languageR (version 1.0)

english: English visual lexical decision and naming latencies

Description

This data set gives mean visual lexical decision latencies and word naming latencies to 2284 monomorphemic English nouns and verbs, averaged for old and young subjects, with various predictor variables.

Usage

data(english)

source

Balota, D.A., Cortese, M.J. and Pilotti, M. (1999) Visual lexical decision latencies for 2906 words. Available at http://www.artsci.wustl.edu/~dbalota/lexical_decision.html.

Spieler, D. H. and Balota, D. A. (1998) Naming latencies for 2820 words, http://www.artsci.wustl.edu/~dbalota/naming.html.

References

Balota, D., Cortese, M., Sergent-Marshall, S., Spieler, D. and Yap, M. (2004) Visual word recognition for single-syllable words, Journal of Experimental Psychology:General, 133, 283-316.

Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.

Examples

data(english)

# ---- orthogonalize orthographic consistency measures

items = english[english$AgeSubject == "young",]
items.pca = prcomp(items[ , c(18:27)], center = TRUE, scale = TRUE)
x = as.data.frame(items.pca$rotation[,1:4])
items$PC1 =  items.pca$x[,1]
items$PC2 =  items.pca$x[,2]
items$PC3 =  items.pca$x[,3]
items$PC4 =  items.pca$x[,4]
items2 = english[english$AgeSubject != "young", ]
items2$PC1 =  items.pca$x[,1]
items2$PC2 =  items.pca$x[,2]
items2$PC3 =  items.pca$x[,3]
items2$PC4 =  items.pca$x[,4]
english = rbind(items, items2) 

# ---- add Noun-Verb frequency ratio

english$NVratio = log(english$NounFrequency+1)-log(english$VerbFrequency+1)

# ---- build model with ols() from Design

library(Design)
english.dd = datadist(english)
options(datadist = 'english.dd')

english.ols = ols(RTlexdec ~ Voice + PC1 + MeanBigramFrequency + 
   rcs(WrittenFrequency, 5) + rcs(WrittenSpokenFrequencyRatio, 3) + 
   NVratio + WordCategory + AgeSubject +
   rcs(FamilySize, 3) + InflectionalEntropy + 
   NumberComplexSynsets + rcs(WrittenFrequency, 5) : AgeSubject,
   data = english, x = TRUE, y = TRUE)

# ---- plot partial effects

par(mfrow = c(4, 3), mar = c(4, 4, 1, 1), oma = rep(1, 4))
plot(english.ols, adj.subtitle = FALSE, ylim = c(6.4, 6.9), conf.int = FALSE)
par(mfrow = c(1, 1))

# ---- validate the model

validate(english.ols, bw = TRUE, B = 200)