diff --git a/pyvbmc_optimization.py b/pyvbmc_optimization.py
index 2cbccc0..228b264 100644
--- a/pyvbmc_optimization.py
+++ b/pyvbmc_optimization.py
@@ -348,7 +348,7 @@ def qlearning_generic(
 
 
 def fit_participant_pyvbmc(
-    participant_data: pd.DataFrame, model_config: Dict, verbose: bool = True
+    participant_data: pd.DataFrame, model_config: Dict, verbose: bool = True, plot : bool = True
 ) -> Dict:
     """
     Optimise les paramètres du modèle pour un participant utilisant PyVBMC.
@@ -399,6 +399,8 @@ def fit_participant_pyvbmc(
         options={
             # "verbose": 0 if not verbose else 1,
             "display": "off",
+            "plot": plot,
+            "log_file_name": None,
         },
         prior=UniformBox(
             a=model_config["lower"], b=model_config["upper"], D=model_config["n_params"]
@@ -446,8 +448,13 @@ def fit_participant_pyvbmc(
 
     # Ajout des paramètres estimés
     for i, param_name in enumerate(model_config["param_names"]):
-        result[param_name] = posterior_mean[i]
-        result[f"sd_{param_name}"] = posterior_sd[i]
+        # Here we use expit for parameters that were originally bounded between 0 and 1
+        if param_name.startswith("alpha") or param_name.startswith("forget"):
+            result[param_name] = expit(posterior_mean[i])
+            result[f"sd_{param_name}"] = expit(posterior_sd[i])
+        else:
+            result[param_name] = posterior_mean[i]
+            result[f"sd_{param_name}"] = posterior_sd[i]
 
     return result
 
@@ -530,9 +537,13 @@ def fit_participant_deoptim(
         "posterior_mean": posterior_mean,
     }
 
-    # Ajout des paramètres estimés
+    # Ajout des paramètres estimés après les avoir renvoyés dans par logis
     for i, param_name in enumerate(model_config["param_names"]):
-        result_dict[param_name] = posterior_mean[i]
+        # Here we expit alpha, forget
+        if param_name.startswith("alpha") or param_name.startswith("forget"):
+            result_dict[param_name] = expit(posterior_mean[i])
+        else:
+            result_dict[param_name] = posterior_mean[i]
 
     return result_dict
 
@@ -548,6 +559,7 @@ def fit_all_participants(
     method: str = "VBMC",
     n_participants: Optional[int] = None,
     verbose: bool = True,
+    plot: bool = True,
 ) -> Dict[str, List[Dict]]:
     """
     Ajuste tous les modèles pour tous les participants.
@@ -588,7 +600,7 @@ def fit_all_participants(
             try:
                 if method == "VBMC":
                     result = fit_participant_pyvbmc(
-                        participant_data, model_config, verbose=False
+                        participant_data, model_config, verbose=False, plot=plot
                     )
                 else:
                     result = fit_participant_deoptim(
@@ -849,14 +861,14 @@ if __name__ == "__main__":
 
     # Ajustement de quelques modèles pour test
     models_to_fit = [
-        "HOMOGENEOUS",
-        "GAIN_LOSS",
-        "REE_BIASED_SIMPLE",
-        "REE_BIASED_COMPLEX",
-        "REE_LEARNING_SIMPLE",
-        "REE_LEARNING_COMPLEX",
+        # "HOMOGENEOUS",
+        # "GAIN_LOSS",
+        # "REE_BIASED_SIMPLE",
+        # "REE_BIASED_COMPLEX",
+        # "REE_LEARNING_SIMPLE",
+        # "REE_LEARNING_COMPLEX",
         "REE_LEARNING_BIASED_SIMPLE",
-        "REE_LEARNING_BIASED_COMPLEX",
+        # "REE_LEARNING_BIASED_COMPLEX",
     ]
 
     # all_results = fit_all_participants_both_methods(
@@ -874,6 +886,7 @@ if __name__ == "__main__":
         method=method,
         n_participants=1,  # Set to a number to limit for testing
         verbose=True,
+        plot=True
     )
 
     # Comparaison des modèles