diff --git a/Examples.ipynb b/Examples.ipynb
index 7941650..ef80005 100644
--- a/Examples.ipynb
+++ b/Examples.ipynb
@@ -194,8 +194,8 @@
     "# actual parameters of the observer\n",
     "bias = -10.\n",
     "threshold = 20.\n",
-    "gamma1 = .2\n",
-    "gamma2 = 0.\n",
+    "gamma1 = .2  # lapse rate at low (-ve) stimulus values\n",
+    "gamma2 = 0.  # lapse rate at high (+ve) stimulus values\n",
     "\n",
     "# fake experimental data given those parameters\n",
     "pp = psy.erf_psycho_2gammas((bias, threshold, gamma1, gamma2), xx)\n",
@@ -216,7 +216,7 @@
     "}\n",
     "\n",
     "# data: \n",
-    "# 3 x n matrix where first row corrsponds to stim levels (% contrast), \n",
+    "# 3 x n matrix where first row corresponds to stim levels (% contrast), \n",
     "# the second to number of trials for each stim level (int),\n",
     "# the third to proportion rightward (float between 0 and 1)\n",
     "data = np.vstack((xx, ntrials * np.ones((nxx,)), np.mean(dd, axis=0)))\n",
diff --git a/psychofit.py b/psychofit.py
index 4e15352..40495c3 100644
--- a/psychofit.py
+++ b/psychofit.py
@@ -118,7 +118,7 @@ def neg_likelihood(pars, data, P_model='weibull', parmin=None, parmax=None):
 
     Args:
         pars: Model parameters [threshold, slope, gamma], or if
-              using the 'erf_psycho_2gammas' model append a second gamma value.
+              using the 'erf_psycho_2gammas' model append a second gamma value (i.e. lapse high).
         data: 3 x n matrix where first row corresponds to stim levels,
               the second to number of trials for each stim level (int),
               the third to proportion correct / proportion rightward (float between 0 and 1)
@@ -292,8 +292,11 @@ def erf_psycho_2gammas(pars, xx):
     """
     erf function from 0 to 1, with two lapse rates.
 
+    gamma1 cooresponds to 'lapse low', i.e. the lapse rate at low (-ve) stimulus values;
+    gamma2 cooresponds to 'lapse high'.
+
     Args:
-        pars: Model parameters [bias, slope, gamma].
+        pars: Model parameters [bias, slope, gamma1, gamma2].
         xx: vector of stim levels (%)
 
     Returns: