JoF (version 0.1.0)

PASS2: Modelling Judgments of Frequency with PASS 2

Description

Modelling Judgments of Frequency with PASS 2

Usage

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).

Value

PASS2 returns the relative judgment of frequency for each input.

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. $$if an outputuni O_{i} losses: \Delta w_{ij} = 0$$ $$if an outputuni O_{i} wins: \Delta w_{ij} = g_{w} \frac{a_{i}}{\sum_{i}^{n}{a_{i}}} - g_{w}w_{ij}$$

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

# NOT RUN {
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)
# }