RescorlaWagner: Implementation of the Rescorla-Wagner equations.
In ndl: Naive Discriminative Learning

Description Usage Arguments Details Value Author(s) References See Also Examples

RescorlaWagner implements an iterative simulation based on the Rescorla-Wagner equations. Given a data frame specifying cues, outcomes, and frequencies, it calculates, for a given cue-outcome pair, the temporal sequence of developing weights.

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RescorlaWagner(cuesOutcomes, traceCue="h", traceOutcome="hand",
   nruns=1, random=TRUE, randomOrder = NA, alpha=0.1, lambda=1,
   beta1=0.1, beta2=0.1)

`cuesOutcomes`	A data frame specifying cues, outcomes, and frequencies of combinations of cues and outcomes. In the data frame, cues and outcomes should be character vectors.
`traceCue`	A character string specifying the cue to be traced over time.
`traceOutcome`	A character string specifying the outcome to be traced over time.
`nruns`	An integer specifying the number of times the data have to be presented for learning. The total number of learning trials is `nruns*sum(cuesOutcomes$Frequency)`.
`random`	A logical specifying whether the order of the learning trials for a given run should be randomly reordered. Can be set to `FALSE` in case all frequencies are 1, and the sequence of learning trials in `cuesOutcomes` is given by the order of the rows.
`randomOrder`	If not `NA`, a vector specifying the (usually random) order of the learning trials.
`alpha`	The salience of the trace cue.
`lambda`	The maximum level of associative strength possible.
`beta1`	The salience of the situation in which the outcome occurs.
`beta2`	The salience of the situation in which the outcome does not occur.

The equilibrium weights (Danks, 2003) are also estimated.

An object of the class "RescorlaWagner", being a list with the following components:

weightvector: A numeric vector with the weights for all nruns*sum(dat[,"Frequency"]) training trials.
equilibriumWeight: The weight of the cue-outcome link at equilibrium.
traceCue: A character string specifying the trace cue.
traceOutcome: A character string specifying the trace outcome.

R. H. Baayen and Antti Arppe

Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47 (2), 109-121.

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H., & Prokasy, W. F. (Eds.), Classical conditioning II: Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts.

orthoCoding, plot.RescorlaWagner, numbers

data(lexample)
lexample$Cues <- orthoCoding(lexample$Word, grams=1)
lexample.rw <- RescorlaWagner(lexample, nruns=25, 
   traceCue="h", traceOutcome="hand")
plot(lexample.rw)

data(numbers)
traceCues=c( "exactly1", "exactly2", "exactly3", "exactly4",
   "exactly5", "exactly6", "exactly7", "exactly10", "exactly15")
traceOutcomes=c("1", "2", "3", "4", "5", "6", "7", "10", "15")
ylimit=c(0,1)
par(mfrow=c(3,3),mar=c(4,4,1,1))
     
for(i in 1:length(traceCues)) {
   numbers.rw <- RescorlaWagner(numbers, nruns=1,
      traceCue=traceCues[i], traceOutcome=traceOutcomes[i])
    plot(numbers.rw, ylimit=ylimit)
    mtext(paste(traceCues[i], " - ", traceOutcomes[i], sep=""), 
       side=3, line=-1, cex=0.7)
  }
par(mfrow=c(1,1))