Question: Does association-type position predict accuracy?


Read in data

# tdcs data
d.all = read.csv("../data/labDataPlusNorms.csv") %>%
  rename(condition = electrode,
         association.num = mention) %>%
  select(1:4,7,9:19,21,22) %>%
  mutate(condition = fct_recode(condition, "sham" = "na"))

# turker accuracy data
d.bw = read.csv("../data/backALLfinal_withALLSimilaritiesPlusNorms.csv") %>%  
      select(cue, labSubj, subjCode, isRight)

Median split on lists by accuracy

Calculate the proportion of turkers for each list that correctly guesed the cue. We then assign that list a 1 if it was guessed correctly more often than the median, and 0 if less.

# add trial-wise accuracy column to tdcs data based on turker data
prop.correct = d.bw %>%
  group_by(cue, labSubj) %>% 
  summarize(prop.turk.correct = sum(isRight)/length(isRight))

median.correct = median(prop.correct$prop.turk.correct)

d.all.bw = left_join(d.all, prop.correct) %>%
  mutate(above.median = ifelse(prop.turk.correct > median.correct, 1, 0)) %>%
  mutate(above.median = as.factor(above.median)) %>%
  filter(!is.na(above.median))

Order distributions by word_type

Distributions of word types across association numbers, split by correctness.

cat.dists = d.all.bw %>%
  select(-hyponym, -hypernym, -meronymPart,
         -meronymSubstance, -taxonomic, -synonym) %>% 
  gather("category", "present", 6:12) %>%
  filter(present == 1, category != "metaphor", category != "holonym")

ggplot(cat.dists, aes(x = association.num, 
                      group = above.median, fill = above.median)) +
  geom_histogram(position = "dodge", binwidth =1)  +
  facet_grid(~category)