A reproducibly simulated psycholinguistic lexical-decision experiment, in
which participants judge whether letter strings are real words. The design
is fully crossed and counterbalanced: 24 participants each respond to all 40
items once (960 trials). condition (related/unrelated priming) and
modality (visual/auditory) are both within-participant and within-item,
assigned by a Latin-square list rotation so that, across participants, every
item appears equally often in each condition and in each modality. Neither
factor is therefore collinear with the item random intercept, which makes
this a clean fit for crossed-random-effects models such as
lmer(RT ~ condition + modality + word_frequency + (1 | participant) + (1 | item)), and hence for optimizer_fixef_plot(), coefficient_plot()
and the distribution plots.
Format
A data frame with 960 rows and 7 variables:
- participant
Participant identifier (factor, 24 levels).
- item
Item identifier (factor, 40 levels).
- condition
Priming condition (factor):
relatedorunrelated.- modality
Presentation modality (factor):
visualorauditory.- word_frequency
Item-level word frequency on the Zipf scale.
- RT
Reaction time, in milliseconds.
- accuracy
Response accuracy: 1 correct, 0 incorrect.
Details
RT is generated log-normally from participant and item random intercepts
plus the fixed effects (unrelated priming about 35 ms slower; auditory a
touch slower; more frequent words faster), with a 250 ms floor; RT analyses
should use correct trials (accuracy == 1). accuracy is generated from
the exogenous predictors (word_frequency, condition, modality) and
random effects only - it is not derived from RT - so classification demos
built on it are not circular.
The data are synthetic, generated by data-raw/generate_datasets.R with a
fixed seed; they do not describe any real individuals.