diff --git a/ccn2019.Rmd b/ccn2019.Rmd
index eeb95ce..d88dba2 100644
--- a/ccn2019.Rmd
+++ b/ccn2019.Rmd
@@ -18,9 +18,18 @@
 $\dots$
 
 Problem: 
+ - local statistical properties of the n-back affect how we respond (RT and Accuracy)
+ - local vs. global properties
 
 ## 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?)
+ - Investigate if extracted clusters are statistically different
+ - 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