diff --git a/ccn2019.Rmd b/ccn2019.Rmd
index 51b0168..9080b91 100644
--- a/ccn2019.Rmd
+++ b/ccn2019.Rmd
@@ -1,20 +1,17 @@
 ---
-title: "Unbiased N-Back"
-output:
-  ioslides_presentation: default
-  slidy_presentation: default
+title: "Evaluating N-Back Sequences"
 ---
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = FALSE)
-library(ggplot2)
 library(tidyverse)
-library(GA)
+library(ggplot2)
 library(stringi)
-
-load('./data/CL2015.RData')
+library(GA)
 ```
 
+# Introduction
+
 ## Problem
  - local statistical properties of the n-back affect how we respond.
  - local vs. global properties, what matters the most?
@@ -52,7 +49,15 @@
  - 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)
 
 
-```{r}
+The N-Back data set from @cardoso-leite2015 contains all required parameters for this study including RT, accuracy, and stimuli.
+
+```{r, echo=F}
+load('./data/CL2015.RData')
+```
+
+
+
+```{r, include=F}
 trials <- c('a','b','c','d','c','d','b','a','a','d','b','a','c','c','a','c')
 min_len <-4
 max_len <-4
@@ -122,7 +127,7 @@
   sapply(
     1:length(stim), 
     function(i) {
-      switch(condition[i],
+      switch(as.character(condition[i]),
              "2-back" = {
                ifelse(
                  stim[i]==stri_sub(history[i],-2,-2) ||  stim[i]==stri_sub(history[i],-4,-4), 
@@ -165,7 +170,11 @@
 }
 
 with_skewness_score <- function(stimulus, history) {
-  sapply(1:length(history), function(i) 0)
+  sapply(1:length(history), function(i) {
+    trials <- stimulus[(i-str_length(history[i])):i]
+    trials <- unlist(trials, use.names=FALSE)
+    sum(sort(table(trials), decreasing = T)[1:2]) - 1
+  })
 }
 
 with_history <- function(stims, max=8) {