887 research outputs found

    Hopping Transport in the Presence of Site Energy Disorder: Temperature and Concentration Scaling of Conductivity Spectra

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    Recent measurements on ion conducting glasses have revealed that conductivity spectra for various temperatures and ionic concentrations can be superimposed onto a common master curve by an appropriate rescaling of the conductivity and frequency. In order to understand the origin of the observed scaling behavior, we investigate by Monte Carlo simulations the diffusion of particles in a lattice with site energy disorder for a wide range of both temperatures and concentrations. While the model can account for the changes in ionic activation energies upon changing the concentration, it in general yields conductivity spectra that exhibit no scaling behavior. However, for typical concentrations and sufficiently low temperatures, a fairly good data collapse is obtained analogous to that found in experiment.Comment: 6 pages, 4 figure

    Metacognition in human decision-making: confidence and error monitoring

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    People are capable of robust evaluations of their decisions: they are often aware of their mistakes even without explicit feedback, and report levels of confidence in their decisions that correlate with objective performance. These metacognitive abilities help people to avoid making the same mistakes twice, and to avoid overcommitting time or resources to decisions that are based on unreliable evidence. In this review, we consider progress in characterizing the neural and mechanistic basis of these related aspects of metacognition—confidence judgements and error monitoring—and identify crucial points of convergence between methods and theories in the two fields. This convergence suggests that common principles govern metacognitive judgements of confidence and accuracy; in particular, a shared reliance on post-decisional processing within the systems responsible for the initial decision. However, research in both fields has focused rather narrowly on simple, discrete decisions—reflecting the correspondingly restricted focus of current models of the decision process itself—raising doubts about the degree to which discovered principles will scale up to explain metacognitive evaluation of real-world decisions and actions that are fluid, temporally extended, and embedded in the broader context of evolving behavioural goals

    Simple Lattice-Models of Ion Conduction: Counter Ion Model vs. Random Energy Model

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    The role of Coulomb interaction between the mobile particles in ionic conductors is still under debate. To clarify this aspect we perform Monte Carlo simulations on two simple lattice models (Counter Ion Model and Random Energy Model) which contain Coulomb interaction between the positively charged mobile particles, moving on a static disordered energy landscape. We find that the nature of static disorder plays an important role if one wishes to explore the impact of Coulomb interaction on the microscopic dynamics. This Coulomb type interaction impedes the dynamics in the Random Energy Model, but enhances dynamics in the Counter Ion Model in the relevant parameter range.Comment: To be published in Phys. Rev.

    Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex

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    Humans can navigate flexibly to meet their goals. Here, we asked how the neural representation of allocentric space is distorted by goal-directed behavior. Participants navigated an agent to two successive goal locations in a grid world environment comprising four interlinked rooms, with a contextual cue indicating the conditional dependence of one goal location on another. Examining the neural geometry by which room and context were encoded in fMRI signals, we found that map-like representations of the environment emerged in both hippocampus and neocortex. Cognitive maps in hippocampus and orbitofrontal cortices were compressed so that locations cued as goals were coded together in neural state space, and these distortions predicted successful learning. This effect was captured by a computational model in which current and prospective locations are jointly encoded in a place code, providing a theory of how goals warp the neural representation of space in macroscopic neural signals

    Indications for liver transplantation in the cyclosporine era

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    One hundred seventy orthotopic liver transplants were performed under conventional immunosuppression with azathioprine and steroids with 1- and 5-year survivals of 32.9% and 20.0%, respectively. Since the introduction of cyclosporine-prednisone therapy in March 1980, 313 primary orthotopic liver transplants have been performed. Actuarial survivals at 1 and 5 years have improved to 69.7% and 62.8%, respectively. Biliary atresia is now the most common indication for liver replacement. In adults, primary biliary cirrhosis and sclerosing cholangitis have become more common indications for transplantation, and alcoholic cirrhosis and primary liver malignancy as indications have declined. Early enthusiasm for liver transplantation in patients with hepatic cancer has been tempered by the finding that recurrence is both common and rapid. An increasing number of patients with inborn errors of metabolism originating in the liver are receiving transplants, including patients with Wilson's disease, tyrosinemia, alpha-1-antitrypsin deficiency, glycogen storage disease, familial hypercholesterolemia, and hemochromatosis. Survival in this group of patients has been excellent (74.4% at 1 and 5 years). A hemophiliac who received a transplant for postnecrotic cirrhosis has survived and may have been cured of his hemophilia. About 20% of patients require retransplantation for rejection, technical failure, or primary graft failure. Only 4 of the patients receiving retransplants under conventional immunosuppression survived beyond 6 months, and all died within 14 months of retransplantation. Sixty-eight patients have received retransplants under cyclosporine-prednisone. Thirty-one patients are surviving, all for at least 1 year. Six of the 12 patients requiring a third transplant are alive 2 to 3 years after the primary operation. An aggressive approach to retransplantation in the patient with a failed graft is justified

    Comparing families of dynamic causal models

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    Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data
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