Hierarchical Gaussian Filtering (HGF) is a novel and adaptive package for doing cognitive and behavioral modelling. With the HGF you can fit time series data fit participant-level individual parameters, measure group differences based on model-specific parameters or use the model for any time series with underlying change in uncertainty.

The HGF consists of a network of probabilistic nodes hierarchically structured. The hierarchy is determined by the coupling between nodes. A node (child node) in the network can inheret either its value or volatility sufficient statistics from a node higher in the hierarchy (a parent node).

The presentation of a new observation at the lower level of the hierarchy (i.e. the input node) trigger a recursuve update of the nodes belief throught the bottom-up propagation of precision-weigthed prediction error.

The HGF will be explained in more detail in the theory section of the documentation

It is also recommended to check out the ActionModels.jl pacakge for stronger intuition behind the use of agents and action models.

The last official release can be downloaded from Julia with "] add HierarchicalGaussianFiltering"

We provide a script for getting started with commonly used functions and use cases

Load packages

using HierarchicalGaussianFiltering
using ActionModels

premade_agent("help")

agent = premade_agent("hgf_binary_softmax")

get_states(agent)

get_parameters(agent)

Set a new parameter for initial precision of xprob and define some inputs

set_parameters!(agent, ("xprob", "initial_precision"), 0.9)
inputs = [1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0];
nothing #hide

actions = give_inputs!(agent, inputs)

using StatsPlots
using Plots
plot_trajectory(agent, ("u", "input_value"))
plot_trajectory!(agent, ("x", "prediction"))

Plot state trajectory of input value, action and prediction of x

plot_trajectory(agent, ("u", "input_value"))
plot_trajectory!(agent, "action")
plot_trajectory!(agent, ("x", "prediction"))

using Distributions
prior = Dict(("xprob", "volatility") => Normal(1, 0.5))

#Create model
model = create_model(agent, prior, inputs, actions;)

#Fit single chain with 10 iterations
fitted_model = fit_model(model; n_iterations = 10, n_chains = 1)

plot(model)

# plot_parameter_distribution(model, prior)

get_posteriors(model)