diff --git a/topomodelx/nn/hypergraph/hypergat_layer.py b/topomodelx/nn/hypergraph/hypergat_layer.py
index 6f303aa5..1f2d769f 100644
--- a/topomodelx/nn/hypergraph/hypergat_layer.py
+++ b/topomodelx/nn/hypergraph/hypergat_layer.py
@@ -113,7 +113,7 @@ def attention(
             Attention weights: one scalar per message between a source and a target cell.
         """
         if mechanism == "node-level":
-            x_source_per_message = x_source[self.target_index_i]
+            x_source_per_message = x_source[self.source_index_j]
             return torch.nn.functional.softmax(
                 torch.matmul(
                     torch.nn.functional.leaky_relu(x_source_per_message),
@@ -122,11 +122,11 @@ def attention(
                 dim=1,
             )
 
-        x_source_per_message = x_source[self.source_index_j]
+        x_source_per_message = x_source[self.target_index_i]
         x_target_per_message = (
             x_source[self.target_index_i]
             if x_target is None
-            else x_target[self.target_index_i]
+            else x_target[self.source_index_j]
         )
 
         x_source_target_per_message = torch.nn.functional.leaky_relu(
@@ -183,7 +183,7 @@ def forward(self, x_0, incidence_1):
             Output hyperedge features.
         """
         intra_aggregation = incidence_1.t() @ (x_0 @ self.weight1)
-
+        
         self.target_index_i, self.source_index_j = incidence_1.indices()
 
         attention_values = self.attention(intra_aggregation).squeeze()
@@ -200,7 +200,7 @@ def forward(self, x_0, incidence_1):
         inter_aggregation = incidence_1 @ (messages_on_edges @ self.weight2)
 
         attention_values = self.attention(
-            inter_aggregation, intra_aggregation
+            inter_aggregation, intra_aggregation, "edge-level"
         ).squeeze()
         incidence_with_attention = torch.sparse_coo_tensor(
             indices=incidence_1.indices(),