2004), sensory integration (Jacobs, 1999; Ernst and Banks, 2002; Knill and Saunders, 2003; Alais and Burr, 2004), salience and…”
Rationality for Mortals: How People Cope with Uncertainty (2008, background): “At the lower levels of processing, some human capabilities apparently involve calculations that seem surprisingly diffi cult (e.g., Bayesian estimation in a sensorimotor context: Körding & Wolpert, 2004).”
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011, background): “Therefore many researchers have suggested that information processing in the brain carries out probabilistic, rather than logical, inference for making decisions and choosing actions [9–22].”