# Function implementing the Rescorla-Wagner learning equations without cue competition for a single learning event. 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. ```r updateWeightsNoCueCompetition( cur.cues, cur.outcomes, wm = NULL, eta = 0.01, lambda = 1, alpha = 0.1, beta1 = 0.1, beta2 = 0.1 ) ``` ## Arguments - `cur.cues`: A vector with cues. - `cur.outcomes`: A vector with outcomes. - `wm`: A weightmatrix of class matrix. 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. - `beta1`: Learning parameter for positive evidence, typically set to 0.1. - `beta2`: Learning parameter for negative evidence, typically set to 0.1. ## Returns A weightmatrix (matrix) ## Examples ```r # 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,] ``` ## See Also `RescorlaWagner`, `RWlearning` Other functions for explaining error-driven learning: `RWlearningNoCueCompetition()`, `RWlearningNoOutcomeCompetition()`, `updateWeightsNoOutcomeCompetition()` ## Author(s) Dorothee Hoppe, based on `RescorlaWagner`

updateWeightsNoCueCompetition function