Ever wondered how faces affect the choices we make? That's what drove my recent study. We wanted to see how people's faces, whether they seem trustworthy or not, influence our decisions.
Have you heard of the saying "a wolf in sheep's clothing"? It's about people who seem nice but might have hidden motives. Then there are those who look tough but are actually kind – a "sheep in wolf's clothing," if you will.
Some people might be a “wolf in sheep's clothing” , or people with bad intentions that seem good
Vs.
Other people might be “sheep in wolves clothing”, or people who may seem like they have bad intentions but they are actually good
We programmed a game called the Iowa Gambling Task. People had to pick cards from different decks, each with its own risks. We paired these decks with pictures of people with different expressions to see how it affected their choices.
Standard Iowa Gambling Task Online Card Decks.
After collecting all the data, we did some serious number-crunching. We used fancy stats to see if there was a connection between facial expressions and decision-making.
#Preprocessing the data
full_data <- read.csv(here::here("..","Final_Data","sigt_modeling_data.csv"))
data_count <- count(full_data, "subjID")
full_data <- full_data[!(full_data$subjID =='11002'),]
write.table(full_data, here::here("..","Final_Data","sigt_modeling_data.txt"), sep = "\\t", row.names = FALSE)
// MLM 1: Age Group x Condition x Block on Performance Score
// Full Model with Study_num as a covariate
meglm NetScore i.Condition i.Block i.Condition#i.Block || ID:, vce(robust)
predict NetScore_predicted
// Test Interactions
contrast i.Condition##i.Block
// Test Condition by Block
contrast rb3.Condition@Block, pveffects nowald mcompare(bonferroni)
contrast rb1.Condition@Block, pveffects nowald mcompare(bonferroni)
// Plot condition by blocks
margins, at(Block=(1,2,3,4) Condition=(1,2,3))
marginsplot
// Get means/SD/CI
sort Condition Block
summarize NetScore_predicted
ci means NetScore_predicted