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


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Documentation for package ‘edl’ version 1.0

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activationsCueSet Calculate the change in activation for a specific cue or set of cues.
activationsEvents Calculate the activations for each learning event.
activationsMatrix Calculate the activations for one or a set of cues.
activationsOutcomes Calculate the activations for all outcomes in the data.
check Remove empty cues and/or outcomes.
checkWM Check whether cues and outcomes exist in a weight matrix and optionally add.
createTrainingData Create event training data from a frequency data frame.
createWM Create empty weight matrix based on a set of cues and outcomes.
cueWindow Create a 'cue window', for overlapping or continuous cues.
dat Simulated learning data.
edl Toolbox for Error-Driven Learning Simulations with Two-Layer Networks
getActivations Function to calculate the activations.
getCues Extract cues from list of weightmatrices.
getLambda Retrieve the lambda values for all or specific outcomes for each learning event.
getOutcomes Extract outcomes from list of weightmatrices.
getUpdate Retrieve the weight updates and their change for each learning event.
getValues Retrieve all cues from a vector of text strings.
getWeightsByCue Extract the change of connection weights between a specific cue and all outcomes.
getWeightsByOutcome Extract the change of connection weights between all cues and a specific outcome.
getWM Retrieve all cues from a vector of text strings.
luceChoice Function implementing the Luce choice rule.
plotActivations Visualize the change of connection weights between a specific outcome and all cues.
plotCueWeights Visualize the change of connection weights between a specific cue and all outcomes.
plotNetwork Return strong weights.
plotOutcomeWeights Visualize the change of connection weights between a specific outcome and all cues.
RWlearning Function implementing the Rescorla-Wagner learning.
RWlearningMatrix Function implementing the Rescorla-Wagner learning.
RWlearningNoCueCompetition Function implementing the Rescorla-Wagner learning equations without cue competition (for illustration purposes).
RWlearningNoOutcomeCompetition Function implementing the Rescorla-Wagner learning equetions without outcome competition (for illustration purposes).
setBackground Set value background cue.
updateWeights Function implementing the Rescorla-Wagner learning for a single learning event.
updateWeightsNoCueCompetition Function implementing the Rescorla-Wagner learning equations without cue competition for a single learning event.
updateWeightsNoOutcomeCompetition Function implementing the Rescorla-Wagner learning equations without outcome competition (for illustration purposes) for a single learning event.