Description Usage Arguments Details Value References Examples
Modeling Judgments of Frequency with PASS 1
1 |
x |
input handled by PASS 1. Only binary input is allowed. |
y |
a second binary input handled by PASS 1. At least two inputs are needed for the simulation. |
... |
other binary inputs for modeling. |
sqc |
sequence of the different objects. Each input gets
an ascending number. |
att |
attention is a vector with numeric values
between 0 and 1. |
dec |
decay is a vector with numeric values between
-1 and 0. |
ifc |
interference is a vector with numeric values
between -1 and 0. |
rdm_weights |
a logical value indicating whether random
weights in the neural network are used or not. If
|
noise |
a proportion between 0 and 1 which determine the number of randome activiated inputunits (hihger numbers indicate higher noise). |
PASS 1 is a simple neural pattern associator learning by delta rule.
Learning:
if Ui and Uj are activated, then Δ wij = Θ1 * ( 1 - wij)
Interference:
if either Ui or Uj is activated, then Δ wij = Θ2 * wij
Decay:
if neither Ui nor Uj is activated, then Δ wij = Θ3 * wij
PASS1
returns the relative judgment of frequency
for each input.
Sedlmeier, P. (2002). Associative learning and frequency judgements: The PASS model. In P. Sedlmeier, T. Betsch (Eds.), Etc.: Frequency processing and cognition (pp. 137-152). New York: Oxford University Press.
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