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Despite considerable studies focused on the symptoms of posttraumatic stress disorder (PTSD), little is understood about how symptoms of PTSD naturalistically change over time.

Meta-analysis is a crucial tool for accumulating evidence in basic and applied research. In the open science era, meta-analysis becomes an important way for integrating open data from different sources.

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community.

Nonsignificant results are common in psychological research and can be easily misinterpreted as evidence for accepting null hypothesis. This misinterpretation may lead to false statistical inferences in empirical research.

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces.

During the past two decades, cognitive neuroscientists have sought to elucidate the common neural basis of the experience of beauty.

People display systematic priorities to self-related stimuli. As the self is not a unified entity however, it remains unclear which aspects of the self are crucial to producing this stimulus prioritization.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1.

P values and confidence intervals (CIs) are the most widely used statistical indices in scientific literature. Several surveys have revealed that these two indices are generally misunderstood.

Network analysis models (or Network Psychometrics) have been widely used in psychology research in recent years. Unlike latent variable models which conceive observable variables as outcomes of unobservable latent factors, network analysis models apply the graph theory to construct a network to depict the associations among observable variables.