Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -3L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)

Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -4L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)

Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -5L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)

Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -6L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)

Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -7L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)

Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -8L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)

Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -9L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)

Data: data
Models:
fits[[c]]: log_rt ~ 0 + m.matrix[, -10L] + (1 + re1.stimulus1 + re1.frequency1 ||
fits[[c]]:     id) + (1 + re2.task1 || item)
full_model: log_rt ~ task * stimulus * density * frequency + (1 + re1.stimulus1 +
full_model:     re1.frequency1 || id) + (1 + re2.task1 || item)