Updated post-error behavior measures

Author: fzakirov

code/preprocessing-behavior/behavior_analysis.py renamed to code/behavior/behavior_analysis.py

@@ -223,43 +223,53 @@ def convert_to_list_resp(series):
             processing_log["rt_err"+prefix].append(np.round(condition_data[(condition_data["congruent"] == 0) & (condition_data["accuracy"] == 0)]["rt"].mean() * 1000, 3))
             processing_log["pes"+prefix].append(np.round(
                 np.log(
-                    condition_data[(condition_data["accuracy"] == 1) & (condition_data["pre_accuracy"] == 0)].rt
+                    condition_data[(condition_data["accuracy"] == 1) & (condition_data["pre_accuracy"] == 0) &\
+                    (condition_data["pre_congruent"] == 0)].rt
                 ).mean()\
                 - np.log(
-                    condition_data[(condition_data["accuracy"] == 1) & (condition_data["pre_accuracy"] == 1)].rt
+                    condition_data[(condition_data["accuracy"] == 1) & (condition_data["pre_accuracy"] == 1) &\
+                    (condition_data["pre_congruent"] == 0)].rt
                 ).mean(), 5
             ))
             processing_log["pea"+prefix].append(np.round(
-                condition_data[condition_data["pre_accuracy"] == 0].accuracy.mean()\
-                - condition_data[condition_data["pre_accuracy"] == 1].accuracy.mean(), 5
+                condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["pre_congruent"] == 0)].accuracy.mean()\
+                - condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["pre_congruent"] == 0)].accuracy.mean(), 5
             ))
 
             processing_log["peri_acc"+prefix].append(np.round(
                 (
-                    condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 0)]["accuracy"].mean()\
-                 - condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 1)]["accuracy"].mean()
+                    condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 0) &\
+                    (condition_data["pre_congruent"] == 0)]["accuracy"].mean()\
+                 - condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 1) &\
+                    (condition_data["pre_congruent"] == 0)]["accuracy"].mean()
                 )\
                 - (
-                    condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 0)]["accuracy"].mean()\
-                 - condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 1)]["accuracy"].mean()
+                    condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 0) &\
+                    (condition_data["pre_congruent"] == 0)]["accuracy"].mean()\
+                 - condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 1) &\
+                    (condition_data["pre_congruent"] == 0)]["accuracy"].mean()
                 ), 5
             ))
 
             processing_log["peri_rt"+prefix].append(np.round(
                 (
                     np.log(
-                    condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 0)]["rt"]
+                    condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 0) &\
+                        (condition_data["pre_congruent"] == 0)]["rt"]
                     ).mean()\
                  - np.log(
-                     condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 1)]["rt"]
+                     condition_data[(condition_data["pre_accuracy"] == 0) & (condition_data["congruent"] == 1) &\
+                     (condition_data["pre_congruent"] == 0)]["rt"]
                  ).mean()
                 )\
                 - (
                     np.log(
-                    condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 0)]["rt"]
+                    condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 0) &\
+                    (condition_data["pre_congruent"] == 0)]["rt"]
                     ).mean()\
                  - np.log(
-                     condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 1)]["rt"]
+                     condition_data[(condition_data["pre_accuracy"] == 1) & (condition_data["congruent"] == 1) &\
+                     (condition_data["pre_congruent"] == 0)]["rt"]
                  ).mean()
                   ), 5
             ))