diff --git a/ccn2019.rev2.Rmd b/ccn2019.rev2.Rmd
index 3e8cfd9..ef37de0 100644
--- a/ccn2019.rev2.Rmd
+++ b/ccn2019.rev2.Rmd
@@ -18,6 +18,57 @@
     - Global:  No lures, large vocabulary -> pattern repeats implies a target
 
 
+## Formulating Generating the N-Back Sequences as a CSP instance
+
+$P=\langle V,D,C,W\rangle$
+
+$V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}$
+
+$D=\{\}$
+
+
+Constraints:
+
+$$
+\\
+
+x_n = N, W_n = 1 - |10 \times dnorm(x_n-N,sd=4)|
+
+\\\\
+
+x_t = T \times trials, W_t = 1 - |10\times dnorm(T\times trials-x_t,sd=4)|
+
+\\\\
+
+x_{tl} = {T \times w \over trials}, W_{tl} = 1 - |10\times dnorm(x_{tl} - {T \over trials} \times w,sd=4)|
+
+\\\\
+
+x_{l} = L \times trials
+\\\\
+
+x_{ll} = L \times w
+\\\\
+
+x_{v} = |V|
+\\
+
+x_{ul} = w
+\\\\
+
+x_{s} = {trials \over |V|}
+\\\\
+
+x_{sl} = max(1, {w \over |V|})
+\\\\
+
+x_{g} = {trials \over w}
+
+\\\\
+
+x_{vl} = min(|V|, w)
+$$
+
 ```{r libraries, message=FALSE, include=FALSE, paged.print=FALSE}
 library(ggplot2)
 library(tidyverse)
@@ -34,13 +85,6 @@
 ```
 
 
-$P=\{V,D,C,W\}$
-
-$V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}$
-
-$D=\{\}$
-
-
 
 ```{r history}
 

diff --git a/ccn2019.rev2.Rmd b/ccn2019.rev2.Rmd
index 3e8cfd9..ef37de0 100644
--- a/ccn2019.rev2.Rmd
+++ b/ccn2019.rev2.Rmd
@@ -18,6 +18,57 @@
     - Global:  No lures, large vocabulary -> pattern repeats implies a target
 
 
+## Formulating Generating the N-Back Sequences as a CSP instance
+
+$P=\langle V,D,C,W\rangle$
+
+$V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}$
+
+$D=\{\}$
+
+
+Constraints:
+
+$$
+\\
+
+x_n = N, W_n = 1 - |10 \times dnorm(x_n-N,sd=4)|
+
+\\\\
+
+x_t = T \times trials, W_t = 1 - |10\times dnorm(T\times trials-x_t,sd=4)|
+
+\\\\
+
+x_{tl} = {T \times w \over trials}, W_{tl} = 1 - |10\times dnorm(x_{tl} - {T \over trials} \times w,sd=4)|
+
+\\\\
+
+x_{l} = L \times trials
+\\\\
+
+x_{ll} = L \times w
+\\\\
+
+x_{v} = |V|
+\\
+
+x_{ul} = w
+\\\\
+
+x_{s} = {trials \over |V|}
+\\\\
+
+x_{sl} = max(1, {w \over |V|})
+\\\\
+
+x_{g} = {trials \over w}
+
+\\\\
+
+x_{vl} = min(|V|, w)
+$$
+
 ```{r libraries, message=FALSE, include=FALSE, paged.print=FALSE}
 library(ggplot2)
 library(tidyverse)
@@ -34,13 +85,6 @@
 ```
 
 
-$P=\{V,D,C,W\}$
-
-$V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}$
-
-$D=\{\}$
-
-
 
 ```{r history}
 
diff --git a/ccn2019_diagrams.R b/ccn2019_diagrams.R
new file mode 100644
index 0000000..7da6569
--- /dev/null
+++ b/ccn2019_diagrams.R
@@ -0,0 +1,51 @@
+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)
+

diff --git a/ccn2019.rev2.Rmd b/ccn2019.rev2.Rmd
index 3e8cfd9..ef37de0 100644
--- a/ccn2019.rev2.Rmd
+++ b/ccn2019.rev2.Rmd
@@ -18,6 +18,57 @@
     - Global:  No lures, large vocabulary -> pattern repeats implies a target
 
 
+## Formulating Generating the N-Back Sequences as a CSP instance
+
+$P=\langle V,D,C,W\rangle$
+
+$V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}$
+
+$D=\{\}$
+
+
+Constraints:
+
+$$
+\\
+
+x_n = N, W_n = 1 - |10 \times dnorm(x_n-N,sd=4)|
+
+\\\\
+
+x_t = T \times trials, W_t = 1 - |10\times dnorm(T\times trials-x_t,sd=4)|
+
+\\\\
+
+x_{tl} = {T \times w \over trials}, W_{tl} = 1 - |10\times dnorm(x_{tl} - {T \over trials} \times w,sd=4)|
+
+\\\\
+
+x_{l} = L \times trials
+\\\\
+
+x_{ll} = L \times w
+\\\\
+
+x_{v} = |V|
+\\
+
+x_{ul} = w
+\\\\
+
+x_{s} = {trials \over |V|}
+\\\\
+
+x_{sl} = max(1, {w \over |V|})
+\\\\
+
+x_{g} = {trials \over w}
+
+\\\\
+
+x_{vl} = min(|V|, w)
+$$
+
 ```{r libraries, message=FALSE, include=FALSE, paged.print=FALSE}
 library(ggplot2)
 library(tidyverse)
@@ -34,13 +85,6 @@
 ```
 
 
-$P=\{V,D,C,W\}$
-
-$V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}$
-
-$D=\{\}$
-
-
 
 ```{r history}
 
diff --git a/ccn2019_diagrams.R b/ccn2019_diagrams.R
new file mode 100644
index 0000000..7da6569
--- /dev/null
+++ b/ccn2019_diagrams.R
@@ -0,0 +1,51 @@
+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)
+
diff --git a/pls_playground.Rmd b/pls_playground.Rmd
index 22d877f..83c79a7 100644
--- a/pls_playground.Rmd
+++ b/pls_playground.Rmd
@@ -12,25 +12,43 @@
 library(pls)
 
 ## 1. load sample data
-drinks <- read.csv("http://wiki.q-researchsoftware.com/images/d/db/Stacked_colas.csv")
-#str(drinks)
+#data <- read.csv("http://wiki.q-researchsoftware.com/images/d/db/Stacked_colas.csv")
+
+rm(NB)
+load("./data/CL2015.RData")
+data <- NB
+str(data)
 
 ## 2. clean data (remove brand and URLID)
-drinks <- drinks %>% 
-  select(-URLID, -brand)
+data <- data %>% 
+  mutate(n=ifelse(condition=='2-back', 2, 3)) %>%
+  select(-condition,
+         -stimulus,
+         -block,
+         -trial)
+# %>%
+#   rename(
+#          ev.participant=participant,
+#          ev.n=n,
+#          ev.block=block,
+#          ev.stimulus_type=stimulus_type,
+#          rv.choice=choice,
+#          rv.rt=rt,
+#          rv.correct=correct
+#          )
 
 ## 3. use cross validatation to find the optimal number of dimensions
-pls.model = plsr(pref ~ ., data = drinks, validation = "CV")
+pls.model = plsr(rt ~ ., data = data, validation = "CV")
 
 ## 3.1. find the model with lowest cv error
 cv <- RMSEP(pls.model)
 best_dims <- which.min(cv$val[estimate = "adjCV", , ]) - 1
 
 ## 4. rebuild the model
-pls.model <- plsr(pref ~ ., data = drinks, ncomp = best_dims)
+pls.model <- plsr(rt ~ ., data = data, ncomp = best_dims)
 
 ## 5. Sort, and visualize top coefficients
 coefs <- coef(pls.model)
 
-barplot(tail(sort(coefs[,1,1])))
+barplot(sort(coefs[,1,1], decreasing = T)[1:4])
 ```
\ No newline at end of file