# Winter Storm 2015 Modeling Workshop
# Word Learning Model
# ==explosion.R==
# defines a parallel word learner with optional cost
# or benefit to future learning from learned words
# explosion(TOTALWORDS = total words to be learned
# 			MEAN_T2L = mean number of time steps to learn a word
# 			STD_DV2L = standard deviation in word difficulty)
# =================================================================
# McMurray, B. (2007). Defusing the childhood vocabulary explosion. Science, 317(5838), 631-631.
# 
# code author: Rachael Richardson
# rachaelr@umd.edu

explosion <- function( TOTALWORDS = 10000, MEAN_T2L = 4000, STD_DV2L = 800 )	{

        # define x-axis with integer sequence
        x <- seq(0,TOTALWORDS-1)
        # standard normal distribution: most words are learned at MEAN_T2L

	# standard normal distribution: most words are learned at MEAN_T2L
	vocabulary_dist <- rnorm( x, MEAN_T2L, STD_DV2L )
	vocabulary <- sample( vocabulary_dist, TOTALWORDS )

	# plot gaussian distribution of sample words' required time steps for acquisition
	plot( seq( 0, TOTALWORDS-1 ), vocabulary, type='p', xlab='word index', ylab='learning threshold (time steps)', main='samples from a vocabulary of normally distributed difficulty' )

	# run the learner on the vocabulary
	wordsknown <- learner(vocabulary)

        # plot the results with a specified x-axis limit for neatness
        cutoff <- 6000
	plot( seq(0,length(wordsknown)-1), wordsknown, type='l', xlim = c(0,cutoff), xlab='time steps to acquisition', ylim = c(0,cutoff), ylab='total # of words known', main='cumulative vocabulary' )

        # associate word learning with decreasing word learning costs
	withbonus <- learner( vocabulary, offset = -0.05 )

        # add results to the plot in blue
	lines( seq(0,length(withbonus)-1), withbonus, col="blue" )

	# associate word learning with increasing word learning costs
	withpenalty <- learner( vocabulary, offset = 0.05 )

        # add results to the plot in red
	lines( seq(0,length(withpenalty)-1), withpenalty, col="red" )

}