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.
A data frame with 4568 observations on the following variables.
numeric vector of log RT in visual lexical decision.
numeric vector of log RT in word naming.
numeric vector of subjective familiarity ratings.
a factor with 2284 words.
a factor with as levels the age group of the subject:
a factor with as levels the word categories
N (noun) and
numeric vector with log frequency in the CELEX lexical database.
numeric vector with the logged ratio of written frequency (CELEX) to spoken frequency (British National Corpus).
numeric vector with log morphological family size.
numeric vector with derivational entropy.
numeric vector with inflectional entropy.
numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed.
numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed as part of a compound.
numeric vector with length of the word in letters.
numeric vector with orthographic neighborhood density, defined as the number of lemmas in CELEX with the same length (in letters) at Hamming distance 1.
numeric vector with mean log bigram frequency.
numeric vector with log frequency of initial diphone.
numeric vector with type count of orthographic neighbors.
numeric vector with token count of orthographic neighbors.
numeric vector with type count of phonological neighbors.
numeric vector with token count of phonological neighbors.
numeric vector with type counts of consistent words.
numeric vector with token counts of consistent words.
numeric vector with type count of forward inconsistent words
numeric vector with token count of forward inconsistent words
numeric vector with type count of backward inconsistent words
numeric vector with token count of backward inconsistent words
numeric vector with the frequency of the word used as noun.
numeric vector with the frequency of the word used as verb.
factor specifying whether the initial phoneme of
the word is a consonant (
C) or a vowel (
factor specifying whether the initial phoneme
of the word is a continuant (
cont) or an obstruent (
factor specifying whether the initial phoneme
has a burst (
burst) or frication (
consonant-initial words, and for vowel-initial words whether the vowel is
factor indicating whether the initial phoneme is
numeric vector with the log-transformed frequency of the initial diphone given that it is syllable-initial.
numeric vector with the log-transformed frequency of the initial diphone given that it is word initial.
numeric vector with the proportion of subjects that accepted the item as a word in lexical decision.
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, available at http://www.artsci.wustl.edu/~dbalota/naming.html.
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.
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## Not run: 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 rms library(rms) 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 plot(Predict(english.ols)) # ---- validate the model validate(english.ols, bw = TRUE, B = 200) ## End(Not run)
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