diff --git a/ccn2019.Rmd b/ccn2019.Rmd
index d88dba2..7301fc5 100644
--- a/ccn2019.Rmd
+++ b/ccn2019.Rmd
@@ -11,25 +11,32 @@
 library(ggplot2)
 library(tidyverse)
 library(GA)
+library(stringi)
+
+load('./data/CL2015.RData')
 ```
 
 ## Intro
 
-$\dots$
+### Problem: 
+ - local statistical properties of the n-back affect how we respond.
+ - local vs. global properties, what matters the most?
 
-Problem: 
- - local statistical properties of the n-back affect how we respond (RT and Accuracy)
- - local vs. global properties
+### By-products
+ - an agent to replicate behavioral data sets
+ - an online service to generate and evaluate n-back sequences
 
 ## Method
- - create history window or contiguous subsequences
- - calculate local T, L, S, U, RT_{mean}, Accuracy_{mean}
- - Model RT and Acc (response vars) in accordance with local properties (exp vars)
- - Cluster responses (or classify?)
+ - create history window (a.k.a, contiguous subsequences)
+ - calculate local T, L, S, U, $RT_{mean}$, $Accuracy_{mean}$ for each subsequence
+ - Model RT/Acc (response vars) with local properties (exp. vars)
+ - Cluster responses (or exp. vars?)
  - Investigate if extracted clusters are statistically different
+
+## Modeling
  - Create two models for local and global features as explanatory vars
  - Continue with modeling RT and Accuracy based upon local and global feats and compare them. Which model provides a better description of the recoreded RT and Accuracy vars? (model comparasion, model selection, etc)
- 
+
 ## Constraints
 
 - fixed number of targets
@@ -37,7 +44,6 @@
 - uniform distribution of choices
 - controlled local lumpiness
 
-## Constraint Satisfaction Problem
 
 ```{r}
 trials <- c('a','b','c','d','c','d','b','a','a','d','b','a','c','c','a','c')
@@ -100,41 +106,48 @@
 
 ```{r}
 
-load('./data/CL2015.RData')
-
-library(stringi)
+#NB %>%
+#  filter(participant=='P1') %>%
+#  group_by(block) %>%
+#  summarise(trials=n_distinct(trial))
 
 with_lures <- function(stim, stim_type, history) {
   # extend to 2-back/3-back
-  if (length(history)<3)
-    return(as.character(stim_type))
-  lapply(
+
+  res <- sapply(
     1:length(stim), 
     function(i) {
       ifelse(
         stim[i]==stri_sub(history[i],-2,-2) ||  stim[i]==stri_sub(history[i],-4,-4), 
         'lure', 
-        as.character(stim_type[i]))
+        as.character(stim_type[i])
+      )
     })
+  as.factor(res)
 }
 
 with_targets_ratio <- function(correct, history = c(), block_size=NA) {
   if (is.na(block_size)) block_size = str_length(history)
-  lapply(1:length(correct), function(i) {
+  sapply(1:length(correct), function(i) {
     0 #TODO
   })
 }
 
-with_lures_ratio <- function(history) {
-  lapply(1:length(history), function(i) 0)
+with_lures_ratio <- function(stimulus_type, history) {
+  res <- sapply(1:length(history), function(i) {
+    trials <- stimulus_type[(i-str_length(history[i])):i]
+    trials <- unlist(trials, use.names=FALSE)
+    length(trials[trials=="lure"])
+  })
+  res
 }
 
 with_skewness_score <- function(history) {
-  lapply(1:length(history), function(i) 0)
+  sapply(1:length(history), function(i) 0)
 }
 
 with_lumpiness_score <- function(history) {
-  lapply(1:length(history), function(i) 0)
+  sapply(1:length(history), function(i) 0)
 }
 
 with_history <- function(stims, max=8) {
@@ -145,20 +158,27 @@
   res
 }
 
-normalize_scores <- function(trials) {
-  sapply(trials, function(t) t)
+normalize_scores <- function(targets_ratio, lures_ratio, skewness, lumpiness) {
+  sapply(1:length(targets_ratio), function(i) 0)
 }
 
-NB %>%
+NB_modified <- NB %>%
   group_by(participant, condition, block) %>%
   mutate(history = with_history(stimulus)) %>%
   #mutate(stimulus_type = map_chr(.x=stimulus, stim_type=stimulus_type, history=history,.f=with_lures))
-  mutate(stimulus_type = with_lures(stimulus, stimulus_type, history)) %>%
+  mutate(stimulus_type_2 = with_lures(stimulus, stimulus_type, history)) %>%
   mutate(targets_ratio = with_targets_ratio(correct)) %>%
-  mutate(lures_ratio = with_lures_ratio(correct)) %>%
+  mutate(lures_ratio = with_lures_ratio(stimulus_type_2, history)) %>%
   mutate(skewness = with_skewness_score(history)) %>%
   mutate(lumpiness = with_lumpiness_score(history)) %>%
-  normalize_scores()
+  #normalize_scores(targets_ratio, lures_ratio, skewness, lumpiness) %>%
+  ungroup()
+
+# print
+NB_modified %>%
+  filter(participant=='P1', lures_ratio>0) %>%
+  View()
+  
 ```
 
 #TODO