NB_avg %>%
  mutate(cluster = dbscan::dbscan(cbind(accuracy,rts), eps = 0.5, minPts = 3)$cluster) %>%
  ggplot(aes(targets, accuracy, color=factor(cluster))) +
  ggtitle("targets (window = 8 trials)", "NOTE: each point is a single participant") +
  geom_point(alpha=0.3) +
  #geom_smooth(method='lm', se = F) +
  facet_wrap(~condition)


NB_avg %>%
  ggplot(aes(lures, accuracy, color=condition)) +
  ggtitle("lures (window = 8 trials)", "NOTE: each point is a single participant") +
  geom_point(alpha=0.3) +
  geom_smooth(method='lm', se = F)

NB_avg %>%
  ggplot(aes(skewness, accuracy, color=condition)) +
  ggtitle("skewness (window = 8 trials)", "NOTE: each point is a single participant") +
  geom_point(alpha=0.3) +
  geom_smooth(method='lm', se = F)

NB_avg %>%
  ggplot(aes(lumpiness, accuracy, color=condition)) +
  ggtitle("lumpiness", "NOTE: each point is a single participant") +
  geom_point(alpha=0.3) +
  geom_smooth(method='lm', se = F)

NB_avg %>%
  ggplot(aes(lumpiness, rts, color=condition)) +
  ggtitle("lumpiness (window = 8 trials)", "NOTE: each point is a single participant") +
  xlab("lumpiness") +
  ylab("Average RT") +
  geom_point(alpha=0.3) +
  geom_smooth(method='lm', se = F)

nback <- NB_modified

nback %>%
  mutate(block=as.factor(block)) %>%
  mutate(trial=as.factor(trial)) %>%
  mutate(condition=ifelse(condition=='2-back',2,3)) %>%
  #filter(condition=='3-back') %>%
  #mutate(correct=as.numeric(correct)) %>%
  inspect_cor(show_plot = T)

averaged_nback <- NB_avg

averaged_nback %>%
  mutate(condition=ifelse(condition=='2-back',2,3)) %>%
  inspect_cor(show_plot = T)