diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
+1,nb_gm_004,2,18,1.5522487163543701,6,3,True,EACADCDCACBABFCBCA
+2,nb_gm_004,7,36,3.1199769973754883,12,6,False,ABEABFDABBAEBDAABFDBFDAFFACFCDECACBF
+3,nb_gm_004,2,37,3.655029773712158,12,6,False,BFDFEDEDCECDCABADBDFADAFEFBEBCACFAFEC
+4,nb_gm_004,4,39,3.4385950565338135,13,6,False,AFBDAFFDEACDCAECBAEBAFABCBAACFDECDFEDEF
+5,nb_gm_004,7,54,4.757416009902954,18,9,False,BFAECBDBFFACEDBBDACBEAEDAFDBADCAEFCAFCEADCEFDEDACFBFBD
+6,nb_gm_004,7,22,1.847884178161621,7,3,False,EDCBFAAEDBCFFAECBDCFDB
+7,nb_gm_004,4,32,2.5619711875915527,10,5,False,DBFEDBFCEDFAECABACACFEBDCEDBAFDB
+8,nb_gm_004,5,45,3.880707025527954,15,7,False,BCDEDBBDEECBDBFACDBEDADEDCAAFDEBAFBDADFEBAEFC
+9,nb_gm_004,7,18,1.5218820571899414,5,3,False,FEBCDADFEBACAFDECB
+10,nb_gm_004,5,43,3.712139129638672,14,7,False,AEFFCABEFFABDFAADBEACDCBDCECFBBEDCEDEACFBDA
+11,nb_gm_004,7,60,5.11740517616272,19,10,False,FDEDCEBFDDECEDFFDCCFEBFCBAFEDBBEAAEBFBAEACADBBCACFAADCABDFCE
+12,nb_gm_004,7,48,4.116453170776367,16,8,False,ADBADCCADFBDDCADCFDEFEACCDBFDCECFBEFBEFEABABEFCE
+13,nb_gm_004,4,17,1.3661417961120605,4,2,False,CBEACBBDCBFCDAEEF
+14,nb_gm_004,4,61,5.220706224441528,20,10,False,CEDACEDCACDBEBDDEAFAEAEFBDECFDEDBFBDCFABEFAEDEACBFABCFCBFFDCC
+15,nb_gm_004,3,52,4.4212260246276855,17,8,False,EBCDBCDEBFDBFBCEFCBFECDEADCEDAEFBDEBDBAABFDAFCADFAEE
+16,nb_gm_004,7,27,2.323706865310669,9,4,False,EACFDBBEAAFDDCEBCFFECABDCFA
+17,nb_gm_004,3,32,2.64604115486145,10,5,False,CBACEBCDBFCBCEBFCBACDDFCEFAFEAFA
+18,nb_gm_004,6,26,2.1442930698394775,7,4,False,ECBADFECBBAFEADDCFDFABCEFC
+19,nb_gm_004,2,44,3.7390267848968506,14,7,False,EDCECACAFADFDAEACECDBDCBCEFEAFFDAFADBDCBCFCB
+20,nb_gm_004,4,62,5.2222230434417725,20,10,False,EACFEACDFADCCAFDFAFCAADFADBFCABCDADBFBDBCFABFEDAFDADFAEDBACEBD
+21,nb_gm_004,5,61,5.658479928970337,20,10,False,BAEFCBBEFFBCCAFEBCEFABECBAEEFCDECFBCBAFFDBDFAFADAEFABDEADBEDD
+22,nb_gm_004,5,32,2.645524024963379,10,5,False,ECDBCEEDBBFEFBAFAEDACDAFBCADFCEA
+23,nb_gm_004,7,62,5.216619968414307,20,10,False,EADDFBDEAADFFCEEBDFADCDBAFEDFCADFFDBABDDFACAEACDABEAFCACBCEFCB
+24,nb_gm_004,3,24,2.0706608295440674,8,3,False,EBAEBFAEFDECBDCADBACFCDF
+25,nb_gm_004,3,36,3.1037821769714355,12,6,False,BCFEBFECFEFCDFBDEFDBCDEBFEDBECEDCEFD
+26,nb_gm_004,6,61,5.195503234863281,20,10,False,CADBEACADDBACEBDFBEFBBFCADBABCBDABFCBECBDFFEBBEDDEFFBDEBFEFDA
+27,nb_gm_004,6,11,0.8340439796447754,3,0,False,AEFBCDABFEC
+28,nb_gm_004,4,39,3.390317916870117,13,6,False,ACBFACBCFABEBAECEAEDBCADFDADAFFDECFBEFC
+29,nb_gm_004,2,56,4.782411098480225,18,9,False,BCDBDCDCFDFBCBEBFEFABFBECEAEDADBCDCEFEBFBEDACDCFEFBEBACA
+30,nb_gm_004,2,53,4.454513788223267,17,8,False,CEDEDBEBABDADECDCECDBCBDBCABADAFCDCFCEFFCECFEFADFDAEA
+31,nb_gm_004,5,41,3.454822063446045,13,6,False,BEFACBBFAAEBCBAADCDAFDECFEDFCADEAEBDFCEED
+32,nb_gm_004,2,53,4.557706117630005,17,8,False,BDEDEBDBEBAECEFEDFAFCFECACBABECEAEFDFCDDAEAFEFADFDBCB
+33,nb_gm_004,3,57,4.94400691986084,19,9,False,FCEFBEDFEDBFBEFBCEECAEDCFDAFADFEBCFBADBACDAEFCAFCFEACEDCA
+34,nb_gm_004,5,13,1.0586318969726562,4,2,False,FAEDBCFEDBACE
+35,nb_gm_004,3,43,3.702164888381958,14,7,False,ACEADCADFCDABEAAEBAFCBFDEFBDFECFBEDBFCBEDCE
+36,nb_gm_004,4,40,3.446906089782715,13,6,False,EDCAEDCBAEABCBAAEBCADBACBCACFDAECDEBEDCF
+37,nb_gm_004,7,60,5.721850156784058,20,10,False,FABAFDBFAABFDAFFCBCDBAFDBECFADDABFDAADBFADAFCDFBBACBCFDCACAB
+38,nb_gm_004,2,40,3.326483964920044,13,6,False,DEAEACAFCFDCDFEDEAFADFDBEBABFCBCACEBECBF
+39,nb_gm_004,5,45,3.9018988609313965,15,7,False,CABEACCBEFACDEBAECEDFAFEBEACFBCAECBDBDCCDFDBE
+40,nb_gm_004,3,18,1.55079984664917,6,3,False,EBADBAABFCAFCEDCBE
+41,nb_gm_004,6,34,2.884040355682373,11,5,False,ECBABFECBBAFEADBDACFDBEDBFCEFDDACA
+42,nb_gm_004,2,48,4.0855629444122314,14,7,False,AFEFAEAEFAFDBDCDBCBFEFCECDBDABACBCEDFDEBCACDABEF
+43,nb_gm_004,6,50,4.191677093505859,16,8,False,FDEFACFDEEFCEDFDECFDBADCBDCBDABFECEADABEBABACECBFA
+44,nb_gm_004,3,25,2.081367015838623,8,3,False,EBDEABEACBAFDCFCDFCFAEDBE
+45,nb_gm_004,5,15,1.2862391471862793,5,2,False,ABDCEAFBCEDFFDE
+46,nb_gm_004,4,19,1.6599581241607666,6,3,False,BFCABFFACBDABEDFAEC
+47,nb_gm_004,5,61,5.23445987701416,20,10,True,CEFEFCCFEECDFDADCFDEFCEFCFBFECABCEBCCFEEACAEFABCEADAFECDFABCA
+48,nb_gm_004,4,51,4.420213937759399,17,8,False,FDABFDDEFFDBFDEABDEDFDCEEACCEDDAEEDBEDFEBDCEDBFEABC
+49,nb_gm_004,2,61,5.241312026977539,20,10,True,FDEDCECEDCDEDBCDCECBFBEFDFEFCEDECDCEAECEFCBCACEAFACFCBCAECEBF
+50,nb_gm_004,7,53,4.433992147445679,17,8,False,CFABEFECFFBEFCECEBEDAEDCFADACECABEFCEDCDABFBDADBADBDF
+51,nb_gm_004,3,40,3.2851059436798096,13,6,False,BDFCDFCFDFBDABCACBFDBFADFBEFEAFBECDEEDAD
+52,nb_gm_004,6,19,1.5269551277160645,6,3,False,CBADEFCBAFDEEBAFCDC
+53,nb_gm_004,7,33,2.7817859649658203,11,5,False,FADAECBFAADECBDFEDCDBBAEFCBFECAFC
+54,nb_gm_004,2,60,5.081879138946533,20,10,False,ADFDFAFBAFAEACFAFEFBCBDCDABACBABCECFCEACAFACFDFBABEBDEFEDFDB
+55,nb_gm_004,3,13,1.0153288841247559,3,1,False,DECBAFCAFDBED
+56,nb_gm_004,7,52,4.270846843719482,17,8,False,ADBDCFCADDBCBCAADCEFCCAFEBFFEAFACBCEBEADBEFEDCDBEBFF
+57,nb_gm_004,5,34,2.718968152999878,11,5,False,BEDFEBBDFFDBAFDDCBFACDBEBCACEACEAF
+58,nb_gm_004,2,59,4.88099479675293,19,9,True,BDFDFADACDCBFBCFCECBACABEBCBACFCBABFAFBFECFBDBCABACAFBABCBE
+59,nb_gm_004,7,15,1.2424840927124023,5,2,False,CDFEBAACDFBEBAC
+60,nb_gm_004,4,45,3.7462897300720215,15,7,False,FBECFBDEFBCDCBEDACEAFECADEADAEBDFEABFDCCBDFCA
+61,nb_gm_004,7,49,3.994555950164795,16,8,False,BDECFEFBDDEFECBBDCCEBCEDCEFECBACFFAEEFCCBAEACFABF
+62,nb_gm_004,5,42,3.4888839721679688,13,7,False,CDFFBCEDFFCECFDCDEADBFDAABCBEABEAAEBECBAFD
+63,nb_gm_004,7,61,5.040170907974243,20,10,False,DEBFBCFDEEFBECDDEBCEFCFECFEEAFBCEFEBFAECAEAFEACFEDCAECDECCDAD
+64,nb_gm_004,7,43,3.566600799560547,14,7,False,CEDEAFBCEEDADBCCEAACFBFEAEBCDFCBEFBDFDABFDA
+65,nb_gm_004,5,48,3.976670980453491,16,8,False,BFDDFBBEDEFBEDAAFECDADBCBEDDFBBDFEABDBFFEDEAFFAE
+66,nb_gm_004,6,17,1.2704148292541504,5,2,False,DFBACEDFBCAEECDFA
+67,nb_gm_004,3,11,0.8034019470214844,2,1,False,ECAFDBADBCF
+68,nb_gm_004,7,43,3.4832749366760254,14,7,False,FAEBDFEFAAEDFECFADFCEDCABECCDBBFEEBDBCAFDBA
+69,nb_gm_004,2,12,1.0047342777252197,2,0,False,FCABEDEDFABC
+70,nb_gm_004,4,49,4.085228681564331,16,8,False,DCAADCCAFDCBDACDBADFBABDDEBBDFABEFEACBEEFBEFBFECD
+71,nb_gm_004,5,49,3.932910680770874,16,8,False,DFABEDDABBDACBDADCFDBCDFEBDCBEBEFCEACFBEFABAEFCAF
+72,nb_gm_004,4,12,0.9949767589569092,3,1,False,EAFDBCADBCFE
+73,nb_gm_004,5,43,3.502958059310913,14,7,False,EABBCEEDBDCEDBCBCDFCEDCFAEFECABFAEAFDAFCADF
+74,nb_gm_004,3,44,3.5608408451080322,13,7,False,EFCFECFEBCDBFEBFCEFABEAFCADACDFBDDEADABDEACB
+75,nb_gm_004,2,61,4.981963157653809,20,10,False,ABFBDFDFBFABAEFAFDFACADCECFCAFBFCBCFEFDFEDBDEBEDECACEAEDBDCBE
+76,nb_gm_004,2,61,5.044064044952393,20,10,False,DFBFCBCBEBDEDCBDBEAECEDCBCECAEABFBDFDEAEBABCFCAFDFAFBADAFDFEE
+77,nb_gm_004,3,26,2.024602174758911,8,4,False,FCAFBCFBEDFEDCADBCECABEABD
+78,nb_gm_004,6,16,1.2625012397766113,5,2,False,CDEBFACDEBAFCBDF
+79,nb_gm_004,2,14,1.071295976638794,4,2,False,CFBCBDBEAEDAFA
+80,nb_gm_004,5,33,2.739096164703369,11,5,False,ADFFEAAFCEFAFECECFCBEEACDEBBADCBD
+81,nb_gm_004,2,61,4.994161128997803,20,10,False,AFBABDBDEBEDECBCFBEBCBACAFEFCEDEBEABDBCDCAEABACBCFDFBDEDFEFAC
+82,nb_gm_004,2,24,2.010740041732788,7,3,False,DEAEFAFACFCBEBDBCDCEABFD
+83,nb_gm_004,7,10,0.7493958473205566,3,0,False,FACBEDBFAC
+84,nb_gm_004,2,63,5.357555150985718,21,10,False,ECFCFBCBEBDEAECEDCDEBEDBDCADAEACBABCECACDADBFBAFEFCECBFBEFECDCF
+85,nb_gm_004,2,44,3.593871831893921,14,7,False,BAEAFEFECEDCBCFCEFEBAEACDCFDFABCDBDFDBADABFB
+86,nb_gm_004,3,17,1.3206660747528076,5,2,False,FDCFECAFCBFDABEAC
+87,nb_gm_004,6,18,1.50052809715271,6,3,False,DCFEABDCFEBADACFBE
+88,nb_gm_004,4,29,2.3075549602508545,8,4,False,FACEFAABFFDBCEDCDEDBCBAECEBDA
+89,nb_gm_004,3,11,0.8077938556671143,2,1,False,ADEBCFECFDA
+90,nb_gm_004,3,55,4.561264276504517,18,9,True,BACBCADCAEDACBADCABCEACDBCABDCFDBCEBFECBACBEDCADEACDFCB
+91,nb_gm_004,3,62,5.1214518547058105,20,10,False,DFEBDECDEEDCADEABDAFEAEFDCFFCAACBABCDBEBDEBFACAFCFBCBFDBEFCAFC
+92,nb_gm_004,7,63,5.354221820831299,21,10,False,ECDEACDECCEAABEECACDBBEDAEBDCEDEBABCBFEBCAADFFBAECDDFEADFCEFCAF
+93,nb_gm_004,2,42,3.4913809299468994,13,7,False,FEAEBABADACDEDCDBCBFCFDEDAFAEFBFCBCEBEDFAC
+94,nb_gm_004,4,58,4.802886962890625,19,9,False,ABDCABDACADBFBDBEFDFCEDCAFDCFAECAADEEABDEFDFEFCBBECEBDFCBF
+95,nb_gm_004,6,14,1.061150074005127,4,2,False,ABCEDFABCFEDDB
+96,nb_gm_004,4,41,3.3158693313598633,13,6,False,DEFADEFEADFAABEFAECFEACEADCEDBDABBACABECE
+97,nb_gm_004,4,24,2.0305070877075195,8,4,False,DEBCDEFBDEAFCDABCAEFCBAF
+98,nb_gm_004,6,35,2.8204848766326904,10,5,False,FDCEFBFDCCEBDECDCBAEDFCAFACBDAEABEB
+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
+1,nb_gm_004,2,18,1.5522487163543701,6,3,True,EACADCDCACBABFCBCA
+2,nb_gm_004,7,36,3.1199769973754883,12,6,False,ABEABFDABBAEBDAABFDBFDAFFACFCDECACBF
+3,nb_gm_004,2,37,3.655029773712158,12,6,False,BFDFEDEDCECDCABADBDFADAFEFBEBCACFAFEC
+4,nb_gm_004,4,39,3.4385950565338135,13,6,False,AFBDAFFDEACDCAECBAEBAFABCBAACFDECDFEDEF
+5,nb_gm_004,7,54,4.757416009902954,18,9,False,BFAECBDBFFACEDBBDACBEAEDAFDBADCAEFCAFCEADCEFDEDACFBFBD
+6,nb_gm_004,7,22,1.847884178161621,7,3,False,EDCBFAAEDBCFFAECBDCFDB
+7,nb_gm_004,4,32,2.5619711875915527,10,5,False,DBFEDBFCEDFAECABACACFEBDCEDBAFDB
+8,nb_gm_004,5,45,3.880707025527954,15,7,False,BCDEDBBDEECBDBFACDBEDADEDCAAFDEBAFBDADFEBAEFC
+9,nb_gm_004,7,18,1.5218820571899414,5,3,False,FEBCDADFEBACAFDECB
+10,nb_gm_004,5,43,3.712139129638672,14,7,False,AEFFCABEFFABDFAADBEACDCBDCECFBBEDCEDEACFBDA
+11,nb_gm_004,7,60,5.11740517616272,19,10,False,FDEDCEBFDDECEDFFDCCFEBFCBAFEDBBEAAEBFBAEACADBBCACFAADCABDFCE
+12,nb_gm_004,7,48,4.116453170776367,16,8,False,ADBADCCADFBDDCADCFDEFEACCDBFDCECFBEFBEFEABABEFCE
+13,nb_gm_004,4,17,1.3661417961120605,4,2,False,CBEACBBDCBFCDAEEF
+14,nb_gm_004,4,61,5.220706224441528,20,10,False,CEDACEDCACDBEBDDEAFAEAEFBDECFDEDBFBDCFABEFAEDEACBFABCFCBFFDCC
+15,nb_gm_004,3,52,4.4212260246276855,17,8,False,EBCDBCDEBFDBFBCEFCBFECDEADCEDAEFBDEBDBAABFDAFCADFAEE
+16,nb_gm_004,7,27,2.323706865310669,9,4,False,EACFDBBEAAFDDCEBCFFECABDCFA
+17,nb_gm_004,3,32,2.64604115486145,10,5,False,CBACEBCDBFCBCEBFCBACDDFCEFAFEAFA
+18,nb_gm_004,6,26,2.1442930698394775,7,4,False,ECBADFECBBAFEADDCFDFABCEFC
+19,nb_gm_004,2,44,3.7390267848968506,14,7,False,EDCECACAFADFDAEACECDBDCBCEFEAFFDAFADBDCBCFCB
+20,nb_gm_004,4,62,5.2222230434417725,20,10,False,EACFEACDFADCCAFDFAFCAADFADBFCABCDADBFBDBCFABFEDAFDADFAEDBACEBD
+21,nb_gm_004,5,61,5.658479928970337,20,10,False,BAEFCBBEFFBCCAFEBCEFABECBAEEFCDECFBCBAFFDBDFAFADAEFABDEADBEDD
+22,nb_gm_004,5,32,2.645524024963379,10,5,False,ECDBCEEDBBFEFBAFAEDACDAFBCADFCEA
+23,nb_gm_004,7,62,5.216619968414307,20,10,False,EADDFBDEAADFFCEEBDFADCDBAFEDFCADFFDBABDDFACAEACDABEAFCACBCEFCB
+24,nb_gm_004,3,24,2.0706608295440674,8,3,False,EBAEBFAEFDECBDCADBACFCDF
+25,nb_gm_004,3,36,3.1037821769714355,12,6,False,BCFEBFECFEFCDFBDEFDBCDEBFEDBECEDCEFD
+26,nb_gm_004,6,61,5.195503234863281,20,10,False,CADBEACADDBACEBDFBEFBBFCADBABCBDABFCBECBDFFEBBEDDEFFBDEBFEFDA
+27,nb_gm_004,6,11,0.8340439796447754,3,0,False,AEFBCDABFEC
+28,nb_gm_004,4,39,3.390317916870117,13,6,False,ACBFACBCFABEBAECEAEDBCADFDADAFFDECFBEFC
+29,nb_gm_004,2,56,4.782411098480225,18,9,False,BCDBDCDCFDFBCBEBFEFABFBECEAEDADBCDCEFEBFBEDACDCFEFBEBACA
+30,nb_gm_004,2,53,4.454513788223267,17,8,False,CEDEDBEBABDADECDCECDBCBDBCABADAFCDCFCEFFCECFEFADFDAEA
+31,nb_gm_004,5,41,3.454822063446045,13,6,False,BEFACBBFAAEBCBAADCDAFDECFEDFCADEAEBDFCEED
+32,nb_gm_004,2,53,4.557706117630005,17,8,False,BDEDEBDBEBAECEFEDFAFCFECACBABECEAEFDFCDDAEAFEFADFDBCB
+33,nb_gm_004,3,57,4.94400691986084,19,9,False,FCEFBEDFEDBFBEFBCEECAEDCFDAFADFEBCFBADBACDAEFCAFCFEACEDCA
+34,nb_gm_004,5,13,1.0586318969726562,4,2,False,FAEDBCFEDBACE
+35,nb_gm_004,3,43,3.702164888381958,14,7,False,ACEADCADFCDABEAAEBAFCBFDEFBDFECFBEDBFCBEDCE
+36,nb_gm_004,4,40,3.446906089782715,13,6,False,EDCAEDCBAEABCBAAEBCADBACBCACFDAECDEBEDCF
+37,nb_gm_004,7,60,5.721850156784058,20,10,False,FABAFDBFAABFDAFFCBCDBAFDBECFADDABFDAADBFADAFCDFBBACBCFDCACAB
+38,nb_gm_004,2,40,3.326483964920044,13,6,False,DEAEACAFCFDCDFEDEAFADFDBEBABFCBCACEBECBF
+39,nb_gm_004,5,45,3.9018988609313965,15,7,False,CABEACCBEFACDEBAECEDFAFEBEACFBCAECBDBDCCDFDBE
+40,nb_gm_004,3,18,1.55079984664917,6,3,False,EBADBAABFCAFCEDCBE
+41,nb_gm_004,6,34,2.884040355682373,11,5,False,ECBABFECBBAFEADBDACFDBEDBFCEFDDACA
+42,nb_gm_004,2,48,4.0855629444122314,14,7,False,AFEFAEAEFAFDBDCDBCBFEFCECDBDABACBCEDFDEBCACDABEF
+43,nb_gm_004,6,50,4.191677093505859,16,8,False,FDEFACFDEEFCEDFDECFDBADCBDCBDABFECEADABEBABACECBFA
+44,nb_gm_004,3,25,2.081367015838623,8,3,False,EBDEABEACBAFDCFCDFCFAEDBE
+45,nb_gm_004,5,15,1.2862391471862793,5,2,False,ABDCEAFBCEDFFDE
+46,nb_gm_004,4,19,1.6599581241607666,6,3,False,BFCABFFACBDABEDFAEC
+47,nb_gm_004,5,61,5.23445987701416,20,10,True,CEFEFCCFEECDFDADCFDEFCEFCFBFECABCEBCCFEEACAEFABCEADAFECDFABCA
+48,nb_gm_004,4,51,4.420213937759399,17,8,False,FDABFDDEFFDBFDEABDEDFDCEEACCEDDAEEDBEDFEBDCEDBFEABC
+49,nb_gm_004,2,61,5.241312026977539,20,10,True,FDEDCECEDCDEDBCDCECBFBEFDFEFCEDECDCEAECEFCBCACEAFACFCBCAECEBF
+50,nb_gm_004,7,53,4.433992147445679,17,8,False,CFABEFECFFBEFCECEBEDAEDCFADACECABEFCEDCDABFBDADBADBDF
+51,nb_gm_004,3,40,3.2851059436798096,13,6,False,BDFCDFCFDFBDABCACBFDBFADFBEFEAFBECDEEDAD
+52,nb_gm_004,6,19,1.5269551277160645,6,3,False,CBADEFCBAFDEEBAFCDC
+53,nb_gm_004,7,33,2.7817859649658203,11,5,False,FADAECBFAADECBDFEDCDBBAEFCBFECAFC
+54,nb_gm_004,2,60,5.081879138946533,20,10,False,ADFDFAFBAFAEACFAFEFBCBDCDABACBABCECFCEACAFACFDFBABEBDEFEDFDB
+55,nb_gm_004,3,13,1.0153288841247559,3,1,False,DECBAFCAFDBED
+56,nb_gm_004,7,52,4.270846843719482,17,8,False,ADBDCFCADDBCBCAADCEFCCAFEBFFEAFACBCEBEADBEFEDCDBEBFF
+57,nb_gm_004,5,34,2.718968152999878,11,5,False,BEDFEBBDFFDBAFDDCBFACDBEBCACEACEAF
+58,nb_gm_004,2,59,4.88099479675293,19,9,True,BDFDFADACDCBFBCFCECBACABEBCBACFCBABFAFBFECFBDBCABACAFBABCBE
+59,nb_gm_004,7,15,1.2424840927124023,5,2,False,CDFEBAACDFBEBAC
+60,nb_gm_004,4,45,3.7462897300720215,15,7,False,FBECFBDEFBCDCBEDACEAFECADEADAEBDFEABFDCCBDFCA
+61,nb_gm_004,7,49,3.994555950164795,16,8,False,BDECFEFBDDEFECBBDCCEBCEDCEFECBACFFAEEFCCBAEACFABF
+62,nb_gm_004,5,42,3.4888839721679688,13,7,False,CDFFBCEDFFCECFDCDEADBFDAABCBEABEAAEBECBAFD
+63,nb_gm_004,7,61,5.040170907974243,20,10,False,DEBFBCFDEEFBECDDEBCEFCFECFEEAFBCEFEBFAECAEAFEACFEDCAECDECCDAD
+64,nb_gm_004,7,43,3.566600799560547,14,7,False,CEDEAFBCEEDADBCCEAACFBFEAEBCDFCBEFBDFDABFDA
+65,nb_gm_004,5,48,3.976670980453491,16,8,False,BFDDFBBEDEFBEDAAFECDADBCBEDDFBBDFEABDBFFEDEAFFAE
+66,nb_gm_004,6,17,1.2704148292541504,5,2,False,DFBACEDFBCAEECDFA
+67,nb_gm_004,3,11,0.8034019470214844,2,1,False,ECAFDBADBCF
+68,nb_gm_004,7,43,3.4832749366760254,14,7,False,FAEBDFEFAAEDFECFADFCEDCABECCDBBFEEBDBCAFDBA
+69,nb_gm_004,2,12,1.0047342777252197,2,0,False,FCABEDEDFABC
+70,nb_gm_004,4,49,4.085228681564331,16,8,False,DCAADCCAFDCBDACDBADFBABDDEBBDFABEFEACBEEFBEFBFECD
+71,nb_gm_004,5,49,3.932910680770874,16,8,False,DFABEDDABBDACBDADCFDBCDFEBDCBEBEFCEACFBEFABAEFCAF
+72,nb_gm_004,4,12,0.9949767589569092,3,1,False,EAFDBCADBCFE
+73,nb_gm_004,5,43,3.502958059310913,14,7,False,EABBCEEDBDCEDBCBCDFCEDCFAEFECABFAEAFDAFCADF
+74,nb_gm_004,3,44,3.5608408451080322,13,7,False,EFCFECFEBCDBFEBFCEFABEAFCADACDFBDDEADABDEACB
+75,nb_gm_004,2,61,4.981963157653809,20,10,False,ABFBDFDFBFABAEFAFDFACADCECFCAFBFCBCFEFDFEDBDEBEDECACEAEDBDCBE
+76,nb_gm_004,2,61,5.044064044952393,20,10,False,DFBFCBCBEBDEDCBDBEAECEDCBCECAEABFBDFDEAEBABCFCAFDFAFBADAFDFEE
+77,nb_gm_004,3,26,2.024602174758911,8,4,False,FCAFBCFBEDFEDCADBCECABEABD
+78,nb_gm_004,6,16,1.2625012397766113,5,2,False,CDEBFACDEBAFCBDF
+79,nb_gm_004,2,14,1.071295976638794,4,2,False,CFBCBDBEAEDAFA
+80,nb_gm_004,5,33,2.739096164703369,11,5,False,ADFFEAAFCEFAFECECFCBEEACDEBBADCBD
+81,nb_gm_004,2,61,4.994161128997803,20,10,False,AFBABDBDEBEDECBCFBEBCBACAFEFCEDEBEABDBCDCAEABACBCFDFBDEDFEFAC
+82,nb_gm_004,2,24,2.010740041732788,7,3,False,DEAEFAFACFCBEBDBCDCEABFD
+83,nb_gm_004,7,10,0.7493958473205566,3,0,False,FACBEDBFAC
+84,nb_gm_004,2,63,5.357555150985718,21,10,False,ECFCFBCBEBDEAECEDCDEBEDBDCADAEACBABCECACDADBFBAFEFCECBFBEFECDCF
+85,nb_gm_004,2,44,3.593871831893921,14,7,False,BAEAFEFECEDCBCFCEFEBAEACDCFDFABCDBDFDBADABFB
+86,nb_gm_004,3,17,1.3206660747528076,5,2,False,FDCFECAFCBFDABEAC
+87,nb_gm_004,6,18,1.50052809715271,6,3,False,DCFEABDCFEBADACFBE
+88,nb_gm_004,4,29,2.3075549602508545,8,4,False,FACEFAABFFDBCEDCDEDBCBAECEBDA
+89,nb_gm_004,3,11,0.8077938556671143,2,1,False,ADEBCFECFDA
+90,nb_gm_004,3,55,4.561264276504517,18,9,True,BACBCADCAEDACBADCABCEACDBCABDCFDBCEBFECBACBEDCADEACDFCB
+91,nb_gm_004,3,62,5.1214518547058105,20,10,False,DFEBDECDEEDCADEABDAFEAEFDCFFCAACBABCDBEBDEBFACAFCFBCBFDBEFCAFC
+92,nb_gm_004,7,63,5.354221820831299,21,10,False,ECDEACDECCEAABEECACDBBEDAEBDCEDEBABCBFEBCAADFFBAECDDFEADFCEFCAF
+93,nb_gm_004,2,42,3.4913809299468994,13,7,False,FEAEBABADACDEDCDBCBFCFDEDAFAEFBFCBCEBEDFAC
+94,nb_gm_004,4,58,4.802886962890625,19,9,False,ABDCABDACADBFBDBEFDFCEDCAFDCFAECAADEEABDEFDFEFCBBECEBDFCBF
+95,nb_gm_004,6,14,1.061150074005127,4,2,False,ABCEDFABCFEDDB
+96,nb_gm_004,4,41,3.3158693313598633,13,6,False,DEFADEFEADFAABEFAECFEACEADCEDBDABBACABECE
+97,nb_gm_004,4,24,2.0305070877075195,8,4,False,DEBCDEFBDEAFCDABCAEFCBAF
+98,nb_gm_004,6,35,2.8204848766326904,10,5,False,FDCEFBFDCCEBDECDCBAEDFCAFACBDAEABEB
+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
+1,nb_gm_004,2,18,1.5522487163543701,6,3,True,EACADCDCACBABFCBCA
+2,nb_gm_004,7,36,3.1199769973754883,12,6,False,ABEABFDABBAEBDAABFDBFDAFFACFCDECACBF
+3,nb_gm_004,2,37,3.655029773712158,12,6,False,BFDFEDEDCECDCABADBDFADAFEFBEBCACFAFEC
+4,nb_gm_004,4,39,3.4385950565338135,13,6,False,AFBDAFFDEACDCAECBAEBAFABCBAACFDECDFEDEF
+5,nb_gm_004,7,54,4.757416009902954,18,9,False,BFAECBDBFFACEDBBDACBEAEDAFDBADCAEFCAFCEADCEFDEDACFBFBD
+6,nb_gm_004,7,22,1.847884178161621,7,3,False,EDCBFAAEDBCFFAECBDCFDB
+7,nb_gm_004,4,32,2.5619711875915527,10,5,False,DBFEDBFCEDFAECABACACFEBDCEDBAFDB
+8,nb_gm_004,5,45,3.880707025527954,15,7,False,BCDEDBBDEECBDBFACDBEDADEDCAAFDEBAFBDADFEBAEFC
+9,nb_gm_004,7,18,1.5218820571899414,5,3,False,FEBCDADFEBACAFDECB
+10,nb_gm_004,5,43,3.712139129638672,14,7,False,AEFFCABEFFABDFAADBEACDCBDCECFBBEDCEDEACFBDA
+11,nb_gm_004,7,60,5.11740517616272,19,10,False,FDEDCEBFDDECEDFFDCCFEBFCBAFEDBBEAAEBFBAEACADBBCACFAADCABDFCE
+12,nb_gm_004,7,48,4.116453170776367,16,8,False,ADBADCCADFBDDCADCFDEFEACCDBFDCECFBEFBEFEABABEFCE
+13,nb_gm_004,4,17,1.3661417961120605,4,2,False,CBEACBBDCBFCDAEEF
+14,nb_gm_004,4,61,5.220706224441528,20,10,False,CEDACEDCACDBEBDDEAFAEAEFBDECFDEDBFBDCFABEFAEDEACBFABCFCBFFDCC
+15,nb_gm_004,3,52,4.4212260246276855,17,8,False,EBCDBCDEBFDBFBCEFCBFECDEADCEDAEFBDEBDBAABFDAFCADFAEE
+16,nb_gm_004,7,27,2.323706865310669,9,4,False,EACFDBBEAAFDDCEBCFFECABDCFA
+17,nb_gm_004,3,32,2.64604115486145,10,5,False,CBACEBCDBFCBCEBFCBACDDFCEFAFEAFA
+18,nb_gm_004,6,26,2.1442930698394775,7,4,False,ECBADFECBBAFEADDCFDFABCEFC
+19,nb_gm_004,2,44,3.7390267848968506,14,7,False,EDCECACAFADFDAEACECDBDCBCEFEAFFDAFADBDCBCFCB
+20,nb_gm_004,4,62,5.2222230434417725,20,10,False,EACFEACDFADCCAFDFAFCAADFADBFCABCDADBFBDBCFABFEDAFDADFAEDBACEBD
+21,nb_gm_004,5,61,5.658479928970337,20,10,False,BAEFCBBEFFBCCAFEBCEFABECBAEEFCDECFBCBAFFDBDFAFADAEFABDEADBEDD
+22,nb_gm_004,5,32,2.645524024963379,10,5,False,ECDBCEEDBBFEFBAFAEDACDAFBCADFCEA
+23,nb_gm_004,7,62,5.216619968414307,20,10,False,EADDFBDEAADFFCEEBDFADCDBAFEDFCADFFDBABDDFACAEACDABEAFCACBCEFCB
+24,nb_gm_004,3,24,2.0706608295440674,8,3,False,EBAEBFAEFDECBDCADBACFCDF
+25,nb_gm_004,3,36,3.1037821769714355,12,6,False,BCFEBFECFEFCDFBDEFDBCDEBFEDBECEDCEFD
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+77,nb_gm_004,3,26,2.024602174758911,8,4,False,FCAFBCFBEDFEDCADBCECABEABD
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+79,nb_gm_004,2,14,1.071295976638794,4,2,False,CFBCBDBEAEDAFA
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+84,nb_gm_004,2,63,5.357555150985718,21,10,False,ECFCFBCBEBDEAECEDCDEBEDBDCADAEACBABCECACDADBFBAFEFCECBFBEFECDCF
+85,nb_gm_004,2,44,3.593871831893921,14,7,False,BAEAFEFECEDCBCFCEFEBAEACDCFDFABCDBDFDBADABFB
+86,nb_gm_004,3,17,1.3206660747528076,5,2,False,FDCFECAFCBFDABEAC
+87,nb_gm_004,6,18,1.50052809715271,6,3,False,DCFEABDCFEBADACFBE
+88,nb_gm_004,4,29,2.3075549602508545,8,4,False,FACEFAABFFDBCEDCDEDBCBAECEBDA
+89,nb_gm_004,3,11,0.8077938556671143,2,1,False,ADEBCFECFDA
+90,nb_gm_004,3,55,4.561264276504517,18,9,True,BACBCADCAEDACBADCABCEACDBCABDCFDBCEBFECBACBEDCADEACDFCB
+91,nb_gm_004,3,62,5.1214518547058105,20,10,False,DFEBDECDEEDCADEABDAFEAEFDCFFCAACBABCDBEBDEBFACAFCFBCBFDBEFCAFC
+92,nb_gm_004,7,63,5.354221820831299,21,10,False,ECDEACDECCEAABEECACDBBEDAEBDCEDEBABCBFEBCAADFFBAECDDFEADFCEFCAF
+93,nb_gm_004,2,42,3.4913809299468994,13,7,False,FEAEBABADACDEDCDBCBFCFDEDAFAEFBFCBCEBEDFAC
+94,nb_gm_004,4,58,4.802886962890625,19,9,False,ABDCABDACADBFBDBEFDFCEDCAFDCFAECAADEEABDEFDFEFCBBECEBDFCBF
+95,nb_gm_004,6,14,1.061150074005127,4,2,False,ABCEDFABCFEDDB
+96,nb_gm_004,4,41,3.3158693313598633,13,6,False,DEFADEFEADFAABEFAECFEACEADCEDBDABBACABECE
+97,nb_gm_004,4,24,2.0305070877075195,8,4,False,DEBCDEFBDEAFCDABCAEFCBAF
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+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_002_demo_sdt.py b/demo/nb_gm_002_demo_sdt.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo_sdt.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
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+8,nb_gm_004,5,45,3.880707025527954,15,7,False,BCDEDBBDEECBDBFACDBEDADEDCAAFDEBAFBDADFEBAEFC
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+10,nb_gm_004,5,43,3.712139129638672,14,7,False,AEFFCABEFFABDFAADBEACDCBDCECFBBEDCEDEACFBDA
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+24,nb_gm_004,3,24,2.0706608295440674,8,3,False,EBAEBFAEFDECBDCADBACFCDF
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+64,nb_gm_004,7,43,3.566600799560547,14,7,False,CEDEAFBCEEDADBCCEAACFBFEAEBCDFCBEFBDFDABFDA
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+67,nb_gm_004,3,11,0.8034019470214844,2,1,False,ECAFDBADBCF
+68,nb_gm_004,7,43,3.4832749366760254,14,7,False,FAEBDFEFAAEDFECFADFCEDCABECCDBBFEEBDBCAFDBA
+69,nb_gm_004,2,12,1.0047342777252197,2,0,False,FCABEDEDFABC
+70,nb_gm_004,4,49,4.085228681564331,16,8,False,DCAADCCAFDCBDACDBADFBABDDEBBDFABEFEACBEEFBEFBFECD
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+82,nb_gm_004,2,24,2.010740041732788,7,3,False,DEAEFAFACFCBEBDBCDCEABFD
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+84,nb_gm_004,2,63,5.357555150985718,21,10,False,ECFCFBCBEBDEAECEDCDEBEDBDCADAEACBABCECACDADBFBAFEFCECBFBEFECDCF
+85,nb_gm_004,2,44,3.593871831893921,14,7,False,BAEAFEFECEDCBCFCEFEBAEACDCFDFABCDBDFDBADABFB
+86,nb_gm_004,3,17,1.3206660747528076,5,2,False,FDCFECAFCBFDABEAC
+87,nb_gm_004,6,18,1.50052809715271,6,3,False,DCFEABDCFEBADACFBE
+88,nb_gm_004,4,29,2.3075549602508545,8,4,False,FACEFAABFFDBCEDCDEDBCBAECEBDA
+89,nb_gm_004,3,11,0.8077938556671143,2,1,False,ADEBCFECFDA
+90,nb_gm_004,3,55,4.561264276504517,18,9,True,BACBCADCAEDACBADCABCEACDBCABDCFDBCEBFECBACBEDCADEACDFCB
+91,nb_gm_004,3,62,5.1214518547058105,20,10,False,DFEBDECDEEDCADEABDAFEAEFDCFFCAACBABCDBEBDEBFACAFCFBCBFDBEFCAFC
+92,nb_gm_004,7,63,5.354221820831299,21,10,False,ECDEACDECCEAABEECACDBBEDAEBDCEDEBABCBFEBCAADFFBAECDDFEADFCEFCAF
+93,nb_gm_004,2,42,3.4913809299468994,13,7,False,FEAEBABADACDEDCDBCBFCFDEDAFAEFBFCBCEBEDFAC
+94,nb_gm_004,4,58,4.802886962890625,19,9,False,ABDCABDACADBFBDBEFDFCEDCAFDCFAECAADEEABDEFDFEFCBBECEBDFCBF
+95,nb_gm_004,6,14,1.061150074005127,4,2,False,ABCEDFABCFEDDB
+96,nb_gm_004,4,41,3.3158693313598633,13,6,False,DEFADEFEADFAABEFAECFEACEADCEDBDABBACABECE
+97,nb_gm_004,4,24,2.0305070877075195,8,4,False,DEBCDEFBDEAFCDABCAEFCBAF
+98,nb_gm_004,6,35,2.8204848766326904,10,5,False,FDCEFBFDCCEBDECDCBAEDFCAFACBDAEABEB
+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_002_demo_sdt.py b/demo/nb_gm_002_demo_sdt.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo_sdt.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_003_demo.py b/demo/nb_gm_003_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_003_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
+1,nb_gm_004,2,18,1.5522487163543701,6,3,True,EACADCDCACBABFCBCA
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+3,nb_gm_004,2,37,3.655029773712158,12,6,False,BFDFEDEDCECDCABADBDFADAFEFBEBCACFAFEC
+4,nb_gm_004,4,39,3.4385950565338135,13,6,False,AFBDAFFDEACDCAECBAEBAFABCBAACFDECDFEDEF
+5,nb_gm_004,7,54,4.757416009902954,18,9,False,BFAECBDBFFACEDBBDACBEAEDAFDBADCAEFCAFCEADCEFDEDACFBFBD
+6,nb_gm_004,7,22,1.847884178161621,7,3,False,EDCBFAAEDBCFFAECBDCFDB
+7,nb_gm_004,4,32,2.5619711875915527,10,5,False,DBFEDBFCEDFAECABACACFEBDCEDBAFDB
+8,nb_gm_004,5,45,3.880707025527954,15,7,False,BCDEDBBDEECBDBFACDBEDADEDCAAFDEBAFBDADFEBAEFC
+9,nb_gm_004,7,18,1.5218820571899414,5,3,False,FEBCDADFEBACAFDECB
+10,nb_gm_004,5,43,3.712139129638672,14,7,False,AEFFCABEFFABDFAADBEACDCBDCECFBBEDCEDEACFBDA
+11,nb_gm_004,7,60,5.11740517616272,19,10,False,FDEDCEBFDDECEDFFDCCFEBFCBAFEDBBEAAEBFBAEACADBBCACFAADCABDFCE
+12,nb_gm_004,7,48,4.116453170776367,16,8,False,ADBADCCADFBDDCADCFDEFEACCDBFDCECFBEFBEFEABABEFCE
+13,nb_gm_004,4,17,1.3661417961120605,4,2,False,CBEACBBDCBFCDAEEF
+14,nb_gm_004,4,61,5.220706224441528,20,10,False,CEDACEDCACDBEBDDEAFAEAEFBDECFDEDBFBDCFABEFAEDEACBFABCFCBFFDCC
+15,nb_gm_004,3,52,4.4212260246276855,17,8,False,EBCDBCDEBFDBFBCEFCBFECDEADCEDAEFBDEBDBAABFDAFCADFAEE
+16,nb_gm_004,7,27,2.323706865310669,9,4,False,EACFDBBEAAFDDCEBCFFECABDCFA
+17,nb_gm_004,3,32,2.64604115486145,10,5,False,CBACEBCDBFCBCEBFCBACDDFCEFAFEAFA
+18,nb_gm_004,6,26,2.1442930698394775,7,4,False,ECBADFECBBAFEADDCFDFABCEFC
+19,nb_gm_004,2,44,3.7390267848968506,14,7,False,EDCECACAFADFDAEACECDBDCBCEFEAFFDAFADBDCBCFCB
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+22,nb_gm_004,5,32,2.645524024963379,10,5,False,ECDBCEEDBBFEFBAFAEDACDAFBCADFCEA
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+24,nb_gm_004,3,24,2.0706608295440674,8,3,False,EBAEBFAEFDECBDCADBACFCDF
+25,nb_gm_004,3,36,3.1037821769714355,12,6,False,BCFEBFECFEFCDFBDEFDBCDEBFEDBECEDCEFD
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+28,nb_gm_004,4,39,3.390317916870117,13,6,False,ACBFACBCFABEBAECEAEDBCADFDADAFFDECFBEFC
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+31,nb_gm_004,5,41,3.454822063446045,13,6,False,BEFACBBFAAEBCBAADCDAFDECFEDFCADEAEBDFCEED
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+33,nb_gm_004,3,57,4.94400691986084,19,9,False,FCEFBEDFEDBFBEFBCEECAEDCFDAFADFEBCFBADBACDAEFCAFCFEACEDCA
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+35,nb_gm_004,3,43,3.702164888381958,14,7,False,ACEADCADFCDABEAAEBAFCBFDEFBDFECFBEDBFCBEDCE
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+72,nb_gm_004,4,12,0.9949767589569092,3,1,False,EAFDBCADBCFE
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+75,nb_gm_004,2,61,4.981963157653809,20,10,False,ABFBDFDFBFABAEFAFDFACADCECFCAFBFCBCFEFDFEDBDEBEDECACEAEDBDCBE
+76,nb_gm_004,2,61,5.044064044952393,20,10,False,DFBFCBCBEBDEDCBDBEAECEDCBCECAEABFBDFDEAEBABCFCAFDFAFBADAFDFEE
+77,nb_gm_004,3,26,2.024602174758911,8,4,False,FCAFBCFBEDFEDCADBCECABEABD
+78,nb_gm_004,6,16,1.2625012397766113,5,2,False,CDEBFACDEBAFCBDF
+79,nb_gm_004,2,14,1.071295976638794,4,2,False,CFBCBDBEAEDAFA
+80,nb_gm_004,5,33,2.739096164703369,11,5,False,ADFFEAAFCEFAFECECFCBEEACDEBBADCBD
+81,nb_gm_004,2,61,4.994161128997803,20,10,False,AFBABDBDEBEDECBCFBEBCBACAFEFCEDEBEABDBCDCAEABACBCFDFBDEDFEFAC
+82,nb_gm_004,2,24,2.010740041732788,7,3,False,DEAEFAFACFCBEBDBCDCEABFD
+83,nb_gm_004,7,10,0.7493958473205566,3,0,False,FACBEDBFAC
+84,nb_gm_004,2,63,5.357555150985718,21,10,False,ECFCFBCBEBDEAECEDCDEBEDBDCADAEACBABCECACDADBFBAFEFCECBFBEFECDCF
+85,nb_gm_004,2,44,3.593871831893921,14,7,False,BAEAFEFECEDCBCFCEFEBAEACDCFDFABCDBDFDBADABFB
+86,nb_gm_004,3,17,1.3206660747528076,5,2,False,FDCFECAFCBFDABEAC
+87,nb_gm_004,6,18,1.50052809715271,6,3,False,DCFEABDCFEBADACFBE
+88,nb_gm_004,4,29,2.3075549602508545,8,4,False,FACEFAABFFDBCEDCDEDBCBAECEBDA
+89,nb_gm_004,3,11,0.8077938556671143,2,1,False,ADEBCFECFDA
+90,nb_gm_004,3,55,4.561264276504517,18,9,True,BACBCADCAEDACBADCABCEACDBCABDCFDBCEBFECBACBEDCADEACDFCB
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+92,nb_gm_004,7,63,5.354221820831299,21,10,False,ECDEACDECCEAABEECACDBBEDAEBDCEDEBABCBFEBCAADFFBAECDDFEADFCEFCAF
+93,nb_gm_004,2,42,3.4913809299468994,13,7,False,FEAEBABADACDEDCDBCBFCFDEDAFAEFBFCBCEBEDFAC
+94,nb_gm_004,4,58,4.802886962890625,19,9,False,ABDCABDACADBFBDBEFDFCEDCAFDCFAECAADEEABDEFDFEFCBBECEBDFCBF
+95,nb_gm_004,6,14,1.061150074005127,4,2,False,ABCEDFABCFEDDB
+96,nb_gm_004,4,41,3.3158693313598633,13,6,False,DEFADEFEADFAABEFAECFEACEADCEDBDABBACABECE
+97,nb_gm_004,4,24,2.0305070877075195,8,4,False,DEBCDEFBDEAFCDABCAEFCBAF
+98,nb_gm_004,6,35,2.8204848766326904,10,5,False,FDCEFBFDCCEBDECDCBAEDFCAFACBDAEABEB
+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_002_demo_sdt.py b/demo/nb_gm_002_demo_sdt.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo_sdt.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_003_demo.py b/demo/nb_gm_003_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_003_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_005_demo.py b/demo/nb_gm_005_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_005_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
+1,nb_gm_004,2,18,1.5522487163543701,6,3,True,EACADCDCACBABFCBCA
+2,nb_gm_004,7,36,3.1199769973754883,12,6,False,ABEABFDABBAEBDAABFDBFDAFFACFCDECACBF
+3,nb_gm_004,2,37,3.655029773712158,12,6,False,BFDFEDEDCECDCABADBDFADAFEFBEBCACFAFEC
+4,nb_gm_004,4,39,3.4385950565338135,13,6,False,AFBDAFFDEACDCAECBAEBAFABCBAACFDECDFEDEF
+5,nb_gm_004,7,54,4.757416009902954,18,9,False,BFAECBDBFFACEDBBDACBEAEDAFDBADCAEFCAFCEADCEFDEDACFBFBD
+6,nb_gm_004,7,22,1.847884178161621,7,3,False,EDCBFAAEDBCFFAECBDCFDB
+7,nb_gm_004,4,32,2.5619711875915527,10,5,False,DBFEDBFCEDFAECABACACFEBDCEDBAFDB
+8,nb_gm_004,5,45,3.880707025527954,15,7,False,BCDEDBBDEECBDBFACDBEDADEDCAAFDEBAFBDADFEBAEFC
+9,nb_gm_004,7,18,1.5218820571899414,5,3,False,FEBCDADFEBACAFDECB
+10,nb_gm_004,5,43,3.712139129638672,14,7,False,AEFFCABEFFABDFAADBEACDCBDCECFBBEDCEDEACFBDA
+11,nb_gm_004,7,60,5.11740517616272,19,10,False,FDEDCEBFDDECEDFFDCCFEBFCBAFEDBBEAAEBFBAEACADBBCACFAADCABDFCE
+12,nb_gm_004,7,48,4.116453170776367,16,8,False,ADBADCCADFBDDCADCFDEFEACCDBFDCECFBEFBEFEABABEFCE
+13,nb_gm_004,4,17,1.3661417961120605,4,2,False,CBEACBBDCBFCDAEEF
+14,nb_gm_004,4,61,5.220706224441528,20,10,False,CEDACEDCACDBEBDDEAFAEAEFBDECFDEDBFBDCFABEFAEDEACBFABCFCBFFDCC
+15,nb_gm_004,3,52,4.4212260246276855,17,8,False,EBCDBCDEBFDBFBCEFCBFECDEADCEDAEFBDEBDBAABFDAFCADFAEE
+16,nb_gm_004,7,27,2.323706865310669,9,4,False,EACFDBBEAAFDDCEBCFFECABDCFA
+17,nb_gm_004,3,32,2.64604115486145,10,5,False,CBACEBCDBFCBCEBFCBACDDFCEFAFEAFA
+18,nb_gm_004,6,26,2.1442930698394775,7,4,False,ECBADFECBBAFEADDCFDFABCEFC
+19,nb_gm_004,2,44,3.7390267848968506,14,7,False,EDCECACAFADFDAEACECDBDCBCEFEAFFDAFADBDCBCFCB
+20,nb_gm_004,4,62,5.2222230434417725,20,10,False,EACFEACDFADCCAFDFAFCAADFADBFCABCDADBFBDBCFABFEDAFDADFAEDBACEBD
+21,nb_gm_004,5,61,5.658479928970337,20,10,False,BAEFCBBEFFBCCAFEBCEFABECBAEEFCDECFBCBAFFDBDFAFADAEFABDEADBEDD
+22,nb_gm_004,5,32,2.645524024963379,10,5,False,ECDBCEEDBBFEFBAFAEDACDAFBCADFCEA
+23,nb_gm_004,7,62,5.216619968414307,20,10,False,EADDFBDEAADFFCEEBDFADCDBAFEDFCADFFDBABDDFACAEACDABEAFCACBCEFCB
+24,nb_gm_004,3,24,2.0706608295440674,8,3,False,EBAEBFAEFDECBDCADBACFCDF
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+69,nb_gm_004,2,12,1.0047342777252197,2,0,False,FCABEDEDFABC
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+76,nb_gm_004,2,61,5.044064044952393,20,10,False,DFBFCBCBEBDEDCBDBEAECEDCBCECAEABFBDFDEAEBABCFCAFDFAFBADAFDFEE
+77,nb_gm_004,3,26,2.024602174758911,8,4,False,FCAFBCFBEDFEDCADBCECABEABD
+78,nb_gm_004,6,16,1.2625012397766113,5,2,False,CDEBFACDEBAFCBDF
+79,nb_gm_004,2,14,1.071295976638794,4,2,False,CFBCBDBEAEDAFA
+80,nb_gm_004,5,33,2.739096164703369,11,5,False,ADFFEAAFCEFAFECECFCBEEACDEBBADCBD
+81,nb_gm_004,2,61,4.994161128997803,20,10,False,AFBABDBDEBEDECBCFBEBCBACAFEFCEDEBEABDBCDCAEABACBCFDFBDEDFEFAC
+82,nb_gm_004,2,24,2.010740041732788,7,3,False,DEAEFAFACFCBEBDBCDCEABFD
+83,nb_gm_004,7,10,0.7493958473205566,3,0,False,FACBEDBFAC
+84,nb_gm_004,2,63,5.357555150985718,21,10,False,ECFCFBCBEBDEAECEDCDEBEDBDCADAEACBABCECACDADBFBAFEFCECBFBEFECDCF
+85,nb_gm_004,2,44,3.593871831893921,14,7,False,BAEAFEFECEDCBCFCEFEBAEACDCFDFABCDBDFDBADABFB
+86,nb_gm_004,3,17,1.3206660747528076,5,2,False,FDCFECAFCBFDABEAC
+87,nb_gm_004,6,18,1.50052809715271,6,3,False,DCFEABDCFEBADACFBE
+88,nb_gm_004,4,29,2.3075549602508545,8,4,False,FACEFAABFFDBCEDCDEDBCBAECEBDA
+89,nb_gm_004,3,11,0.8077938556671143,2,1,False,ADEBCFECFDA
+90,nb_gm_004,3,55,4.561264276504517,18,9,True,BACBCADCAEDACBADCABCEACDBCABDCFDBCEBFECBACBEDCADEACDFCB
+91,nb_gm_004,3,62,5.1214518547058105,20,10,False,DFEBDECDEEDCADEABDAFEAEFDCFFCAACBABCDBEBDEBFACAFCFBCBFDBEFCAFC
+92,nb_gm_004,7,63,5.354221820831299,21,10,False,ECDEACDECCEAABEECACDBBEDAEBDCEDEBABCBFEBCAADFFBAECDDFEADFCEFCAF
+93,nb_gm_004,2,42,3.4913809299468994,13,7,False,FEAEBABADACDEDCDBCBFCFDEDAFAEFBFCBCEBEDFAC
+94,nb_gm_004,4,58,4.802886962890625,19,9,False,ABDCABDACADBFBDBEFDFCEDCAFDCFAECAADEEABDEFDFEFCBBECEBDFCBF
+95,nb_gm_004,6,14,1.061150074005127,4,2,False,ABCEDFABCFEDDB
+96,nb_gm_004,4,41,3.3158693313598633,13,6,False,DEFADEFEADFAABEFAECFEACEADCEDBDABBACABECE
+97,nb_gm_004,4,24,2.0305070877075195,8,4,False,DEBCDEFBDEAFCDABCAEFCBAF
+98,nb_gm_004,6,35,2.8204848766326904,10,5,False,FDCEFBFDCCEBDECDCBAEDFCAFACBDAEABEB
+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_002_demo_sdt.py b/demo/nb_gm_002_demo_sdt.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo_sdt.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_003_demo.py b/demo/nb_gm_003_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_003_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_005_demo.py b/demo/nb_gm_005_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_005_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/skewed_nback.py b/demo/skewed_nback.py
deleted file mode 100644
index 44dcfa1..0000000
--- a/demo/skewed_nback.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import expyriment
-from expyriment import design, control, stimuli, misc
-from generators import nb_gm_002
-
-nback_sequence = nb_gm_002.SequenceGenerator().generate()
-
-exp = design.Experiment("Skewed N-Back Task")
-control.initialize(exp)
-exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
-
-
-fixation = stimuli.FixCross()
-fixation.preload()
-
-# left and right arrow keys for responses
-response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
-
-expyriment.control.start(exp)
-
-# initialize three blocks
-for b in range(0, 2):
-    block = design.Block()
-    block.set_factor("block", b)
-    for item in nback_sequence:
-        trial = design.Trial()
-        trial.set_factor("stimulus", item)
-        stim = stimuli.TextLine(item, text_size=200)
-        stim.preload()
-        trial.add_stimulus(stim)
-        block.add_trial(trial, random_position=False)
-    exp.add_block(block)
-
-for block in exp.blocks:
-    for trial in block.trials:
-        exp.clock.wait(1000 - fixation.present())
-        trial.stimuli[0].present()
-        pressed_key, rt = exp.keyboard.wait(response_keys)
-        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
-        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
-
-expyriment.control.end("Goodbye!", goodbye_delay=1000)
-

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
+1,nb_gm_004,2,18,1.5522487163543701,6,3,True,EACADCDCACBABFCBCA
+2,nb_gm_004,7,36,3.1199769973754883,12,6,False,ABEABFDABBAEBDAABFDBFDAFFACFCDECACBF
+3,nb_gm_004,2,37,3.655029773712158,12,6,False,BFDFEDEDCECDCABADBDFADAFEFBEBCACFAFEC
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diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_002_demo_sdt.py b/demo/nb_gm_002_demo_sdt.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo_sdt.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_003_demo.py b/demo/nb_gm_003_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_003_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_005_demo.py b/demo/nb_gm_005_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_005_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/skewed_nback.py b/demo/skewed_nback.py
deleted file mode 100644
index 44dcfa1..0000000
--- a/demo/skewed_nback.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import expyriment
-from expyriment import design, control, stimuli, misc
-from generators import nb_gm_002
-
-nback_sequence = nb_gm_002.SequenceGenerator().generate()
-
-exp = design.Experiment("Skewed N-Back Task")
-control.initialize(exp)
-exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
-
-
-fixation = stimuli.FixCross()
-fixation.preload()
-
-# left and right arrow keys for responses
-response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
-
-expyriment.control.start(exp)
-
-# initialize three blocks
-for b in range(0, 2):
-    block = design.Block()
-    block.set_factor("block", b)
-    for item in nback_sequence:
-        trial = design.Trial()
-        trial.set_factor("stimulus", item)
-        stim = stimuli.TextLine(item, text_size=200)
-        stim.preload()
-        trial.add_stimulus(stim)
-        block.add_trial(trial, random_position=False)
-    exp.add_block(block)
-
-for block in exp.blocks:
-    for trial in block.trials:
-        exp.clock.wait(1000 - fixation.present())
-        trial.stimuli[0].present()
-        pressed_key, rt = exp.keyboard.wait(response_keys)
-        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
-        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
-
-expyriment.control.end("Goodbye!", goodbye_delay=1000)
-
diff --git a/generators/nb_gm_004.py b/generators/nb_gm_004.py
index 20ce55d..56678a3 100644
--- a/generators/nb_gm_004.py
+++ b/generators/nb_gm_004.py
@@ -40,7 +40,7 @@
         self.reset(trials, targets, lures)
         while not self.seq or len(self.seq) < self.trials:
             #self.seq += self.best_choice()
-            chunk_size = self.n+1 if len(self.seq) + self.n + 1 <= self.trials else self.trials-len(self.seq)
+            chunk_size = 3 if len(self.seq) + 3 <= self.trials else self.trials-len(self.seq)
             self.seq += self.best_chunk(chunk_size)
         return self.seq
 
@@ -83,19 +83,3 @@
     def lures_cost(self, lures_ratio) -> float:
         return 1.0 - self.lures_norm.pdf(lures_ratio) / self.lures_norm.pdf(self.lures_ratio)
 
-
-if __name__ == '__main__':
-    import time
-    import numpy as np
-
-    trials, targets, lures = 24, 8, 4
-    choices = ['1', '2', '3', '4', '5', '6']
-    print('alg,n,trials,time,targets,lures')
-    for s in range(1, common.sample_size):
-        n = 3 #np.random.randint(2, 8)
-        gen = SequenceGenerator(choices, n)
-        st = time.time()
-        s = gen.generate(trials, targets, lures)
-        st = time.time() - st
-        t, lu = common.count_targets_and_lures(s, n)
-        print(f"nb_gm_004,{n},{trials},{st:0.2f},{t},{lu}")

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_004,2,64,5.629104852676392,20,10,False,DBFBFAFEAECACFDCDABADBDCFCBCABACECAEDEADAFAEDEFBFEBABFDCECDEBEFB
+1,nb_gm_004,2,18,1.5522487163543701,6,3,True,EACADCDCACBABFCBCA
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+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_002_demo_sdt.py b/demo/nb_gm_002_demo_sdt.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo_sdt.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_003_demo.py b/demo/nb_gm_003_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_003_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_005_demo.py b/demo/nb_gm_005_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_005_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/skewed_nback.py b/demo/skewed_nback.py
deleted file mode 100644
index 44dcfa1..0000000
--- a/demo/skewed_nback.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import expyriment
-from expyriment import design, control, stimuli, misc
-from generators import nb_gm_002
-
-nback_sequence = nb_gm_002.SequenceGenerator().generate()
-
-exp = design.Experiment("Skewed N-Back Task")
-control.initialize(exp)
-exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
-
-
-fixation = stimuli.FixCross()
-fixation.preload()
-
-# left and right arrow keys for responses
-response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
-
-expyriment.control.start(exp)
-
-# initialize three blocks
-for b in range(0, 2):
-    block = design.Block()
-    block.set_factor("block", b)
-    for item in nback_sequence:
-        trial = design.Trial()
-        trial.set_factor("stimulus", item)
-        stim = stimuli.TextLine(item, text_size=200)
-        stim.preload()
-        trial.add_stimulus(stim)
-        block.add_trial(trial, random_position=False)
-    exp.add_block(block)
-
-for block in exp.blocks:
-    for trial in block.trials:
-        exp.clock.wait(1000 - fixation.present())
-        trial.stimuli[0].present()
-        pressed_key, rt = exp.keyboard.wait(response_keys)
-        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
-        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
-
-expyriment.control.end("Goodbye!", goodbye_delay=1000)
-
diff --git a/generators/nb_gm_004.py b/generators/nb_gm_004.py
index 20ce55d..56678a3 100644
--- a/generators/nb_gm_004.py
+++ b/generators/nb_gm_004.py
@@ -40,7 +40,7 @@
         self.reset(trials, targets, lures)
         while not self.seq or len(self.seq) < self.trials:
             #self.seq += self.best_choice()
-            chunk_size = self.n+1 if len(self.seq) + self.n + 1 <= self.trials else self.trials-len(self.seq)
+            chunk_size = 3 if len(self.seq) + 3 <= self.trials else self.trials-len(self.seq)
             self.seq += self.best_chunk(chunk_size)
         return self.seq
 
@@ -83,19 +83,3 @@
     def lures_cost(self, lures_ratio) -> float:
         return 1.0 - self.lures_norm.pdf(lures_ratio) / self.lures_norm.pdf(self.lures_ratio)
 
-
-if __name__ == '__main__':
-    import time
-    import numpy as np
-
-    trials, targets, lures = 24, 8, 4
-    choices = ['1', '2', '3', '4', '5', '6']
-    print('alg,n,trials,time,targets,lures')
-    for s in range(1, common.sample_size):
-        n = 3 #np.random.randint(2, 8)
-        gen = SequenceGenerator(choices, n)
-        st = time.time()
-        s = gen.generate(trials, targets, lures)
-        st = time.time() - st
-        t, lu = common.count_targets_and_lures(s, n)
-        print(f"nb_gm_004,{n},{trials},{st:0.2f},{t},{lu}")
diff --git a/generators/progressive_random.py b/generators/progressive_random.py
index 81bebbc..441656f 100644
--- a/generators/progressive_random.py
+++ b/generators/progressive_random.py
@@ -1,6 +1,8 @@
 import random
 import scipy.stats
 
+import benchmarks.common as common
+
 
 class SequenceGenerator:
     """Generate a sequence progressively according to a predefined TL ratio and an even distribution"""
@@ -57,26 +59,15 @@
         :return:
         """
 
-        targets, lures = self.count_targets_and_lures(seq)
+        targets, lures = common.count_targets_and_lures(seq, self.n)
         targets_cost = 1.0 - self.norm_targets_ratio_dist.pdf(targets/self.trials)
         tl_cost = 1.0 - self.norm_tl_ratio_dist.pdf(self.tl(seq))
         even_cost = self.even_distribution_cost(seq)
         return targets_cost + tl_cost + even_cost
 
-    def count_targets_and_lures(self, seq):
-        n = self.n
-        targets = 0.0
-        lures = 0.0
-        for index in range(n, len(seq)):
-            if seq[index] == seq[index - n]:
-                targets += 1.0
-            elif seq[index] == seq[index - (n-1)] or seq[index] == seq[index - (n+1)]:
-                lures += 1.0
-        return targets, lures
-
     def tl(self, seq):
         """Calculates the T/L ratio in a block of trials."""
-        targets, lures = self.count_targets_and_lures(seq)
+        targets, lures = common.count_targets_and_lures(seq, self.n)
         if lures < 0.01:  # avoid division by zero
             lures = 0.01
         return targets/lures

diff --git a/benchmarks/common.py b/benchmarks/common.py
index 5e2b1da..e40c0df 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -2,9 +2,9 @@
 
 # Common benchmark parameters
 choices = alphabetic_choices = ['A', 'B', 'C', 'D', 'E', 'F']
-trials_range = (10, 100)
+trials_range = (10, 65)
 n = 4
-sample_size = 20
+sample_size = 100
 
 
 def count_targets_and_lures(seq, n):
@@ -17,3 +17,11 @@
         else:
             mask += 'D'
     return mask.count('T'), mask.count('L')
+
+
+def skewness(seq, choices):
+    import heapq
+    trials = len(seq)
+    freqs = [float(seq.count(c)) for c in choices]
+    ralph_skewed = sum(heapq.nlargest(int(len(choices) / 2), freqs)) > (trials * 2 / 3)
+    return ralph_skewed
diff --git a/benchmarks/nb_gm_004_bm.py b/benchmarks/nb_gm_004_bm.py
new file mode 100644
index 0000000..4b0015c
--- /dev/null
+++ b/benchmarks/nb_gm_004_bm.py
@@ -0,0 +1,26 @@
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+from generators.nb_gm_004 import SequenceGenerator
+
+if __name__ == '__main__':
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, lures)
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_004_profile.csv', sep=',', encoding='utf-8')
diff --git a/benchmarks/results/nb_gm_004_profile.csv b/benchmarks/results/nb_gm_004_profile.csv
new file mode 100644
index 0000000..656adff
--- /dev/null
+++ b/benchmarks/results/nb_gm_004_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
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+82,nb_gm_004,2,24,2.010740041732788,7,3,False,DEAEFAFACFCBEBDBCDCEABFD
+83,nb_gm_004,7,10,0.7493958473205566,3,0,False,FACBEDBFAC
+84,nb_gm_004,2,63,5.357555150985718,21,10,False,ECFCFBCBEBDEAECEDCDEBEDBDCADAEACBABCECACDADBFBAFEFCECBFBEFECDCF
+85,nb_gm_004,2,44,3.593871831893921,14,7,False,BAEAFEFECEDCBCFCEFEBAEACDCFDFABCDBDFDBADABFB
+86,nb_gm_004,3,17,1.3206660747528076,5,2,False,FDCFECAFCBFDABEAC
+87,nb_gm_004,6,18,1.50052809715271,6,3,False,DCFEABDCFEBADACFBE
+88,nb_gm_004,4,29,2.3075549602508545,8,4,False,FACEFAABFFDBCEDCDEDBCBAECEBDA
+89,nb_gm_004,3,11,0.8077938556671143,2,1,False,ADEBCFECFDA
+90,nb_gm_004,3,55,4.561264276504517,18,9,True,BACBCADCAEDACBADCABCEACDBCABDCFDBCEBFECBACBEDCADEACDFCB
+91,nb_gm_004,3,62,5.1214518547058105,20,10,False,DFEBDECDEEDCADEABDAFEAEFDCFFCAACBABCDBEBDEBFACAFCFBCBFDBEFCAFC
+92,nb_gm_004,7,63,5.354221820831299,21,10,False,ECDEACDECCEAABEECACDBBEDAEBDCEDEBABCBFEBCAADFFBAECDDFEADFCEFCAF
+93,nb_gm_004,2,42,3.4913809299468994,13,7,False,FEAEBABADACDEDCDBCBFCFDEDAFAEFBFCBCEBEDFAC
+94,nb_gm_004,4,58,4.802886962890625,19,9,False,ABDCABDACADBFBDBEFDFCEDCAFDCFAECAADEEABDEFDFEFCBBECEBDFCBF
+95,nb_gm_004,6,14,1.061150074005127,4,2,False,ABCEDFABCFEDDB
+96,nb_gm_004,4,41,3.3158693313598633,13,6,False,DEFADEFEADFAABEFAECFEACEADCEDBDABBACABECE
+97,nb_gm_004,4,24,2.0305070877075195,8,4,False,DEBCDEFBDEAFCDABCAEFCBAF
+98,nb_gm_004,6,35,2.8204848766326904,10,5,False,FDCEFBFDCCEBDECDCBAEDFCAFACBDAEABEB
+99,nb_gm_004,3,32,2.5904409885406494,10,4,False,AFBEABFABBAECDECFDDFCCDEDAEFCBFC
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_002_demo_sdt.py b/demo/nb_gm_002_demo_sdt.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_002_demo_sdt.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_003_demo.py b/demo/nb_gm_003_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_003_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/nb_gm_005_demo.py b/demo/nb_gm_005_demo.py
new file mode 100644
index 0000000..776b951
--- /dev/null
+++ b/demo/nb_gm_005_demo.py
@@ -0,0 +1,44 @@
+import expyriment
+from expyriment import design, control, stimuli, misc
+from generators import nb_gm_002
+
+import benchmarks.common as common
+
+nback_sequence = nb_gm_002.SequenceGenerator(common.choices).generate(24, 8, 2, 2)
+
+exp = design.Experiment("Skewed N-Back Task")
+control.initialize(exp)
+exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
+
+
+fixation = stimuli.FixCross()
+fixation.preload()
+
+# left and right arrow keys for responses
+response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
+
+expyriment.control.start(exp)
+
+# initialize three blocks
+for b in range(0, 2):
+    block = design.Block()
+    block.set_factor("block", b)
+    for item in nback_sequence:
+        trial = design.Trial()
+        trial.set_factor("stimulus", item)
+        stim = stimuli.TextLine(item, text_size=200)
+        stim.preload()
+        trial.add_stimulus(stim)
+        block.add_trial(trial, random_position=False)
+    exp.add_block(block)
+
+for block in exp.blocks:
+    for trial in block.trials:
+        exp.clock.wait(1000 - fixation.present())
+        trial.stimuli[0].present()
+        pressed_key, rt = exp.keyboard.wait(response_keys)
+        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
+
+expyriment.control.end("Goodbye!", goodbye_delay=1000)
+
diff --git a/demo/skewed_nback.py b/demo/skewed_nback.py
deleted file mode 100644
index 44dcfa1..0000000
--- a/demo/skewed_nback.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import expyriment
-from expyriment import design, control, stimuli, misc
-from generators import nb_gm_002
-
-nback_sequence = nb_gm_002.SequenceGenerator().generate()
-
-exp = design.Experiment("Skewed N-Back Task")
-control.initialize(exp)
-exp.data_variable_names = ["block", "stimulus", "pressed_key", "rt"]
-
-
-fixation = stimuli.FixCross()
-fixation.preload()
-
-# left and right arrow keys for responses
-response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
-
-expyriment.control.start(exp)
-
-# initialize three blocks
-for b in range(0, 2):
-    block = design.Block()
-    block.set_factor("block", b)
-    for item in nback_sequence:
-        trial = design.Trial()
-        trial.set_factor("stimulus", item)
-        stim = stimuli.TextLine(item, text_size=200)
-        stim.preload()
-        trial.add_stimulus(stim)
-        block.add_trial(trial, random_position=False)
-    exp.add_block(block)
-
-for block in exp.blocks:
-    for trial in block.trials:
-        exp.clock.wait(1000 - fixation.present())
-        trial.stimuli[0].present()
-        pressed_key, rt = exp.keyboard.wait(response_keys)
-        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
-        exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
-
-expyriment.control.end("Goodbye!", goodbye_delay=1000)
-
diff --git a/generators/nb_gm_004.py b/generators/nb_gm_004.py
index 20ce55d..56678a3 100644
--- a/generators/nb_gm_004.py
+++ b/generators/nb_gm_004.py
@@ -40,7 +40,7 @@
         self.reset(trials, targets, lures)
         while not self.seq or len(self.seq) < self.trials:
             #self.seq += self.best_choice()
-            chunk_size = self.n+1 if len(self.seq) + self.n + 1 <= self.trials else self.trials-len(self.seq)
+            chunk_size = 3 if len(self.seq) + 3 <= self.trials else self.trials-len(self.seq)
             self.seq += self.best_chunk(chunk_size)
         return self.seq
 
@@ -83,19 +83,3 @@
     def lures_cost(self, lures_ratio) -> float:
         return 1.0 - self.lures_norm.pdf(lures_ratio) / self.lures_norm.pdf(self.lures_ratio)
 
-
-if __name__ == '__main__':
-    import time
-    import numpy as np
-
-    trials, targets, lures = 24, 8, 4
-    choices = ['1', '2', '3', '4', '5', '6']
-    print('alg,n,trials,time,targets,lures')
-    for s in range(1, common.sample_size):
-        n = 3 #np.random.randint(2, 8)
-        gen = SequenceGenerator(choices, n)
-        st = time.time()
-        s = gen.generate(trials, targets, lures)
-        st = time.time() - st
-        t, lu = common.count_targets_and_lures(s, n)
-        print(f"nb_gm_004,{n},{trials},{st:0.2f},{t},{lu}")
diff --git a/generators/progressive_random.py b/generators/progressive_random.py
index 81bebbc..441656f 100644
--- a/generators/progressive_random.py
+++ b/generators/progressive_random.py
@@ -1,6 +1,8 @@
 import random
 import scipy.stats
 
+import benchmarks.common as common
+
 
 class SequenceGenerator:
     """Generate a sequence progressively according to a predefined TL ratio and an even distribution"""
@@ -57,26 +59,15 @@
         :return:
         """
 
-        targets, lures = self.count_targets_and_lures(seq)
+        targets, lures = common.count_targets_and_lures(seq, self.n)
         targets_cost = 1.0 - self.norm_targets_ratio_dist.pdf(targets/self.trials)
         tl_cost = 1.0 - self.norm_tl_ratio_dist.pdf(self.tl(seq))
         even_cost = self.even_distribution_cost(seq)
         return targets_cost + tl_cost + even_cost
 
-    def count_targets_and_lures(self, seq):
-        n = self.n
-        targets = 0.0
-        lures = 0.0
-        for index in range(n, len(seq)):
-            if seq[index] == seq[index - n]:
-                targets += 1.0
-            elif seq[index] == seq[index - (n-1)] or seq[index] == seq[index - (n+1)]:
-                lures += 1.0
-        return targets, lures
-
     def tl(self, seq):
         """Calculates the T/L ratio in a block of trials."""
-        targets, lures = self.count_targets_and_lures(seq)
+        targets, lures = common.count_targets_and_lures(seq, self.n)
         if lures < 0.01:  # avoid division by zero
             lures = 0.01
         return targets/lures
diff --git a/generators/progressive_random_block_ga.py b/generators/progressive_random_block_ga.py
index c07ac49..a7f454a 100644
--- a/generators/progressive_random_block_ga.py
+++ b/generators/progressive_random_block_ga.py
@@ -1,6 +1,8 @@
 import itertools as it
 import random
 
+import benchmarks.common as common
+import scipy.stats
 
 class ProgressiveGAGenerator:
     """Generate a sequence progressively according to a predefined TL ratio and an even distribution"""
@@ -15,6 +17,10 @@
         """
         self.tl, self.trials, self.choices, self.pool_size, self.n = tl, trials, choices, pool_size, n
         self.pool = []
+
+        self.tl_norm = scipy.stats.norm(self.tl, 0.5)
+        self.skewness_norm = scipy.stats.norm(0, 0.5)
+
         self.__init_pool(pool_size)
 
     def generate(self):
@@ -55,27 +61,6 @@
         sorted_pool = sorted(self.pool, key=lambda ss: self.cost(ss))
         return sorted_pool[:count]
 
-    def even_dist_cost(self, seq):
-        """
-        Calculate fitness according to the similarity to the desired uniform distribution.
-        :param seq: a string
-        :return:
-        """
-        costs = {c: 0.0 for c in self.choices}
-        for c in list(seq):
-            costs[c] += (1.0 if costs.__contains__(c) else 0.0)
-        costs = {k: abs(1.0 - v*len(self.choices)/self.trials) for k,v in costs.items()}
-        return max(list(costs.values()))
-
-    def cost(self, seq):
-        """
-        Calculate overall fitness of a sequence (block of trials).
-        Right now it's a cost function, so we try to minimize this cost.
-        :param seq:
-        :return:
-        """
-        # add fitness for uniform distribution of all stimuli
-        return abs(self.calculate_tl_ratio(seq, self.n) - self.tl) + self.even_dist_cost(seq)
 
     def crossover_all(self):
         """
@@ -108,27 +93,26 @@
             return ''.join(seq_list)
         return seq
 
-    @staticmethod
-    def calculate_tl_ratio(seq, n: int):
-        """Calculates the T/L ratio in a block of trials."""
-        targets = 0.0
-        lures = 0.0
-        for index in range(n, len(seq)):
-            item = seq[index]
-            if item == seq[index - n]:
-                targets += 1.0
-            elif item == seq[index - (n-1)] or item == seq[index - (n+1)]:
-                lures += 1.0
-        if lures - 0.0 < 0.001:  # avoid division by zero
-            lures = 0.001
-        return targets/lures
+    def cost(self, seq):
+        targets, lures = common.count_targets_and_lures(seq, self.n)
+        #print(seq, targets, lures)
+        tl = targets / lures if lures >0 else targets
+        c1, c2 = self.skewness_cost(seq), self.tl_cost(tl)
+        return c1+c2
+
+    def skewness_cost(self, seq):
+        even_ratio = self.trials / len(self.choices)
+        costs = {c: abs(seq.count(c) - even_ratio) for c in self.choices}
+        max_deviation_from_even_dist = max(list(costs.values()))
+        return 1.0 - self.skewness_norm.pdf(max_deviation_from_even_dist) / self.skewness_norm.pdf(0)
+
+    def tl_cost(self, tl):
+        return 1.0 - self.tl_norm.pdf(tl) / self.tl_norm.pdf(self.tl)
 
 
 if __name__ == '__main__':
 
-    generator = ProgressiveGAGenerator(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'], trials=16, n=2)
+    generator = ProgressiveGAGenerator(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'], trials=24, n=4)
     sq = generator.generate()
-    tl_ratio = generator.calculate_tl_ratio(sq, n=2)
-    even_dist = generator.even_dist_cost(sq)
 
-    print('Progressively-Optimized Sequence: %s' % sq, 'with tl_ratio=%f' % tl_ratio, 'and even_dist_cost=%f' % even_dist)
+    print('Progressively-Optimized Sequence: %s' % sq)