Version:	1.1
Date:	2021-09-15
Title:	Toolbox for Error-Driven Learning Simulations with Two-Layer Networks
Maintainer:	Jacolien van Rij <j.c.van.rij@rug.nl>
Description:	Error-driven learning (based on the Widrow & Hoff (1960)https://isl.stanford.edu/~widrow/papers/c1960adaptiveswitching.pdf learning rule, and essentially the same as Rescorla-Wagner's learning equations (Rescorla & Wagner, 1972, ISBN: 0390718017), which are also at the core of Naive Discrimination Learning, (Baayen et al, 2011, <doi:10.1037/a0023851>) can be used to explain bottom-up human learning (Hoppe et al, <doi:10.31234/osf.io/py5kd>), 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, <doi:10.1037/a0023851>) 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.)
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyData:	true
Depends:	R (≥ 4.0.0), plotfunctions (≥ 1.4), data.table
VignetteBuilder:	knitr
Suggests:	knitr, rmarkdown
RoxygenNote:	7.1.0
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2021-09-16 09:48:01 UTC; jacolien
Author:	Jacolien van Rij [aut, cre], Dorothée Hoppe [aut]
Repository:	CRAN
Date/Publication:	2021-09-20 07:40:05 UTC

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

Value

A list with weightmatrices for each learning event.

Author(s)

Jacolien van Rij

See Also

RescorlaWagner, updateWeights

Examples

# 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)

Function implementing the Rescorla-Wagner learning.

Description

Function implementing the Rescorla-Wagner learning.

Usage

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

Arguments

Value

A weightmatrix.

Author(s)

Jacolien van Rij

See Also

RescorlaWagner, updateWeights

Examples

# 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:
wm1 <- RWlearningMatrix(train)
# comparison with a list:
wm2 <- RWlearning(train)
length(wm2)
getWM(wm2)

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

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

train[21,c("Cues", "Outcomes")]
wm <- RWlearningMatrix(train[21,], wm=wm)

Function implementing the Rescorla-Wagner learning equations without cue competition (for illustration purposes).

Description

Function implementing the Rescorla-Wagner learning equations without cue competition (for illustration purposes).

Usage

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

Arguments

Value

A list with weightmatrices for each learning event.

Author(s)

Dorothee Hoppe

See Also

RescorlaWagner, updateWeightsNoCueCompetition

Other functions for explaining error-driven learning: RWlearningNoOutcomeCompetition(), updateWeightsNoCueCompetition(), updateWeightsNoOutcomeCompetition()

Examples

# 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 <- RWlearningNoCueCompetition(train)

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

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

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

Function implementing the Rescorla-Wagner learning equetions without outcome competition (for illustration purposes).

Description

Function implementing the Rescorla-Wagner learning equetions without outcome competition (for illustration purposes).

Usage

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

Arguments

Value

A list with weightmatrices for each learning event.

Author(s)

Dorothee Hoppe

See Also

RescorlaWagner, updateWeightsNoOutcomeCompetition

Other functions for explaining error-driven learning: RWlearningNoCueCompetition(), updateWeightsNoCueCompetition(), updateWeightsNoOutcomeCompetition()

Examples

# 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 <- RWlearningNoOutcomeCompetition(train)

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

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

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

Calculate the change in activation for a specific cue or set of cues.

Description

Calculate the change in activation for a specific cue or set of cues for all outcomes (or a subset) in the weightmatrices.

Usage

activationsCueSet(
  wmlist,
  cueset,
  split = "_",
  select.outcomes = NULL,
  init.value = 0,
  normalize = FALSE
)

Arguments

Value

List of data frames. For each cueset defined in cueset, a dataframe of activation values is provided. These are returned as a list, with the cuesets as names.

Notes

The outcomes are selected based on the weightmatrices, and not necessarily all outcomes present in the training data.

Author(s)

Jacolien van Rij

See Also

getWeightsByCue, getWeightsByOutcome

Other functions for calculating activations: activationsEvents(), activationsMatrix(), activationsOutcomes(), getActivations()

Examples

# 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)

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

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

# Now we calculate the activations for all outcomes
# per event:
activations <- activationsCueSet(wm, cueset="BG_bicycle_red")
names(activations)
head(activations[[1]])

# plot:
a1 <- activations[[1]]
emptyPlot(nrow(a1), range(a1),
    xlab="Learning events", ylab="Activations",
    xmark=TRUE, ymark=TRUE, las=1)
for(i in 1:ncol(a1)){
    lines(a1[,i], col=i, lty=i)
}
legend_margin('topleft', legend=colnames(a1),
    col=1:ncol(a1), lty=1:ncol(a1),
    bty='n', cex=.75)

Calculate the activations for each learning event.

Description

Calculate the activations for each learning event. The values are returned as data frame or as a list of data frames.

Usage

activationsEvents(
  wmlist,
  data,
  split = "_",
  fun = NULL,
  return.list = FALSE,
  init.value = 0,
  normalize = FALSE
)

Arguments

Value

Vector or list of activation values (see return.list and fun for the specific conditions, and the examples below).

Author(s)

Jacolien van Rij

See Also

getWeightsByCue, getWeightsByOutcome

Other functions for calculating activations: activationsCueSet(), activationsMatrix(), activationsOutcomes(), getActivations()

Examples

# 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)

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

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

# Now we calculate the activations for each event:
train$Activation <- activationsEvents(wm, train)

# With multiple outcomes per event, it is better not 
# to directly assign to a new column, as a list will 
# return. See the example below:
dat$Outcomes <- paste(dat$Shape, dat$Color, sep="_")
dat$Cues <- paste("BG", dat$Category, sep="_")
dat$Frequency <- dat$Frequency1
head(dat)
train <- createTrainingData(dat)
wm <- RWlearning(train)
# This code will elicit a warning message:
## Not run: 
    act <- activationsEvents(wm, train)

## End(Not run)
# this code will not elicit a warning:
act <- activationsEvents(wm, train, return.list=TRUE)
head(act)
# to assign one single activation value to each event,
# we could instead apply a function, for example, by
# taking the max activation per event:
train$maxAct <- activationsEvents(wm, train, fun="max")

Calculate the activations for one or a set of cues.

Description

Calculate the activations for one or a set of cues. The values are returned as vector or data frame.

Usage

activationsMatrix(
  wm,
  cues,
  split = "_",
  select.outcomes = NULL,
  init.value = 0,
  normalize = FALSE
)

Arguments

Value

Vector or data frame.

Author(s)

Jacolien van Rij

See Also

getWeightsByCue, getWeightsByOutcome

Other functions for calculating activations: activationsCueSet(), activationsEvents(), activationsOutcomes(), getActivations()

Examples

# load example data:
data(dat)

# setup data:
newdat <- data.frame(Cues =paste("BG", dat$Shape, dat$Color, sep="_"),
   Outcomes  = dat$Category,
   Frequency = dat$Frequency2)
train  <- createTrainingData(newdat)
# learning:
wm     <- RWlearning(train)

# calculate activations for all outcomes:
mat    <- getWM(wm)
activationsMatrix(mat, cues="BG_tree_green")
# only accepts one set of cues - in this case all cues 
# are combined:
activationsMatrix(mat, cues=c("BG_tree", "BG_tree_brown"))
# ... which is the same as this:
activationsMatrix(mat, cues=c("BG", "BG", "tree", "tree", "brown"))
# now select one outcome:
activationsMatrix(mat, cues=c("BG", "tree"), select.outcomes="vehicle")
# cues/outcomes not in matrix:
activationsMatrix(mat, cues=c("na"), select.outcomes="new")

Calculate the activations for all outcomes in the data.

Description

Calculate the activations for all outcomes in the data per learning event. The activation values are returned as data frame.

Usage

activationsOutcomes(
  wmlist,
  data,
  split = "_",
  select.outcomes = NULL,
  init.value = 0,
  normalize = FALSE
)

Arguments

Value

Vector or list of activation values (see return.list and fun for the specific conditions, and the examples below).

Notes

The outcomes are selected based on the data with events, and not necessarily all outcomes present in the weightmatrices. For example, when the weightmatrices were first trained on another data set, some outcomes may be present in the weightmatrices but not in the current training data. To include these as well, the user can specify these extra outcomes with the argument select.outcomes.

Author(s)

Jacolien van Rij

See Also

getWeightsByCue, getWeightsByOutcome

Other functions for calculating activations: activationsCueSet(), activationsEvents(), activationsMatrix(), getActivations()

Examples

# 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)

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

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

# Now we calculate the activations for all outcomes
# per event:
activations <- activationsOutcomes(wm, train)
head(activations)

# Now with selection of outcomes (note that 'dog' does
# not occur as outcome in the data):
activations2 <- activationsOutcomes(wm, train, 
    select.outcomes = c("plant", "vehicle", "dog"))
head(activations2)
tail(activations2)

Remove empty cues and/or outcomes.

Description

Remove empty cues and/or outcomes.

Usage

check(data, rm = TRUE)

Arguments

Details

When rm=FALSE the function returns a code for each row of the data frame indicating whether an empty cue or outcome was detected. The function may return the following values:

0

No empty cues and outcomes were detected in this row.

1

Empty cue(s) but not empty outcomes were detected in this row.

2

Empty outcome(s) but not empty cues were detected in this row.

3

Empty cue(s) AND empty outcome(s) were detected in this row.

Value

data frame or numeric vector (see details)

Author(s)

Jacolien van Rij

Examples

test1 <- c("a_b", "a__b", "_a_b", "a_b_", "_a__b_", "___")
## Not run: 
    # this returns an error:
    check(test1)

## End(Not run)
# data frame with cues and outcomes:
(dat <- data.frame(Cues=test1, Outcomes=sample(test1), stringsAsFactors=TRUE))
# remove empty:
check(dat)
# only indicating which rows contain empty cues/outcomes:
(test <- check(dat, rm=FALSE))
# check empty cues:
dat[test %in% c(1,3),]
# check empty outcomes:
dat[test %in% c(2,3),]

Check whether cues and outcomes exist in a weight matrix and optionally add.

Description

Check whether cues and outcomes exist in a weight matrix and optionally add.

Usage

checkWM(cues, outcomes, wm)

Arguments

Value

A weightmatrix (matrix)

Author(s)

Jacolien van Rij

Examples

data(dat)
# create training data:
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- 1
train <- createTrainingData(dat)
# train network:
wm <- RWlearning(train)
# inspect weight matrix:
wm[[1]]
# retrieve cues and outcomes from data:
c <- getCues(wm)
o <- getOutcomes(wm)
# add missing cues to initial weight matrix:
checkWM(c, o, wm=wm[[1]])

Create event training data from a frequency data frame.

Description

Create event training data from a frequency data frame.

Usage

createTrainingData(
  data,
  nruns = 1,
  random = TRUE,
  within.runs = FALSE,
  add.id = TRUE,
  check = TRUE
)

Arguments

Value

data frame

Author(s)

Jacolien van Rij

Examples

# 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)
head(train)
dim(train)
# the rows should be equal to the sum of frequencies in dat:
sum(dat$Frequency)

# this training data can actually be used train network:
wm <- RWlearning(train)
# inspect weight matrix:
wm[[1]]

# retrieve cues and outcomes from data:
c <- getCues(wm)
o <- getOutcomes(wm)
# add missing cues to initial weight matrix:
checkWM(c, o, wm=wm[[1]])

# -------------------
# additional possibility for  
# simulating experimental designs:
# -------------------
dat$Frequency <- dat$Frequency2
train2 <- createTrainingData(dat, nruns=5)
head(train2)
# items are completely randomized, 
# and not equally distributed over the experiment:
train2$Run <- rep(1:5, each=(nrow(train2)/5))
table(train2$Run, train2$Item)
# in this way the items are randomized within each run: 
train3 <- createTrainingData(dat, nruns=5, within.runs=TRUE)
head(train3)
table(train3$Run, train3$Item)
# difference in learning (may take some time):
## Not run: 
wm2 <- RWlearning(train2)
plotCueWeights(wm2, cue="brown")	
wm3 <- RWlearning(train3)
plotCueWeights(wm3, cue="brown")	
plotOutcomeWeights(wm3, outcome="animal")	

## End(Not run)

Create empty weight matrix based on a set of cues and outcomes.

Description

Create empty weight matrix based on a set of cues and outcomes.

Usage

createWM(cues, outcomes, background = NULL, init.value = 0)

Arguments

Value

A weightmatrix (matrix)

Author(s)

Jacolien van Rij

See Also

link{RWlearning}

Examples

# 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)

# the function RWlearning uses createWM to construct a weight matrix: 
cues <- getValues(dat$Cues, unique=TRUE)
outcomes <- getValues(dat$Outcomes, unique=TRUE)
createWM(cues=cues, outcomes=outcomes)
# add background cue:
createWM(cues=cues, outcomes=outcomes, background=TRUE)

Create a 'cue window', for overlapping or continuous cues.

Description

Create a 'cue window', for overlapping or continuous cues.

Usage

cueWindow(
  x,
  n = 1,
  step = 1,
  weights = NULL,
  min = 1,
  max = 100,
  round.values = TRUE,
  split = "_",
  premark = "",
  postmark = "",
  as.numeric = FALSE,
  dec = NULL
)

Arguments

Value

A vector of strings (default), or a list with vectors of numbers.

Author(s)

Jacolien van Rij

Examples

# generate random sample of cues on continuum of 1-10,
# with sep=1:
set.seed(123)
cues <- round(runif(20, min=0.5, max=10.5),1)

# Note that cues will be converted to rounded numbers
# as round.values=TRUE. With cue window of 3:
cueWindow(cues, n=3, max=10)
# step of 0.5 increases number of neighboring cues:
cueWindow(cues, n=3, max=10, step=.5)
# cue window of 5:
cueWindow(cues, n=5, max=10)
# asymmetrical window:
cueWindow(cues, n=c(2,1), max=10, step=.5)

# non-uniform weights:
cueWindow(cues, n=5, max=10, weights=c(1,2,3,2,1)) 
cueWindow(cues, n=2.5, max=10, step=.5, weights=c(1,2,3,2,1)) 
# left cues have stronger weights:
cueWindow(cues, n=5, max=10, weights=c(3,3,2,1,1))
# adjust weights, so that cue itself is not included:
cueWindow(cues, n=c(2,1), max=10, weights=c(1,1,0,1))
# premarking:
cueWindow(cues, n=2, max=10, weights=c(1,1,1), premark="stimulus")
# numeric output:
cueWindow(cues, n=2, max=10, weights=c(1,2,1), as.numeric=TRUE)

Simulated learning data.

Description

Data set for illustrating discrimination learning.

Usage

dat

Format

A data frame with 36 rows and 5 variables:

Shape

Shape is the discriminative cue. 6 shapes: cat, rabbit, flower, tree, car, bicycle.

Color

Color is the nondiscriminative cue. 6 colors: brown, gray, white, yellow, red, blue.

Category

Three categories: animal, plant, vehicle.

Frequency1

Different frequency values assigned to the shapes, no difference between colors.

Frequency2

Different frequency values assigned to the color-shape combinations, no difference between categories.

Author(s)

Jacolien van Rij

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

• vignette("edl", package="edl") - summarizes the core functions for training and visualization of results.

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

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(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

Function to calculate the activations.

Description

Calculate the activations for all or specific outcomes on the basis of a set of cues. This function combines the various functions to calculate the activations.

Usage

getActivations(
  wmlist,
  data = NULL,
  cueset = NULL,
  split = "_",
  select.outcomes = NULL,
  init.value = 0,
  normalize = FALSE
)

Arguments

Value

List: when data is provided, a list is returned with the outcome activations for each learning event; when cueset is provided, a list is returned with data frames of outcome activations. See examples.

Author(s)

Jacolien van Rij

See Also

getWeightsByCue, getWeightsByOutcome

Other functions for calculating activations: activationsCueSet(), activationsEvents(), activationsMatrix(), activationsOutcomes()

Examples

# load example data:
data(dat)

# add obligatory columns Cues, Outcomes, and Frequency:
dat <- droplevels(dat[1:3,])
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- dat$Frequency1
head(dat)


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

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

# With this data we illustrate four different 
# ways to retrieve activation changes.

# Situation I: return activations for each event 
act1 <- getActivations(wm, data=train)
head(act1)
# plotting activations for each event doesn't provide very 
# useful info:
plot(act1$Activation, type='l', ylim=c(0,1), col=alpha(1),
    ylab='Activation')
# these lines may be more interpretable:
n <- which(act1$Outcomes=="animal")
lines(n, act1$Activation[n], col=alpha(2), lwd=2)
n <- which(act1$Outcomes=="plant")
lines(n, act1$Activation[n], col=alpha(3), lwd=2)

# Situation II: return activations for each events
# for all outcomes
act2 <- getActivations(wm, data=train, select.outcomes=TRUE)
head(act2)

plot(act2$plant, type='l', ylim=c(0,1), col=alpha(1),
    ylab='Activation')
n <- which(act2$Outcomes=="plant")
rug(n, side=1)
lines(n, act2$plant[n], lwd=2, col=alpha(3))
n <- which(act2$Outcomes!="plant")
lines(n, act2$plant[n], lwd=2, col=alpha(2))
legend('topright', 
    legend=c("all events", "outcome present", "outcome absent"),
	   col=c(1,alpha(3),alpha(2)), lwd=c(1,2,2),
    bty='n')

# Situation III: return activations for specific cuesets
# for all outcomes
act3 <- getActivations(wm, cueset=c("BG_cat_brown", "BG_flower_brown"))
str(act3) 

a31 <- act3[["BG_flower_brown"]] # or act3[[1]]
plot(a31$plant, type='l', ylim=c(0,1), col=alpha(1),
    main="BG_flower_brown", ylab='Activation')
lines(a31$animal,col=2)
rug(which(train$Cues == "BG_flower_brown"), side=1)
legend('topright', 
    legend=c("plant", "animal"),
	   col=c(1,2), lwd=1, bty='n')

# Situation IV: return activations for a static weight matrix
# Note: only with cueset
(final <- getWM(wm))
act4 <- getActivations(final, cueset=unique(train$Cues))
act4

Extract cues from list of weightmatrices.

Description

Extract cues from list of weightmatrices.

Usage

getCues(wmlist, extra.check = FALSE)

Arguments

Value

Vector with cues.

Author(s)

Jacolien van Rij

See Also

getOutcomes, getValues

Examples

# load example data:
data(dat)
# prepare training data:
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- dat$Frequency1
train <- createTrainingData(dat)
# learning:
wm <- RWlearning(train)
# retrieve cues from wm list:
getCues(wm)
# or this version (which takes more time):
system.time({getCues(wm, extra.check=TRUE)})
system.time({getCues(wm)})

Retrieve the lambda values for all or specific outcomes for each learning event.

Description

For a given set of training data, the lambda values are returned for each or specific outcomes. The values are returned as data frame.

Usage

getLambda(data, lambda = 1, split = "_", select.outcomes = NULL)

Arguments

Value

Data frame.

Author(s)

Jacolien van Rij

Examples

# 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
head(dat)
dim(dat)
test <- getLambda(dat)
# only outcomes that do not occur in data results in 0:
test2 <- getLambda(dat, select.outcomes=c("a", "b", "C"))

Extract outcomes from list of weightmatrices.

Description

Extract outcomes from list of weightmatrices.

Usage

getOutcomes(wmlist, extra.check = FALSE)

Arguments

Value

Vector with outcomes.

Author(s)

Jacolien van Rij

See Also

getCues, getValues

Examples

# load example data:
data(dat)
# prepare training data:
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- dat$Frequency1
train <- createTrainingData(dat)
# learning:
wm <- RWlearning(train)
# retrieve cues from wm list:
getOutcomes(wm)
# or this version (which takes more time):
system.time({getOutcomes(wm, extra.check=TRUE)})
system.time({getOutcomes(wm)})

Retrieve the weight updates and their change for each learning event.

Description

For a given set of training data, the weight updating values are returned for each or specific outcomes. The values are returned as data frame.

Usage

getUpdate(
  wmlist,
  data,
  select.outcomes = NULL,
  split = "_",
  present.outcome = FALSE
)

Arguments

Value

Data frame.

Author(s)

Jacolien van Rij

Examples

# load example data:
data(dat)

# add obligatory columns Cues, Outcomes, and Frequency:
dat <- droplevels(dat[1:3,])
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- dat$Frequency1
head(dat)


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

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

# retrieve update values for all outcomes:
updates1 <- getUpdate(data=train, wmlist=wm)
head(updates1)

# retrieve update values for observed outcomes:
updates2 <- getUpdate(data=train, wmlist=wm, present.outcome=TRUE)
head(updates2)

# plot:
n <- which("animal" == train$Outcomes)
plot(n, updates2[n], type='l', 
    ylim=c(0,.1), 
    ylab="Weight updates", xlab="Learning event")

Retrieve all cues from a vector of text strings.

Description

Retrieve all cues from a vector of text strings.

Usage

getValues(text, split = "_", unique = FALSE, decreasing = FALSE)

Arguments

Value

A vector with strings

Author(s)

Jacolien van Rij

See Also

strsplit, sort, unique, getOutcomes, getCues

Examples

# load example data:
data(dat)
# prepare training data:
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- dat$Frequency1
train <- createTrainingData(dat)

# find all cues in trainingdata:
cues <- getValues(train$Cues)
table(cues)
# find all outcomes in data:
out <- getValues(train$Outcomes)
table(out)
# find (sorted) unique cues and outcomes:
getValues(dat$Cues, unique=TRUE)
getValues(dat$Outcomes, unique=TRUE)

Retrieve all cues from a vector of text strings.

Description

Retrieve all cues from a vector of text strings.

Usage

getWM(wmlist, event = NULL)

Arguments

Value

A matrix with connection weights between cues (rows) and outcomes.

Author(s)

Jacolien van Rij

See Also

RWlearning, getWeightsByCue, getWeightsByOutcome

Examples

# 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)

# final weight matrix:
getWM(wm)
# ... which is the same as:
wm[[length(wm)]]
# 25th learning event:
getWM(wm, event=25)
# ... which is the same as:
wm[[25]]

Extract the change of connection weights between a specific cue and all outcomes.

Description

Extract the change of connection weights between all cues and a specific outcome. The values are returned as data frame.

Usage

getWeightsByCue(wmlist, cue, select.outcomes = NULL, init.value = 0)

Arguments

Value

Data frame.

Author(s)

Jacolien van Rij

See Also

plotCueWeights, plotOutcomeWeights, getWeightsByOutcome

Examples

# 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)

# final weight matrix:
getWM(wm)

# Inspect the change in connection weights 
# for cue=car
cueweights <- getWeightsByCue(wm, cue='car')
head(cueweights)
emptyPlot(nrow(cueweights), c(-.5,1), h0=0,
    main="Cue='car'", ylab='connection weights', xlab='learning events')
lines(cueweights$vehicle)
lines(cueweights$plant, col='red', lty=4)
lines(cueweights$animal, col='red', lty=2)
legend_margin('topright', legend=c('animal', 'plant', 'vehicle'),
    col=c(2,2,1), lty=c(2,4,1), lwd=1, bty='n')

Extract the change of connection weights between all cues and a specific outcome.

Description

Extract the change of connection weights between all cues and a specific outcome. The values are returned as data frame.

Usage

getWeightsByOutcome(wmlist, outcome, select.cues = NULL, init.value = 0)

Arguments

Value

Data frame.

Author(s)

Jacolien van Rij

See Also

plotCueWeights, plotOutcomeWeights, getWeightsByCue

Examples

# 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)

# final weight matrix:
getWM(wm)

# Inspect the change in connection weights 
# for cue=car
outweights <- getWeightsByOutcome(wm, outcome='vehicle')
head(outweights)
emptyPlot(nrow(outweights), range(outweights), h0=0,
    main="Outcome='vehicle'", ylab='connection weights', xlab='learning events')
lines(outweights$BG)
lines(outweights$car, lty=4)
lines(outweights$bicycle, lty=2)
lines(outweights$cat, col=2)
lines(outweights$red, col='blue', lty=4)
lines(outweights$gray, col='blue', lty=2)
legend('bottomright', legend=c('BG', 'car', 'bicycle', 'cat', 'red', 'gray'),
    col=c(1,1,1,2,'blue', 'blue'), lty=c(1,4,2,1,4,2), lwd=1)

Function implementing the Luce choice rule.

Description

Function implementing the Luce choice rule.

Usage

luceChoice(value, all)

Arguments

Value

A value between [0,1]

Author(s)

Dorothee Hoppe

Examples

# 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)

# caculate activations of outcomes given the cue set blue_car
red_rabbit <- getActivations(getWM(wm), cueset = "red_rabbit")$red_rabbit

# caculate choice probability of outcomes given the cue set blue_car after 
# normalizing with rectified linear unit
luceChoice(red_rabbit["vehicle"], red_rabbit)
luceChoice(red_rabbit["plant"], red_rabbit)
luceChoice(red_rabbit["animal"], red_rabbit)

# note that when some activations are negative, this rule either should not be 
# applied, or negative values have to be corrected for, e.g., with applying a 
# rectified linear unit (relu)
blue_car <- getActivations(getWM(wm), cueset = "blue_car")$blue_car

## Not run: 
# this is should not be done without correction
luceChoice(blue_car["vehicle"], blue_car)
# use, e.g., function relu() on the raw values

## End(Not run)

Visualize the change of connection weights between a specific outcome and all cues.

Description

Visualize the activation or the change of activation per event.

Usage

plotActivations(
  wmlist,
  cueset,
  split = "_",
  select.outcomes = NULL,
  init.value = 0,
  add.labels = TRUE,
  add = FALSE,
  ...
)

Arguments

Value

Optionally a list with label specifications is returned, which allows to plot your own labels. This may be helpful for very long labels, and for overlapping lines.

Author(s)

Jacolien van Rij

See Also

plotCueWeights, getWeightsByOutcome, getWeightsByCue

Examples

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

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

# plot connection weights for cue = 'cat':
plotActivations(wm, cueset="BG_cat_brown")
plotActivations(wm, cueset="BG_cat")

# plot your own labels:
labels <- plotActivations(wm, cues="BG_cat", add.labels=FALSE)
legend_margin('topright', legend=labels$labels, col=labels$col, 
    lwd=1, bty='n')

# change color and select outcomes:
out <- getValues(train$Outcomes, unique=TRUE)
out <- out[! out %in% "animal"]
labels <- plotActivations(wm, cues="BG_cat", 
    select.outcome=out, add.labels=FALSE, 
	   ylim=c(-.25,1),col=alpha(1))
lab2 <- plotActivations(wm, cues="BG_cat", add.labels=FALSE, 
    select.outcomes="animal", add=TRUE, col=2, lwd=2, xpd=TRUE)
legend('topright', legend=c("animal", labels$labels), 
    col=c(lab2$col, labels$col), lwd=c(lab2$lwd, labels$lwd), 
    lty=c(lab2$lty, labels$lty), bty="n")

Visualize the change of connection weights between a specific cue and all outcomes.

Description

Visualize the change of connection weights between a specific cue and all outcomes.

Usage

plotCueWeights(
  wmlist,
  cue,
  select.outcomes = NULL,
  init.value = 0,
  add.labels = TRUE,
  add = FALSE,
  ...
)

Arguments

Value

Optionally a list with label specifications is returned, which allows to plot your own labels. This may be helpful for very long labels, and for overlapping lines.

Author(s)

Jacolien van Rij

See Also

plotOutcomeWeights, getWeightsByOutcome, getWeightsByCue

Examples

# 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)

# plot connection weights for cue = 'car':
plotCueWeights(wm, cue="car")

# plot your own labels:
labels <- plotCueWeights(wm, cue="car", add.labels=FALSE)
legend_margin('topright', legend=labels$labels, col=labels$col, 
    lwd=1, bty='n')

# change color and select outcomes:
out <- getValues(train$Outcomes, unique=TRUE)
out <- out[out != "vehicle"]
labels <- plotCueWeights(wm, cue="car", add.labels=FALSE, 
    col=alphaPalette(c(1,2), f.seq=rep(.5,length(out))), 
    select.outcomes=out)
lab2 <- plotCueWeights(wm, cue="car", add.labels=FALSE, 
    select.outcomes="vehicle", add=TRUE, col=1, lwd=2)
legend_margin('topright', legend=c(labels$labels, "vehicle"), 
    col=c(labels$col, lab2$col), lwd=c(labels$lwd, lab2$lwd), 
    lty=c(labels$lty, lab2$lty))

Return strong weights.

Description

Return strong weights.

Usage

plotNetwork(
  wm,
  select.outcomes = NULL,
  select.cues = NULL,
  color = NULL,
  zlim = NULL,
  add.color.legend = TRUE,
  ...
)

Arguments

Value

No return value

Author(s)

Jacolien van Rij

Visualize the change of connection weights between a specific outcome and all cues.

Description

Visualize the change of connection weights between a specific outcome and all cues.

Usage

plotOutcomeWeights(
  wmlist,
  outcome,
  select.cues = NULL,
  init.value = 0,
  add.labels = TRUE,
  add = FALSE,
  ...
)

Arguments

Value

Optionally a list with label specifications is returned, which allows to plot your own labels. This may be helpful for very long labels, and for overlapping lines.

Author(s)

Jacolien van Rij

See Also

plotCueWeights, getWeightsByOutcome, getWeightsByCue

Examples

# 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)

# plot connection weights for cue = 'car':
plotOutcomeWeights(wm, outcome="vehicle")


# plot your own labels:
labels <- plotOutcomeWeights(wm, outcome="vehicle", add.labels=FALSE)
legend_margin('topright', legend=labels$labels, col=labels$col, 
    lwd=1, bty='n')

# change color and select outcomes:
out <- getValues(train$Cues, unique=TRUE)
out <- out[! out %in% c("car", "bicycle")]
labels <- plotOutcomeWeights(wm, outcome="vehicle", add.labels=FALSE, 
	   ylim=c(-.5,1),col=alpha(1), select.cues=out)
lab2 <- plotOutcomeWeights(wm, outcome="vehicle", add.labels=FALSE, 
    select.cues=c("car", "bicycle"), add=TRUE, col=2, lwd=2, xpd=TRUE)
legend_margin('topright', legend=c(labels$labels, c("car", "bicycle")), 
    col=c(labels$col, lab2$col), lwd=c(labels$lwd, lab2$lwd), 
    lty=c(labels$lty, lab2$lty))

Set value background cue.

Description

Set value background cue.

Usage

setBackground(value)

Arguments

Value

No return value

Function implementing the Rescorla-Wagner learning for a single learning event.

Description

Function implementing the Rescorla-Wagner learning for a single learning event. A set of cues and outcomes are provided, and a weightmatrix that needs to be updated.

Usage

updateWeights(
  cur.cues,
  cur.outcomes,
  wm = NULL,
  eta = 0.01,
  lambda = 1,
  alpha = 0.1,
  beta1 = 0.1,
  beta2 = 0.1
)

Arguments

Value

A weightmatrix (matrix)

Author(s)

Jacolien van Rij, based on RescorlaWagner

See Also

RescorlaWagner, RWlearning

Examples

# 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)
# retrieve trained network:
new <- getWM(wm)

train2 <- createTrainingData(dat)
updateWeights(getValues(train2$Cues[1]), 
    getValues(train2$Outcomes[1]), wm=new)

# comparison between eta and alpha, beta1, beta2:
check.cues <- c("BG", "car", "red")
new[check.cues,]
tmp1 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new)
tmp2 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new, eta=NULL)
tmp3 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new, beta1=0.2)
tmp4 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new, eta=NULL, beta1=0.2)
# these two should be the same:
tmp1[check.cues,]
tmp2[check.cues,]
# now we change beta2, but this does not change anything,
# because eta is being used:
tmp3[check.cues,]
# when we turn eta off, beta2 changes the values:
tmp4[check.cues,]

Function implementing the Rescorla-Wagner learning equations without cue competition for a single learning event.

Description

Function implementing the Rescorla-Wagner learning equations without cue competition (for illustration purposes) for a single learning event. A set of cues and outcomes are provided, and a weightmatrix that needs to be updated.

Usage

updateWeightsNoCueCompetition(
  cur.cues,
  cur.outcomes,
  wm = NULL,
  eta = 0.01,
  lambda = 1,
  alpha = 0.1,
  beta1 = 0.1,
  beta2 = 0.1
)

Arguments

Value

A weightmatrix (matrix)

Author(s)

Dorothee Hoppe, based on RescorlaWagner

See Also

RescorlaWagner, RWlearning

Other functions for explaining error-driven learning: RWlearningNoCueCompetition(), RWlearningNoOutcomeCompetition(), updateWeightsNoOutcomeCompetition()

Examples

# 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 <- RWlearningNoCueCompetition(train)
# retrieve trained network:
new <- getWM(wm)

train2 <- createTrainingData(dat)
updateWeightsNoCueCompetition(getValues(train2$Cues[1]), 
    getValues(train2$Outcomes[1]), wm=new)

# comparison between eta and alpha, beta1, beta2:
check.cues <- c("BG", "car", "red")
new[check.cues,]
tmp1 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new)
tmp2 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new, eta=NULL)
tmp3 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new, beta1=0.2)
tmp4 <- updateWeights(check.cues, 
    c("vehicle", "animal"), wm=new, eta=NULL, beta1=0.2)
# these two should be the same:
tmp1[check.cues,]
tmp2[check.cues,]
# now we change beta2, but this does not change anything,
# because eta is being used:
tmp3[check.cues,]
# when we turn eta off, beta2 changes the values:
tmp4[check.cues,]

Function implementing the Rescorla-Wagner learning equations without outcome competition (for illustration purposes) for a single learning event.

Description

Function implementing the Rescorla-Wagner learning equations without outcome competition (for illustration purposes) for a single learning event. A set of cues and outcomes are provided, and a weightmatrix that needs to be updated.

Usage

updateWeightsNoOutcomeCompetition(
  cur.cues,
  cur.outcomes,
  wm = NULL,
  eta = 0.01,
  lambda = 1,
  alpha = 0.1,
  beta1 = 0.1,
  beta2 = 0.1
)

Arguments

Value

A weightmatrix (matrix)

Author(s)

Dorothee Hoppe, based on RescorlaWagner

See Also

RescorlaWagner, RWlearning

Other functions for explaining error-driven learning: RWlearningNoCueCompetition(), RWlearningNoOutcomeCompetition(), updateWeightsNoCueCompetition()

Examples

# 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 <- RWlearningNoOutcomeCompetition(train)
# retrieve trained network:
new <- getWM(wm)

train2 <- createTrainingData(dat)
updateWeightsNoOutcomeCompetition(getValues(train2$Cues[1]), 
    getValues(train2$Outcomes[1]), wm=new)

# comparison between eta and alpha, beta1, beta2:
check.cues <- c("BG", "car", "red")
new[check.cues,]
tmp1 <- updateWeightsNoOutcomeCompetition(check.cues, 
    c("vehicle", "animal"), wm=new)
tmp2 <- updateWeightsNoOutcomeCompetition(check.cues, 
    c("vehicle", "animal"), wm=new, eta=NULL)
tmp3 <- updateWeightsNoOutcomeCompetition(check.cues, 
    c("vehicle", "animal"), wm=new, beta1=0.2)
tmp4 <- updateWeightsNoOutcomeCompetition(check.cues, 
    c("vehicle", "animal"), wm=new, eta=NULL, beta1=0.2)
# these two should be the same:
tmp1[check.cues,]
tmp2[check.cues,]
# now we change beta2, but this does not change anything,
# because eta is being used:
tmp3[check.cues,]
# when we turn eta off, beta2 changes the values:
tmp4[check.cues,]