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While it is important for the evidence supporting practice guidelines to be current, that is often not the case. The advent of living systematic reviews has made the concept of “living guidelines” realistic, with the promise to provide timely, up-to-date and high-quality guidance to target users. We define living guidelines as an optimization of the guideline development process to allow updating individual recommendations as soon as new relevant evidence becomes available. A major implication of that definition is that the unit of update is the individual recommendation and not the whole guideline. We then discuss when living guidelines are appropriate, the workflows required to support them, the collaboration between living systematic reviews and living guideline teams, the thresholds for changing recommendations, and potential approaches to publication and dissemination. The success and sustainability of the concept of living guideline will depend on those of its major pillar, the living systematic review. We conclude that guideline developers should both experiment with and research the process of living guidelines.

Readers generate situation models representing described events, but the nature of these representations may differ depending on the reading goals. We assessed whether instructions to pay attention to different situational dimensions affect how individuals structure their situation models (Exp. 1) and how they update these models when situations change (Exp. 2 ). In Experiment 1 , participants read and segmented narrative texts into events. Some readers were oriented to pay specific attention to characters or space. Sentences containing character or spatial-location changes were perceived as event boundaries—particularly if the reader was oriented to characters or space, respectively. In Experiment 2 , participants read narratives and responded to recognition probes throughout the texts. Readers who were oriented to the spatial dimension were more likely to update their situation models at spatial changes; all readers tracked the character dimension. The results from both experiments indicated that attention to individual situational dimensions influences how readers segment and update their situation models. More broadly, the results provide evidence for a global situation model updating mechanism that serves to set up new models at important narrative changes.

Robust and accurate visual tracking is a challenging problem in computer vision. In this paper, we exploit spatial and semantic convolutional features extracted from convolutional neural networks in continuous object tracking. The spatial features retain higher resolution for precise localization and semantic features capture more semantic information and less fine-grained spatial details. Therefore, we localize the target by fusing these different features, which improves the tracking accuracy. Besides, we construct the multi-scale pyramid correlation filter of the target and extract its spatial features. This filter determines the scale level effectively and tackles target scale estimation. Finally, we further present a novel model updating strategy, and exploit peak sidelobe ratio (PSR) and skewness to measure the comprehensive fluctuation of response map for efficient tracking performance. Each contribution above is validated on 50 image sequences of tracking benchmark OTB-2013. The experimental comparison shows that our algorithm performs favorably against 12 state-of-the-art trackers.

Pupillometry research has experienced an enormous revival in the last two decades. Here we briefly review the surge of recent studies on task-evoked pupil dilation in the context of cognitive control tasks with the primary aim being to evaluate the feasibility of using pupil dilation as an index of effort exertion, rather than task demand or difficulty. Our review shows that across the three cognitive control domains of updating, switching, and inhibition, increases in task demands typically leads to increases in pupil dilation. Studies show a diverging pattern with respect to the relationship between pupil dilation and performance and we show how an effort account of pupil dilation can provide an explanation of these findings. We also discuss future directions to further corroborate this account in the context of recent theories on cognitive control and effort and their potential neurobiological substrates.

Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.

The practical advantages of Bayesian inference are demonstrated here through two concrete examples. In the first example, we wish to learn about a criminal's IQ: a problem of parameter estimation. In the second example, we wish to quantify and track support in favor of the null hypothesis that Adam Sandier movies are profitable regardless of their quality: a problem of hypothesis testing. The Bayesian approach unifies both problems within a coherent predictive framework, in which parameters and models that predict the data successfully receive a boost in plausibility, whereas parameters and models that predict poorly suffer a decline. Our examples demonstrate how Bayesian analyses can be more informative, more elegant, and more flexible than the orthodox methodology that remains dominant within the field of psychology.

Summary Model updating based on vibration response measurements is a technique for reducing inherent modeling errors in finite element (FE) models that arise from simplifications, idealized connections, and uncertainties with regard to material properties. Updated FE models, which have relatively fewer discrepancies with their real structural counterparts, provide more in‐depth predictions of the dynamic behaviors of those structures for future analysis. In this study, we develop a full‐scale FE model of a major long‐span bridge and update the model to improve an agreement between the identified modal properties of the real measured data and those from the FE model using a Bayesian model updating scheme. Sensitivity‐based cluster analysis is performed to determine robust and efficient updating parameters, which include physical parameters having similar effects on targeted natural frequencies. The hybrid Monte Carlo method, one of the Markov chain Monte Carlo sampling methods, is used to obtain the posterior probability distributions of the updating parameters. Finally, the uncertainties of the updated parameters and the variability of the FE model's modal properties are evaluated.

When challenged with information about the future, healthy participants show an optimistically biased updating pattern, taking desirable information more into account than undesirable information. However, it is unknown how patients suffering from major depressive disorder (MDD), who express pervasive pessimistic beliefs, update their beliefs when receiving information about their future. Here we tested whether an optimistically biased information processing pattern found in healthy individuals is absent in MDD patients. MDD patients (n = 18; 13 medicated; eight with co-morbid anxiety disorder) and healthy controls (n = 19) estimated their personal probability of experiencing 70 adverse life events. After each estimate participants were presented with the average probability of the event occurring to a person living in the same sociocultural environment. This information could be desirable (i.e. average probability better than expected) or undesirable (i.e. average probability worse than expected). To assess how desirable versus undesirable information influenced beliefs, participants estimated their personal probability of experiencing the 70 events a second time. Healthy controls showed an optimistic bias in updating, that is they changed their beliefs more toward desirable versus undesirable information. Overall, this optimistic bias was absent in MDD patients. Symptom severity correlated with biased updating: more severely depressed individuals showed a more pessimistic updating pattern. Furthermore, MDD patients estimated the probability of experiencing adverse life events as higher than healthy controls. Our findings raise the intriguing possibility that optimistically biased updating of expectations about one's personal future is associated with mental health.

Models of updating a set of priors either do not allow a decision maker to make inference about her priors (full bayesian updating or FB) or require an extreme degree of selection (maximum likelihood updating or ML). I characterize a general method for updating a set of priors, partial bayesian updating (PB), in which the decision maker (1) utilizes an event-dependent threshold to determine whether a prior is likely enough, conditional on observed information, and then (2) applies Bayes’ rule to the sufficiently likely priors. I show that PB nests FB and ML and explore its behavioral properties.

Human perceivers continually react to the social world implicitly—that is, spontaneously and rapidly. Earlier research suggested that implicit impressions of other people are slower to change than self-reported impressions in the face of contradictory evidence, often leaving them miscalibrated from what one learns to be true. Recent work, however, has identified conditions under which implicit impressions can be rapidly updated. Here, we review three lines of work showing that implicit impressions are responsive to information that is highly diagnostic, believable, or reframes earlier experience. These findings complement ongoing research on mechanisms of changing implicit impressions in a wider variety of groups, from real people to robots, and provide support for theoretical frameworks that embrace greater unity in the factors that can impact implicit and explicit social cognition.