Yes, KL is very interesting, although its interpretation can be a bit unintuitive... Maybe the Jensen-Shannon divergence would be easier to use in applied contexts because it's easier to interpret, although I’m not sure if it has any disadvantages compared to KL... 🤷‍♂️🤷‍♂️🤷‍♂️

Check out the supplementary material too! The idea of using Kullback-Leibler divergence as a measure of differences between experimental conditions still occasionally crosses my mind...

In fact, much of my current work (the first paper of my PhD, hopefully a preprint before summer!) can't be understood without this article. This is where I first got into Bayesian stats, a year before I took Lee & Wagenmakers' JAGS course in Amsterdam!

Congrats! This is easily one of the best papers I've read on Hedge's reliability paradox—especially clear and accessible for experimental psychologists without a strong statistical background. Great to see it published in Psychological Methods!

Violencia justificada.

Yo estoy tentado de hacer uno sobre psicometría bayesiana, pero me estoy conteniendo...

Ojo!!! Muy bonito!! Puedes compartir por aquí que tal la recepción cuando lo des??

Al mejor de tres!!

The GLLAMM/GLVM framework goes one step further than linear mixed models because it allows us to decompose the so-called random-effects covariance matrix into common and unique latent factors. These frameworks (especially GLLAMM) provided the most empowering statistical insights I've encountered.

As always, the estimation improves as the number of subjects and trials increases.

1. First picture: 300 subjects and 50 trials per condition.
2. Second picture: 50 subjects and 300 trials per condition.

100 subjects, 50 trials per condition (e.g., congruent/incongruent trials in Stroop task), and six experimental tasks. The true correlation between all tasks is 0.50. Results are shown from a Bayesian linear mixed model (first matrix) and a Bayesian GLLAMM/GLVM model (second matrix).

Hola, amiguísima 👽