From 0705824a170b358c2748c74def40b87cb283db15 Mon Sep 17 00:00:00 2001
From: Felix Zakirov <f_zakirov@vk.com>
Date: Tue, 8 Oct 2024 21:19:48 -0400
Subject: [PATCH] Updated post-error behavior measures

---
 .../behavior_analysis.py                      | 34 ++++++++++++-------
 .../behavior_processing_batch.sub             |  0
 .../check_subject_csv.py                      |  0
 3 files changed, 22 insertions(+), 12 deletions(-)
 rename code/{preprocessing-behavior => behavior}/behavior_analysis.py (90%)
 rename code/{preprocessing-behavior => behavior}/behavior_processing_batch.sub (100%)
 rename code/{preprocessing-behavior => behavior}/check_subject_csv.py (100%)

diff --git a/code/preprocessing-behavior/behavior_analysis.py b/code/behavior/behavior_analysis.py
similarity index 90%
rename from code/preprocessing-behavior/behavior_analysis.py
rename to code/behavior/behavior_analysis.py
index 470a391..b0a8e54 100644
--- a/code/preprocessing-behavior/behavior_analysis.py
+++ b/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
             ))
diff --git a/code/preprocessing-behavior/behavior_processing_batch.sub b/code/behavior/behavior_processing_batch.sub
similarity index 100%
rename from code/preprocessing-behavior/behavior_processing_batch.sub
rename to code/behavior/behavior_processing_batch.sub
diff --git a/code/preprocessing-behavior/check_subject_csv.py b/code/behavior/check_subject_csv.py
similarity index 100%
rename from code/preprocessing-behavior/check_subject_csv.py
rename to code/behavior/check_subject_csv.py