# PASS2: Modelling Judgments of Frequency with PASS 2 In JoF: Modelling and Simulating Judgments of Frequency

## Description

Modelling Judgments of Frequency with PASS 2

## Usage

 `1` ```PASS2(x, y, ..., sqc, att, n_output_units = "half", rdm_weights = F, noise = 0) ```

## Arguments

 `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. `x` gets the value `1`, `y` gets the value `2`, `...` gets the value `3` and so on. The argument `sqc = c(1, 2, 3, 2)` means: first input `x` is processed, second input `y` is processed followed by processing input number three and fourth, th input `y` is used again. So `sqc` contains the frequency information too. In `c(1, 2, 3, 2)`, `x` and the third input are presented once. The input `y` is presented twice. `att` attention is a vector with numeric values between 0 and 1. `att` has the same length like `sqc`, so each input processing have its own value and PASS 1 can modulate attention by time or input. If `att` is exact one numeric value (e.g. ` att = .1`), all inputs get the same parameter of attention. `n_output_units` number of output units as numeric value. This must be between 1 and the maximum number of input units. `n_output_units = 'half'` determines the half of the input units. `rdm_weights` a logical value indicating whether random weights in the neural network are used or not. If `rdm_weights = FALSE` all network connections are zero at the beginning. `noise` a proportion between 0 and 1 which determines the number of random activated input units (higher numbers indicate higher noise).

## Details

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

## Value

`PASS2` returns the relative judgment of frequency for each input.

## References

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.

## Examples

 ```1 2 3 4 5 6 7``` ```o1 <- c(1, 0, 0, 0) o2 <- c(0, 1, 0, 0) o3 <- c(0, 0, 1, 0) o4 <- c(0, 0, 0, 1) PASS2(o1, o2, o3, o4, sqc = rep(1:4, 4:1), att = .1, n_output_units = 2, rdm_weights = FALSE, noise = 0) ```

JoF documentation built on April 3, 2020, 5:08 p.m.