diff --git a/aepsych/models/utils.py b/aepsych/models/utils.py
index 8677221c8..d6eb6ac1d 100644
--- a/aepsych/models/utils.py
+++ b/aepsych/models/utils.py
@@ -123,7 +123,7 @@ def get_extremum(
         # Transform locked dims
         tmp = {}
         for key, value in locked_dims.items():
-            tensor = torch.zeros(len(bounds))
+            tensor = torch.zeros(model.dim)
             tensor[key] = value
             tensor = model.transforms.transform(tensor)
             tmp[key] = tensor[key].item()
diff --git a/tests/server/message_handlers/test_query_handlers.py b/tests/server/message_handlers/test_query_handlers.py
index 038731181..d03c52472 100644
--- a/tests/server/message_handlers/test_query_handlers.py
+++ b/tests/server/message_handlers/test_query_handlers.py
@@ -19,7 +19,7 @@ def test_strat_query(self):
             [common]
             stimuli_per_trial=2
             outcome_types=[binary]
-            parnames = [par1, par2]
+            parnames = [par1, par2, par3]
             strategy_names = [opt_strat]
             acqf = PairwiseMCPosteriorVariance
 
@@ -33,6 +33,11 @@ def test_strat_query(self):
             lower_bound = -1
             upper_bound = 1
 
+            [par3]
+            par_type = continuous
+            lower_bound = 10
+            upper_bound = 100
+
             [opt_strat]
             min_asks = 1
             model = PairwiseProbitModel
@@ -57,9 +62,26 @@ def test_strat_query(self):
         tell_request = {
             "type": "tell",
             "message": [
-                {"config": {"par1": [0.5, 0.5], "par2": [-0.5, -0.5]}, "outcome": 1},
-                {"config": {"par1": [0.0, 0.75], "par2": [0.0, -1]}, "outcome": 0},
-                {"config": {"par1": [1, -1], "par2": [0, 0.0]}, "outcome": 0},
+                {
+                    "config": {
+                        "par1": [0.5, 0.5],
+                        "par2": [-0.5, -0.5],
+                        "par3": [40, 50],
+                    },
+                    "outcome": 1,
+                },
+                {
+                    "config": {
+                        "par1": [0.0, 0.75],
+                        "par2": [0.0, -1],
+                        "par3": [11, 99],
+                    },
+                    "outcome": 0,
+                },
+                {
+                    "config": {"par1": [1, -1], "par2": [0, 0.0], "par3": [40, 12]},
+                    "outcome": 0,
+                },
             ],
         }
 
@@ -84,7 +106,7 @@ def test_strat_query(self):
             "type": "query",
             "message": {
                 "query_type": "prediction",
-                "x": {"par1": [0.0], "par2": [-0.5]},
+                "x": {"par1": [0.0], "par2": [-0.5], "par3": [45]},
             },
         }
         query_inv_req = {
@@ -105,7 +127,7 @@ def test_strat_query(self):
         }
         query_inv_const = {
             "type": "query",
-            "message": {"query_type": "inverse", "y": 5.0, "constraints": {1: 0}},
+            "message": {"query_type": "inverse", "y": 5.0, "constraints": {2: 20}},
         }
 
         response = self.s.handle_request(query_min_req)
@@ -131,7 +153,7 @@ def test_strat_query(self):
         self.assertTrue(response["x"]["par1"][0] == 0.25)
 
         response = self.s.handle_request(query_inv_const)
-        self.assertTrue(response["x"]["par2"][0] == 0)
+        self.assertTrue(response["x"]["par3"][0] == 20)
 
     def test_grad_model_smoketest(self):
         # Some models return values with gradients that need to be handled