The Rat Gambling Task (RGT) is a well validated rodent model of addiction-like behaviour. It is based on the Iowa Gambling Task (IGT) - a commonly used clinical assay to measure gambling-like behaviour. Rats choose between 4 options to earn as many sugar pellets as possible within 30 min. Each option is associated with different reward sizes, but also the probability and duration of the time out punishment. The task is designed such that the optimal strategy for earning sugar pellets is to favour the low risk, low reward options. Consistently selecting the high risk, high reward options results in longer and more frequent time-out penalties. Currently, there is no specialized graphical user interface (GUI) designed to extract, clean, and process RGT data. The installation and use of existing tools are challenging for users lacking coding experience and can be extremely time consuming. To address these issues, we developed a free and open source R-Shiny application called shinyRGT, as a GUI for RGT data extraction, analysis, and visualization. Clean and usable data can be easily extracted. As well, publication ready plots can be readily generated and annotated from user input. All generated tables can be downloaded as CSV files and generated graphs can be saved to local machines. shinyRGT is deployed at https://andrewcli.shinyapps.io/shinyRGT/ for online use. The repository is available at https://github.com/andr3wli/shinyRGT.
Bio: Andrew is an incoming psychology masters student at the University of British Columbia under the supervision of Prof. Jiaying Zhao. He is interested in information visualization, human-computer interaction, and cognitive science. As well, he develops and teaches R workshops to undergraduate students. In his free time, Andrew enjoys keeping up with the latest tech, golfing, and training for his first marathon.