This R library implements the Instance-Based Learning model for experience-based decision-making (see Gonzalez & Dutt, 2011; Lejarraga, Dutt, & Gonzalez, 2012)
It contains two primary functions:
- ibl_simulation simulates data based on parameters about
- ibl_fitting estimates the best-fitting model parameters for experimental data.
Install the latest version with:
# install.packages("devtools")
devtools::install_github("dhagmann/iblr")To run a simulation, first define the model parameters, then run the simulation function.
# Options: outcomes and probabilities
ops1 <- c(250,
1)
ops2 <- c(500, 0, 100,
0.7, 0.2, 0.1)
# Collect all gambles in a list
gg <- grep("^ops", ls(), value=TRUE)
gambles <- lapply(1:length(gg), function(g) matrix(get(gg[g]),ncol=2))
ntrials <- 50
prevalues <- 550
# Run simulation
sim <- ibl_simulation(gambles,ntrials,prevalues)
# Plot proportion of times each of the two options is chosen
ibl_simulation_plot(sim)You can recover the initial parameters (with some noise) using the fitting function.