numbers: Example data illustrating the Rescorla-Wagner equations as...

In ndl: Naive Discriminative Learning

Description

The data used in simulation 3 of Ramscar et al. (2011) on numerical cognition.

Usage

data(lexample)

Format

A data frame with 10 observations on the following 3 variables.

Cues

A character vector specifying cues for quantities, separated by underscores.

Frequency

The frequencies with which the numbers appear in the COCA corpus.

Outcomes

A character vector specifying numerical outcomes associated with the input quantities.

Details

The cues represent learning trials with objects of the same size, shape and color. The numeric cues represent the presence of at least one subset of the specified size. The cues exactlyn represent the presence of exactly n objects. We are indebted to Michael Ramscar to making this data set available for inclusion in the package.

References

Michael Ramscar, Melody Dye, Hanna Muenke Popick & Fiona O'Donnell-McCarthy (2011), The Right Words or Les Mots Justes? Why Changing the Way We Speak to Children Can Help Them Learn Numbers Faster. Manuscript, Department of Psychology, Stanford University.

Examples

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))

ndl documentation built on May 2, 2019, 10:28 a.m.