diff --git a/Project.toml b/Project.toml
index 069a638..13e02d4 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "ACTRModels"
 uuid = "c095b0ea-a6ca-5cbd-afed-dbab2e976880"
 authors = ["itsdfish"]
-version = "0.10.3"
+version = "0.10.4"
 
 [deps]
 ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
diff --git a/src/MemoryFunctions.jl b/src/MemoryFunctions.jl
index 3d4c88a..6964708 100644
--- a/src/MemoryFunctions.jl
+++ b/src/MemoryFunctions.jl
@@ -17,18 +17,19 @@ function baselevel(d, lags)
 end
 
 """
-    baselevel(d, chunk)
+    baselevel!(N, L, k, lags, d)
 
-Computes baselevel activation according exact equation.
+Computes baselevel activation with hybrid approximation.
 
 # Arguments
 
-- `actr`: an `ACTR` model object
-- `chunk`: a chunk
+- `N`: the number of times the chunk was used 
+- `L`: lifetime of chunk in seconds 
+- `k`: number of timestaps tracked
+- `lags`: time since last use for each k
+- `d`: decay rate 
 """
-function baselevel!(actr, chunk)
-    @unpack N,L,k,lags = chunk
-    d = actr.parms.d
+function baselevel!(N, L, k, lags, d)
     exact = baselevel(d, lags)
     approx = 0.0
     if N > k
@@ -37,14 +38,30 @@ function baselevel!(actr, chunk)
         x2 = (1 - d) * (L - tk)
         approx = x1 / x2
     end
-    chunk.act_bll = log(exp(exact) + approx)
+    return log(exp(exact) + approx)
+end
+
+"""
+    baselevel!(actr, chunk) 
+
+Computes baselevel activation with hybrid approximation.
+
+# Arguments
+
+- `actr`: an `ACTR` model object
+- `chunk`: a chunk
+"""
+function baselevel!(actr, chunk)
+    @unpack N,L,k,lags = chunk
+    d = actr.parms.d
+    chunk.act_bll = baselevel!(N, L, k, lags, d)
     return nothing
 end
 
 """
     baselevel!(actr)
 
-Computes baselevel activation for all chunks according exact equation.
+Computes baselevel activation with hybrid approximation.
 
 # Arguments
 
@@ -286,10 +303,11 @@ Computes the spreading activation for a given chunk
 - `chunk`: the chunk for which spreading activation is computed
 """
 function spreading_activation!(actr, chunk)
+    @unpack γ,ω = actr.parms 
     imaginal = actr.imaginal
     isempty(imaginal.buffer) ? (return nothing) : nothing 
-    w = compute_weights(imaginal)
-    γ = actr.parms.γ; r = zero(γ); sa = zero(γ)
+    w = compute_weights(imaginal, ω)
+    r = zero(γ); sa = zero(γ)
     slots = imaginal.buffer[1].slots
     denoms = imaginal.denoms
     for (v,d) in zip(slots, denoms)
@@ -323,8 +341,8 @@ function cache_denomitors(actr)
     return nothing
 end
 
-function compute_weights(mod)
-    return mod.ω / length(mod.buffer[1].slots)
+function compute_weights(mod, ω)
+    return ω / length(mod.buffer[1].slots)
 end
 
 function compute_denom(memory, value)
diff --git a/src/Structs.jl b/src/Structs.jl
index bb57191..e639508 100644
--- a/src/Structs.jl
+++ b/src/Structs.jl
@@ -30,8 +30,9 @@ ACT-R parameters with default values. Default values are overwritten with keywor
 - `τ=0.0`: retrieval threshold
 - `s=0.2`: logistic scalar for activation noise.
 - `γ=1.6`: maximum associative strength
-- `blc=0.0`: base level constant
 - `δ=0.0`: mismatch penalty
+- `ω=1.0`: weight for source of spreading activation
+- `blc=0.0`: base level constant
 - `ter=0.0`: a constant for encoding and responding time
 - `mmp_fun`: a mismatch penalty function. By default, `mmp_fun` subtracts `δ` from each non-matching slot value
 - `sa_fun`: a function for spreading activation which requires arguments for actr and chunk
@@ -56,6 +57,7 @@ ACT-R parameters with default values. Default values are overwritten with keywor
     s
     γ
     δ
+    ω
     blc
     ter
     mmp_fun
@@ -81,6 +83,7 @@ function Parms(;
     s = .3,
     γ = 0.0,
     δ = 0.0,
+    ω = 1.0,
     blc = 0.0,
     ter = 0.0,
     mmp_fun = default_penalty,
@@ -106,6 +109,7 @@ function Parms(;
         s,
         γ,
         δ,
+        ω,
         blc,
         ter,
         mmp_fun,
diff --git a/test/benchmarks.jl b/test/benchmarks.jl
index 77c238d..329eb67 100644
--- a/test/benchmarks.jl
+++ b/test/benchmarks.jl
@@ -1,4 +1,3 @@
-
 using ACTRModels, BenchmarkTools
 n = 10
 parms = (sa=false, mmp=true, γ=1.3, δ=1.0)