A worrying number of psychological findings are not replicable. Diagnoses of the causes of
this “replication crisis,” and recommendations to address it, have nearly exclusively focused …

Any mature field of research in psychology—such as short-term/working memory—is
characterized by a wealth of empirical findings. It is currently unrealistic to expect a theory to …

Bayesian parameter estimation and Bayesian hypothesis testing present attractive
alternatives to classical inference using confidence intervals and p values. In part I of this …

Abstract Markov Chain Monte–Carlo (MCMC) is an increasingly popular method for
obtaining information about distributions, especially for estimating posterior distributions in …

The diffusion model is a commonly used tool to infer latent psychological processes
underlying decision making, and to link them to neural mechanisms based on reaction …

This article explains the foundational concepts of Bayesian data analysis using virtually no
mathematical notation. Bayesian ideas already match your intuitions from everyday …

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and
computational theories with which we can disentangle psychiatric conditions into the basic …

The marginal likelihood plays an important role in many areas of Bayesian statistics such as
parameter estimation, model comparison, and model averaging. In most applications …

Experiments in research on memory, language, and in other areas of cognitive science are
increasingly being analyzed using Bayesian methods. This has been facilitated by the …

Computational modeling is now ubiquitous in psychology, and researchers who are not
modelers may find it increasingly difficult to follow the theoretical developments in their field …