Code, tools, talks

I routinely place research code and data in the public domain. These are spread across GitHub and the Open Science Framework. Code relating to specific papers can be found on the publications page. I’ve also placed research talks and other resources below.

Bayesian Cognitive Modelling

Bayesian analysis of delay discounting data

For people who don’t like coding

If you are a non-coder, I have produced a guide on how to run Bayesian analysis of delay discounting data using JASP. No coding required! Read about it here:

For people who can do basic coding

I produced a package, currently implemented in Matlab, which conducts hierarchical Bayesian inference on delay discounting data. This package allows you to generate posterior distributions of discounting parameters. 

Vincent, B. T. (2016). Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks. Behavior Research Methods. 48(4), 1608-1620. [project page | pdf | code]

Since then, I have moved on to conduct Bayesian scoring of delay discounting data in Python for a number of other papers, for example here, here, and here.

A tutorial on Bayesian models of perception

Vincent (2015) A tutorial on Bayesian models of Perception, Journal of Mathematical Psychology. [code]

Bayesian models of covert visual attention

Vincent (2015) Bayesian accounts of covert selective attention: A tutorial review, Attention, Perception, & Psychophysics. [code]

Delay discounting: interactive web apps

I have built a number of handy tools to interactively explore how discounting works. These are built using R and Shiny and the code is available here.

Bayesian statistics

Below is a list of notes and resources that I am slowly accumulating on Bayesian statistical methods.

Bayesian regression for censored and truncated data

Bayesian mediation analysis

Bayesian mediation analysis code in Python/PyMC3

Bayesian moderation analysis

Bayesian moderation analysis in Python/PyMC3

Bayesian piecewise/spline regression

Notebooks on piecewise and spline regression models in Julia

Energy efficient receptive fields

rf-mono-matlab: Matlab code to calculate receptive fields which balance a) encoding information from natural images, b) metabolic energy expense.