Computational modeling of behavior has revolutionized psychology and neuroscience. By
fitting models to experimental data we can probe the algorithms underlying behavior, find …

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 …

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

Linear mixed-effects models have increasingly replaced mixed-model analyses of variance
for statistical inference in factorial psycholinguistic experiments. Although LMMs have many …

Statistical procedures such as Bayes factor model selection and Bayesian model averaging
require the computation of normalizing constants (eg, marginal likelihoods). These …

Fiber photometry is a key technique for characterizing brain-behavior relationships in vivo.
Initially, it was primarily used to report calcium dynamics as a proxy for neural activity via …

We introduce the fundamental tenets of Bayesian inference, which derive from two basic
laws of probability theory. We cover the interpretation of probabilities, discrete and …

Motivation Validation of variable selection and predictive performance is crucial in
construction of robust multivariate models that generalize well, minimize overfitting and …

The central tendency bias is a robust finding in data from experiments using Likert scales to
elicit responses. The present paper offers a Bayesian perspective on this bias, explaining it …

In today's world of work, the need for digital communication and collaboration competencies
became even more prevalent during the ongoing COVID-19 pandemic. Yet, research and …