Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.

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The widespread hypoactivity for the ADHD children on the go/no-go task is consistent with the hypothesis that response inhibition is a specific deficit in attention deficit hyperactivity disorder.

As one of the most prosperous classes of cluster-based materials reported to date, polyoxo-titanium clusters (PTCs) have been closely related to many photo-activities that broadly impact not only chemical but also energy and environmental sciences. In contrast to the well-developed polyoxometalates like polyoxotungstates and polyoxomolybdates, there is still large room for the development of PTCs. The exploration of crystalline PTC materials originates from the molecular model of technically important TiO materials but has been greatly hindered by their daunting and challenging synthesis. This review firstly summarizes the conventional and latest successful synthetic strategies applied to improve the poor degree of control of crystallization of PTCs. And attributed to the synthetic progress achieved in this area, there is a growing number of PTCs with diverse structures known to us, also enabling us to study their bandgap engineering and light absorption behaviours at the molecular level. In addition, exploitation of their applications in many fields is also under way.