edl {edl}R Documentation

Toolbox for Error-Driven Learning Simulations with Two-Layer Networks

Description

The package 'edl' provides a set of functions that facilitate the evaluation, interpretation, and visualization of small error-driven learning simulations.

Details

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 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 pyndl https://pyndl.readthedocs.io/en/latest/ for that purpose.)

Getting started

Also available online: https://jacolienvanrij.com/Rpackages/edl/.

References

Dorothée Hoppe, Petra Hendriks, Michael Ramscar, & Jacolien van Rij (under revision): An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective. Accepted with minor revisions.

Author(s)

Jacolien van Rij and Dorothée Hoppe, originally based on the package 'ndl'.

Maintainer: Jacolien van Rij (j.c.van.rij@rug.nl)

University of Groningen, The Netherlands


[Package edl version 1.0 Index]