Description Usage Arguments Details Value References Examples
Modelling Judgments of Frequency with PASS 2
1 
x 
input handled by PASS 2. 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. 
n_output_units 
number of output units as numeric value.
This must be between 1 and the maximum number of input units.

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 determines the number of random activated input units (higher numbers indicate higher noise). 
PASS 2 uses a competitive learning algorithm, which usually clusters the input as side effect. If weights are equal, the winning unit is chosen randomly, because of this, each simulation is slightly different.
if an outputunit Oi losses: Δ wij = 0
if an outputunit Oi wins: Δ wij = gw * (ai / sum(ai))  gw*wij
PASS2
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. 137152). New York: Oxford University Press.
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