CERP Model

Source code for the Contextualized, Event-predictive, Resource-limited Processing model (CERP).

This repository contains the model implementation, simulation, analysis and plotting scripts.

Installation

The code requires Python 3 and the following packages:

• NumPy
• pandas
• SciPy
• Matplotlib
• Plotly
• Shiny for Python
• shinywidgets

Note: Shiny is not necessary unless you want to run the included Shiny app.

Main files

• model_core.py — core CERP model
• model_test_grid_search_cli.py — run the model using the command-line, with variable parameters
• model_test_grid_search.py — run the model directly in Python, with variable parameters
• model_test_acce_over_t_sim_grid_search.py — aCCE-over-time analyses
• plot_comparison.py — comparison with experimental results
• app.py — interactive Shiny application
• plotting.py, further_plots.py, utilities.py — supporting functions

Running the model

To view the command-line options:

python model_test_grid_search_cli.py --help

To run the model directly in Python, you can execute:

python model_test_grid_search.py

To run the interactive Shiny application, execute:

shiny run app.py

Selected model data and results are provided in 'Exp-Data/' and 'Exp-Results-search/'.

Experimental data

plot_comparison.py uses data from Experiment 2 of:

Lohmann, J., Belardinelli, A., & Butz, M. V. (2019). Hands Ahead in Mind and Motion: Active Inference in Peripersonal Hand Space. Vision, 3(2), 15. https://doi.org/10.3390/vision3020015

The original experimental data and scripts are publicly available from the associated OSF repository: https://osf.io/ap7xt

Download the files (particularly, RawVerbalRT.dat) and place them in: Data/experimental_Lohmannetal2019Exp2/

The experimental data are not redistributed in this repository.

Paper citation

To be added.