R/edl.R

In edl: Toolbox for Error-Driven Learning Simulations with Two-Layer Networks

#' Toolbox for Error-Driven Learning Simulations with Two-Layer Networks
#'
#' The package 'edl' provides a set of functions that facilitate 
#' the evaluation, interpretation, and visualization of small error-driven 
#' learning simulations. 
#' 
#' Error-driven learning is based on the Widrow & Hoff (1960) learning 
#' rule and the Rescorla-Wagner's learning 
#' equations (Rescorla & Wagner, 1972), which are also at the core of 
#' Naive Discrimination Learning (Baayen et al, 2011). Error-driven can 
#' be used to explain bottom-up human learning 
#' (Hoppe et al, under revision), but is also at the 
#' core of artificial neural networks applications in the form of the 
#' Delta rule. 
#' This package provides a set of functions for building 
#' small-scale simulations to investigate the dynamics of error-driven 
#' learning and it's interaction with the structure of the input. For 
#' modeling error-driven learning using the Rescorla-Wagner equations 
#' the package 'ndl' (Baayen et al, 2011) is available on CRAN at 
#' \url{https://cran.r-project.org/package=ndl}. However, the package 
#' currently only allows tracing of a cue-outcome combination, rather 
#' than returning the learned networks.  
#' To fill this gap, we implemented a new package with 
#' a few functions that facilitate inspection of the networks for small 
#' error driven learning simulations. Note that our functions are not 
#' optimized for training large data sets (no parallel processing), as 
#' they are intended for small scale simulations and course examples. 
#' (Consider the python implementation \code{pyndl} 
#' \url{https://pyndl.readthedocs.io/en/latest/} for that purpose.) 
#'
#' @section Getting started:
#' \itemize{
#' \item \code{vignette("edl", package="edl")} - 
#' summarizes the core functions for training and visualization of results.
#' }
#' Also available online: \url{https://jacolienvanrij.com/Rpackages/edl/}.
#'
#' @section References:
#' Dorothée Hoppe, Petra Hendriks, Michael Ramscar, & Jacolien van Rij 
#' (2021): An exploration of error-driven learning in simple 
#' two-layer networks from a discriminative learning perspective. 
#' To appear in Behavior Research Methods.
#'
#' @author
#' Jacolien van Rij and Dorothée Hoppe, 
#' originally based on the package 'ndl'.
#'
#' Maintainer: Jacolien van Rij (\email{j.c.van.rij@rug.nl})
#'
#' University of Groningen, The Netherlands
#' @docType package
#' @name edl
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