DAAGxtras (version 0.8-4)

MaskedPriming: Masked Repetition Priming Data

Description

Words (words), preceded by an invisible identical or unrelated “prime”, or non-words, were flashed in front of subjects (id). The time taken by the subject to identify the letter combination as “not word” or “word” was then measured.

Usage

data(MaskedPriming)

Format

A data frame with 6381 correct responses to words on the following 10 variables.

subjects

a factor with levels 1 to 72

words

a factor with levels 1 to 192

e

the level of familiarity, a factor with levels 1 2 3

ct

a factor with levels HI HU LI LU. Here, HI = high freq, identical prime; HU = high freq, unrelated prime; LI = low, identical; LU = low, unrelated

f

the word frequency, a numeric vector with values -0.5 (High) and 0.5 (Low)

p

priming, a numeric vector with values -0.5 (Identical word) and 0.5 (Unrelated word)

rt

reaction time (milliseconds), a numeric vector

srt

reaction time (sec) = rt/1000, a numeric vector

lrt

loge(reaction time), a numeric vector

rrt

negative of speed of reaction = -1/srt, a numeric vector

Source

Kliegl et al (2008)

Details

This combines the datasets from Bodner and Masson (1997, Exp 1 and Exp 2a) and Kinoshita (2006, Exp 2).

References

Bodner, G.E., and Masson, M. E. J. 1997 Masked repetition priming of words and nonwords: Evidence for a nonlexical basis for priming. Journal of Memory and Language 37, 268-293.

Kinoshita, S. 2006 Additive and interactive effects of word frequency and masked repetition in the lexical decision task. Psychonomic Bulletin & Review 13, 668-673.

Kliegl, R., Masson, M. E. J. and Richter, E. M. 2008. A linear mixed-effects model analysis of masked repetition priming. Manuscript.

Examples

data(MaskedPriming)
str(MaskedPriming)
plot(MaskedPriming[sample(6381,100), 7:10])
## Not run: 
# library(lme4)
# cmat <- matrix(c(-1,  1,  0,
# 	             -1, -1,  2),  3,  2, 
# 	             dimnames=list(c("BM1", "BM2", "SK"), 
# 	                              c(".BM1-2", ".BM-SK")))
# m0 <- lmer(rrt ~ p*f*e + (1 | subjects) + (0 + p | subjects) +
#            (0 + f | subjects) + (1 | words), contrasts=list(e=cmat),
#            data=d)
# m1p <- lmer(rrt ~ p*f*e + (p | subjects) + (0+f | subjects) + (1 | words), 
#                  contrasts=list(e=cmat)
# m2 <- lmer(rrt ~ p*f*e + (p + f | subjects) + (1 | words), 
#                  contrasts=list(e=cmat), data=d)
# anova(m0, m1p, m2)
# ## End(Not run)