Bayesian decision theory in sensorimotor control.

By Konrad Paul Kording, D. M. Wolpert. Trends in Cognitive Sciences, 2006

852 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/22425feb4555bfe5fca1ce54f8aaf815fe255ffd

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Citations in the corpus

Spatiotemporal Movement Planning and Rapid Adaptation for Manual Interaction (2013, methodology): “Finally, we modeled the observed processes leading to successful handover in a probabilistic framework [18,19].”
Uncertainty in perception and the Hierarchical Gaussian Filter (2014, methodology): “…techniques which optimize a free-energy bound on the surprise about sensory inputs, given a model of the environment, and represent a special case of the general “Bayesian brain” hypothesis (Dayan et al., 1995; Knill and Pouget, 2004; Körding and Wolpert, 2006; Friston, 2009; Doya et al., 2011).”
The nonlinear nature of learning : a differential learning approach (2012, background): “Meanwhile noise has become an essential component of a few motor control theories [14, 15].”
Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. (2012, neutral): “…model is inverted in order to establish a mapping from desired effects to motor commands (Carpenter & Williams, 1995; Jordan & Rumelhart, 1992; Kilner, Friston, & Frith, 2007; Körding & Wolpert, 2006; Rao, Shon, & Meltzoff, 2007; Wolpert, Doya, & Kawato, 2003; Wolpert, Ghahramani, & Jordan, 1995).”
Bayesian just-so stories in psychology and neuroscience. (2012, methodology): “After reviewing the literature on integrating perceptual information both within and between modalities, Körding and Wolpert (2006) concluded that " people exhibit approximately Bayes-optimal behaviour.”
Bayesian models of cognition (2008, background): “…human inductive learning and generalization (Tenenbaum, Griﬃths, & Kemp, 2006), visual scene perception (Yuille & Kersten, 2006), motor control (Kording & Wolpert, 2006), semantic memory (Steyvers, Griﬃths, & Dennis, 2006), language processing and acquisition (Chater & Manning, 2006; Xu &…”
Ecological cognition: expert decision-making behaviour in sport (2017, background): “…with reference to chunking theory (Chase & Simon, 1973), and this and other memory-based representations are assumed to underpin experts’ performance superiority, particularly with respect to decisionmaking (Tenenbaum & Gershgoren, 2014; see Kording & Wolpert, 2006 for a Bayesian formalisation).”
Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques (2013, background): “Further, if the spontaneous activity fluctuations are considered as predictive representations as in a Bayesian framework [Pouget et al., 2003; Körding and Wolpert, 2006; Sadaghiani et al., 2010] in which ongoing cortical activity represents a continuous top–down prediction or expectation, then sele”
Computational mechanisms of sensorimotor control. (2011, background): “…states that the probability of different states being correct (termed the posterior) is produced by combining the probability of receiving the sensory information if that state were true (termed the likelihood ) with the prior probability of that state (for a review see Kö rding and Wolpert, 2006).”
Prediction, Cognition and the Brain (2009, background): “This can be accomplished by accumulating information related to statistical regularities while dealing with constant noise and uncertainty in the environment (Kording and Wolpert, 2006), as well as applying inference rules or deducing analogies between events (Pezzulo et al., 2008; Bar, 2009).”
Bayesian sampling in visual perception (2011, background): “2), where n(t) is as an Ornstein–Uhlenbeck process (5) with zero mean and deviation σ (σ = 1.”
Behavioral / Systems / Cognitive Striatal Prediction Error Modulates Cortical Coupling (2010, neutral): “Previous studies have shown that many human perceptual and motor processes approximate the behavior of an ideal Bayesian learner (Kersten et al., 2004; Körding and Wolpert, 2006).”
Principles of sensorimotor learning (2011, neutral): “16 ).”