edl (version 1.1)

RWlearning: Function implementing the Rescorla-Wagner learning.

Description

Function implementing the Rescorla-Wagner learning.

Usage

RWlearning(
  data,
  wm = NULL,
  eta = 0.01,
  lambda = 1,
  alpha = 0.1,
  beta1 = 0.1,
  beta2 = 0.1,
  progress = TRUE,
  ...
)

Arguments

data

A data frame with columns Cues and Outcomes.

wm

A weightmatrix of class matrix, or a list with weight matrices. If not provided a new weightmatrix is returned. Note that the cues and outcomes do not necessarily need to be available as cues and outcomes in the weightmatrix: if not present, they will be added.

eta

Learning parameter, typically set to 0.01. If eta is not specified and set to the value NULL, the values of alpha, beta1, and beta2 determine the learning rate. However, changing these settings is generally not very useful (see Hoppe et al, submitted).

lambda

Constant constraining the connection strength.

alpha

Learning parameter (scaling both positive and negative evidence adjustments), typically set to 0.1. Only used if eta=NULL.

beta1

Learning parameter for positive evidence, typically set to 0.1. Only used if eta=NULL.

beta2

Learning parameter for negative evidence, typically set to 0.1. Only used if eta=NULL.

progress

Logical: whether or not showing a progress bar (may slow down the process).

...

Parameters for the function getValues.

Value

A list with weightmatrices for each learning event.

See Also

RescorlaWagner, updateWeights

Examples

# NOT RUN {
# load example data:
data(dat)

# add obligatory columns Cues, Outcomes, and Frequency:
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- dat$Frequency1
head(dat)
dim(dat)

# now use createTrainingData to sample from the specified frequencies: 
train <- createTrainingData(dat)

# this training data can actually be used train network:
wm <- RWlearning(train)

# in R markdown or knitr reports the progress bar should be turned off:
wm <- RWlearning(train, progress=FALSE)

# Learning in steps is also possible:
wm <- RWlearning(train[1:20,])
getWM(wm)
length(wm)

train[21,c("Cues", "Outcomes")]
wm <- RWlearning(train[21,], wm=wm)
getWM(wm)
length(wm)

# }