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10.1016/j.neubiorev.2014.06.001 http://scihub22266oqcxt.onion/10.1016/j.neubiorev.2014.06.001 C4253563!4253563!24929218     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24929218.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Neurosci+Biobehav+Rev 2014 ; 46 Pt 1 (ä): 30-43 Nephropedia Template TP {{tp|p=24929218|t=2014. Bayesian modeling of flexible cognitive control |pdf=|usr=}}{{24929218}} gab.com Text Bayesian modeling of flexible cognitive control JO: Neurosci Biobehav Rev http://www.kidney.de/pget.php?&tw=1&pmid=24929218 #FOAvip ?Cognitive control? describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation. Twit Text FOAVip Bayesian modeling of flexible cognitive control ????Neurosci Biobehav Rev http://www.kidney.de/pget.php?&tw=1&pmid=24929218 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24929218 #FOAvip English Wikipedia <ref>{{Cite pmid|24929218}}</ref> Bayesian modeling of flexible cognitive control #MMPMID24929218 Jiang J; Heller K; Egner T Neurosci Biobehav Rev 2014[Oct]; 46 Pt 1 (ä): 30-43 PMID24929218show ga ?Cognitive control? describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation. äBayesian modeling of flexible cognitive control (epmc).pdf DeepDyve Pubget Overpricing