Bayesian decision theory in sensorimotor control.

@article{citeulike:733231, abstract = {Action selection is a fundamental decision process for us, and depends on the state of both our body and the environment. Because signals in our sensory and motor systems are corrupted by variability or noise, the nervous system needs to estimate these states. To select an optimal action these state estimates need to be combined with knowledge of the potential costs or rewards of different action outcomes. We review recent studies that have investigated the mechanisms used by the nervous system to solve such estimation and decision problems, which show that human behaviour is close to that predicted by Bayesian Decision Theory. This theory defines optimal behaviour in a world characterized by uncertainty, and provides a coherent way of describing sensorimotor processes.}, address = {Brain and Cognitive Sciences, Massachusetts Institute of Technology, Building NE46-4053, Cambridge, Massachusetts, 02139, USA.}, author = {K\"{o}rding, Konrad P P. and Wolpert, Daniel M M. }, citeulike-article-id = {733231}, doi = {10.1016/j.tics.2006.05.003}, issn = {1364-6613}, journal = {Trends Cogn Sci}, keywords = {learning, motor\_control}, month = {June}, posted-at = {2007-12-10 16:57:30}, priority = {4}, title = {Bayesian decision theory in sensorimotor control.}, url = {http://dx.doi.org/10.1016/j.tics.2006.05.003}, year = {2006} }

TY - JOUR ID - citeulike:733231 L3 - citeulike-article-id:733231 TI - Bayesian decision theory in sensorimotor control. JF - Trends Cogn Sci AD - Brain and Cognitive Sciences, Massachusetts Institute of Technology, Building NE46-4053, Cambridge, Massachusetts, 02139, USA. SN - 1364-6613 N2 - Action selection is a fundamental decision process for us, and depends on the state of both our body and the environment. Because signals in our sensory and motor systems are corrupted by variability or noise, the nervous system needs to estimate these states. To select an optimal action these state estimates need to be combined with knowledge of the potential costs or rewards of different action outcomes. We review recent studies that have investigated the mechanisms used by the nervous system to solve such estimation and decision problems, which show that human behaviour is close to that predicted by Bayesian Decision Theory. This theory defines optimal behaviour in a world characterized by uncertainty, and provides a coherent way of describing sensorimotor processes. KW - learning KW - motor_control AU - Körding, Konrad P AU - Wolpert, Daniel M PY - 2006/06/24/ UR - http://dx.doi.org/10.1016/j.tics.2006.05.003 ER -