People have never played more video games, and many stakeholders are worried that this
activity might be bad for players. So far, research has not had adequate data to test whether …

Deep neural networks ('deep learning') have emerged as a technology of choice to tackle
problems in speech recognition, computer vision, finance, etc. However, adoption of deep …

It is common practice in correlational or quasiexperimental studies to use statistical control to
remove confounding effects from a regression coefficient. Controlling for relevant …

When used appropriately, non-significant p-values have the potential to further our
understanding of what does not work in education, and why. When misinterpreted, they can …

Statistical network models based on Pairwise Markov Random Fields (PMRFs) are popular
tools for analyzing multivariate psychological data, in large part due to their perceived role in …

Personalized networks of psychological symptoms aim to advance therapy by identifying
treatment targets for specific patients. Statistical relations in such networks can be estimated …

Psychoneuroimmunology and immunopsychiatry are quickly approaching a critical point
where the clinical translatability of their evidence base will be tested. To maximize chances …

In this article, we investigate the role of causal reasoning in robotics research. Inspired by a
categorization of human causal cognition, we propose a categorization of robot causal …

Much of our experiments are designed to uncover the cause (s) and effect (s) behind a
phenomenon (ie, data generating mechanism) we happen to be interested in. Uncovering …

Topic models are effective in capturing the latent semantics of large-scale textual data while
existing methods are normally designed and evaluated on balanced corpora. However, it …