Chapter 3 [ beh] expectation ~ cue
What is the purpose of this notebook?
Here, I plot the expectation ratings as a function of cue.
- Main model:
lmer(expect_rating ~ cue) - Main question: do expectations ratings differ as a function of cue type?
- If there is a main effect of cue on expectation ratings, does this cue effect differ depending on task type?
- IV: cue (high / low)
- DV: expectation rating
# parameters _____________________________________ # nolint
subject_varkey <- "src_subject_id"
iv <- "param_cue_type"; iv_keyword <- "cue"; dv <- "event02_expect_angle"; dv_keyword <- "expect"
xlab <- ""; ylim = c(0,180); ylab <- "ratings (degree)"
subject <- "subject"
exclude <- "sub-0001|sub-0003|sub-0004|sub-0005|sub-0025|sub-0999"
subjectwise_mean <- "mean_per_sub"; group_mean <- "mean_per_sub_norm_mean"; se <- "se"
color_scheme <- if (any(startsWith(dv_keyword, c("expect", "Expect")))) {
color_scheme <- c("#1B9E77", "#D95F02")
} else {
color_scheme <- c("#4575B4", "#D73027")
}
print_lmer_output <- FALSE
ggtitle_phrase <- " - Expectation Rating (degree)"
analysis_dir <- file.path(main_dir, "analysis", "mixedeffect", "model01_iv-cue_dv-expect", as.character(Sys.Date()))
dir.create(analysis_dir, showWarnings = FALSE, recursive = TRUE)
3.4 Individual difference analysis
Are cue effects (on expectation ratings) similar across tasks?
Using the random slopes of cue effects, here we plot them side by side with all three tasks of pain, cognitive, vicarious. As we can see, there is a high correlation across the random effects of cue across pain-cognitive, pain-vicarious, and cognitive-vicarious. These plots suggest a universal mechansim in the cue-expectancy effect, although some may critic that the cues were identical across tasks, thereby the cue effects are identical due to the stimuli itself, not necessarily a domain-general expectation process.
Note:
pain Warning: Removed 3 rows containing missing values (geom_point()).
vicarious Warning: Removed 1 rows containing missing values (geom_point()).
cognitive: Removed 3 rows containing missing values (geom_point()).