Open peer review!

Our manuscript “Detecting distortions of peripherally-presented letter stimuli under crowded conditions” (see here) has received an open peer review from Will Harrison. Thanks for your comments Will! They will be valuable in improving the manuscript in a future revision.

Hurrah for open science!*

* as suggested here, from now on “Open Science” will just be called “Science”, and everything else will be called “Closed Science”.

Quick link: QuestIntuition

My friend Daniel Saunders recently released a neat little graphical toolbox for Matlab called QuestIntuition that allows you to play around with the QUEST procedure for adaptive sampling of psychometric thresholds. This will be a good resource for students learning how QUEST works.

Some questions :

  1. What happens if the initial guess is way off? How many trials does QUEST need to recover?
  2. What happens if the assumed slope is way off?
  3. What happens if the upper asymptote of the psychometric function is lower than the threshold QUEST is trying to find (I saw this in a paper I reviewed once)? Does it still produce reasonable samples?

Check it out!


PsyUtils: my utility functions package

You probably have a bunch of functions that you use often in your workflow. You’ve probably put these into a common place that’s easy for you to find. Mine is a Python package I uncreatively called PsyUtils. Whenever I find I want to use a function across multiple projects, I try to put it here. This is a constant work in progress, and isn’t supported for use outside my students and I, but maybe you find something useful in it (or a bug; please do report it).

For example, it can give you a fixation cross for encouraging steady fixation (Thaler et al), a bunch of filters, (modified from Peter Bex’s matlab code), lets you do nifty things with Gabor filterbanks, and I most recently added some functions to work with psychophysical (bernoulli trial) data, that includes some handy wrappers for faceting psychometric function fits using Seaborn. I find this really useful to quickly explore experimental data, and maybe you will too.

Note again, that I do not support this package. I might break backwards compatibility any time (I’ll try to use semantic versioning appropriately), and many of the functions are not well tested. Enjoy!

Quick link: rstanarm and brms

I haven’t used R extensively for some years now, having switched to mainly using Python. With the recent release of rstanarm and brms, it looks like R is going to jump back into my software rotation for the foreseeable future.

Basically, these packages make some of the stuff I was doing by hand in Stan (Bayesian inference for Generalised Linear Mixed Models, GLMMs) a total breeze. Get on it!