Description Usage Format Details Source References Examples
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.
1 |
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
This combines the datasets from Bodner and Masson (1997, Exp 1 and Exp 2a) and Kinoshita (2006, Exp 2).
Kliegl et al (2008)
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | 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)
|
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