Simple shrink spot

pyproject.toml

@@ -7,7 +7,7 @@ requires = ["setuptools", "wheel", "Cython>=0.29.23", "numpy >= 1.20"]
 
 [project]
 name= "ssm-simulators"
-version= "0.7.8"
+version= "0.7.9"
 authors= [{name = "Alexander Fenger", email = "[email protected]"}]
 description= "SSMS is a package collecting simulators and training data generators for a bunch of generative models of interest in the cognitive science / neuroscience and approximate bayesian computation communities"
 readme = "README.md"

ssms/__init__.py

@@ -4,6 +4,6 @@
 from . import config
 from . import support_utils
 
-__version__ = "0.7.8"  # importlib.metadata.version(__package__ or __name__)
+__version__ = "0.7.9"  # importlib.metadata.version(__package__ or __name__)
 
 __all__ = ["basic_simulators", "dataset_generators", "config", "support_utils"]

ssms/basic_simulators/drift_functions.py

@@ -146,9 +146,9 @@ def ds_conflict_drift(
 
 def attend_drift(
     t: np.ndarray = np.arange(0, 20, 0.1),
-    p_target: float = -0.3,
-    p_outer: float = -0.3,
-    p_inner: float = 0.3,
+    ptarget: float = -0.3,
+    pouter: float = -0.3,
+    pinner: float = 0.3,
     r: float = 0.5,
     sda: float = 2,
 ) -> np.ndarray:
@@ -160,11 +160,11 @@ def attend_drift(
         t: np.ndarray
             Timepoints at which to evaluate the drift.
             Usually np.arange() of some sort.
-        p_outer: float
+        pouter: float
             perceptual input for outer flankers
-        p_inner: float
+        pinner: float
             perceptual input for inner flankers
-        p_target: float
+        ptarget: float
             perceptual input for target flanker
         r: float
             rate parameter for sda decrease
@@ -184,7 +184,47 @@ def attend_drift(
         -0.5, loc=0, scale=new_sda
     )
 
-    v_t = 2 * p_outer * a_outer + 2 * p_inner * a_inner + p_target * a_target
+    v_t = (2 * pouter * a_outer) + (2 * pinner * a_inner) + (ptarget * a_target)
+
+    return v_t
+
+
+def attend_drift_simple(
+    t: np.ndarray = np.arange(0, 20, 0.1),
+    ptarget: float = -0.3,
+    pouter: float = -0.3,
+    r: float = 0.5,
+    sda: float = 2,
+) -> np.ndarray:
+    """Drift function for shrinking spotlight model, which involves a time varying
+    function dependent on a linearly decreasing standard deviation of attention.
+
+    Arguments
+    --------
+        t: np.ndarray
+            Timepoints at which to evaluate the drift.
+            Usually np.arange() of some sort.
+        pouter: float
+            perceptual input for outer flankers
+        ptarget: float
+            perceptual input for target flanker
+        r: float
+            rate parameter for sda decrease
+        sda: float
+            width of attentional spotlight
+    Return
+    ------
+    np.ndarray
+        Drift evaluated at timepoints t
+    """
+
+    new_sda = np.maximum(sda - r * t, 0.001)
+    a_outer = 1.0 - norm.cdf(
+        0.5, loc=0, scale=new_sda
+    )  # equivalent to norm.sf(0.5, loc=0, scale=new_sda)
+    a_target = norm.cdf(0.5, loc=0, scale=new_sda) - 0.5
+
+    v_t = (2 * pouter * a_outer) + (2 * ptarget * a_target)
 
     return v_t
 

ssms/basic_simulators/theta_processor.py

@@ -120,7 +120,12 @@ def process_theta(
                 theta["sv"]
             )
 
-        if model in ["shrink_spot", "shrink_spot_extended"]:
+        if model in [
+            "shrink_spot",
+            "shrink_spot_extended",
+            "shrink_spot_extended_angle",
+            "shrink_spot_simple_extended",
+        ]:
             theta["v"] = np.tile(np.array([0], dtype=np.float32), n_trials)
 
         # Multi-particle models

ssms/config/config.py

@@ -84,7 +84,11 @@ def boundary_config_to_function_params(boundary_config: dict) -> dict:
     },
     "attend_drift": {
         "fun": df.attend_drift,
-        "params": ["p_target", "p_outer", "p_inner", "r", "sda"],
+        "params": ["ptarget", "pouter", "pinner", "r", "sda"],
+    },
+    "attend_drift_simple": {
+        "fun": df.attend_drift_simple,
+        "params": ["ptarget", "pouter", "r", "sda"],
     },
 }
 
@@ -344,9 +348,9 @@ def boundary_config_to_function_params(boundary_config: dict) -> dict:
             "a",
             "z",
             "t",
-            "p.target",
-            "p.outer",
-            "p.inner",
+            "ptarget",
+            "pouter",
+            "pinner",
             "r",
             "sda",
         ],
@@ -370,9 +374,9 @@ def boundary_config_to_function_params(boundary_config: dict) -> dict:
             "a",
             "z",
             "t",
-            "p.target",
-            "p.outer",
-            "p.inner",
+            "ptarget",
+            "pouter",
+            "pinner",
             "r",
             "sda",
         ],
@@ -390,6 +394,31 @@ def boundary_config_to_function_params(boundary_config: dict) -> dict:
         "n_particles": 1,
         "simulator": cssm.ddm_flex,
     },
+    "shrink_spot_simple_extended": {
+        "name": "shrink_spot_simple_extended",
+        "params": [
+            "a",
+            "z",
+            "t",
+            "ptarget",
+            "pouter",
+            "r",
+            "sda",
+        ],
+        "param_bounds": [
+            [0.3, 0.1, 1e-3, 2.0, -5.5, 0.01, 1],
+            [3.0, 0.9, 2.0, 5.5, 5.5, 1.0, 3],
+        ],
+        "boundary_name": "constant",
+        "boundary": bf.constant,
+        "drift_name": "attend_drift_simple",
+        "drift_fun": df.attend_drift_simple,
+        "n_params": 7,
+        "default_params": [0.7, 0.5, 0.25, 2.0, -2.0, 0.01, 1],
+        "nchoices": 2,
+        "n_particles": 1,
+        "simulator": cssm.ddm_flex,
+    },
     "gamma_drift_angle": {
         "name": "gamma_drift_angle",
         "params": ["v", "a", "z", "t", "theta", "shape", "scale", "c"],