PASS1 {JoF}

Modeling Judgments of Frequency with PASS 1

Description

Modeling Judgments of Frequency with PASS 1

Usage

PASS1(x, y, ..., sqc, att, dec, ifc, rdm_weights = TRUE, noise = 0)

Arguments

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. 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.

dec

decay is a vector with numeric values between -1 and 0. dec has the same length as sqc, so each input processing have its own value and PASS 1 can modulate decay by time. If dec is exact one numeric value (e.g. dec = -.1), all inputs get the same parameter of decay.

ifc

interference is a vector with numeric values between -1 and 0. ifc must have the same length as sqc. So each inputprocessing have its own value and PASS 1 can modulate inference by time. If ifc is exact one numeric value (e.g. ifc = -.1), all inputs get the same parameter of inference.

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 determine the number of randome activiated inputunits (hihger numbers indicate higher noise).

Details

PASS 1 is a simple neural pattern associator learning by delta rule.

Learning:

if U_{i} and U_{j} are activated, then \Delta w_{ij} = \Theta_{1} ( 1 - w_{ij})

Interference:

if either U_{i} or U_{j} is activated, then \Delta w_{ij} = \Theta_{2} * w_{ij}

Decay:

if neither U_{i} nor U_{j} is activated, then \Delta w_{ij} = \Theta_{3} * w_{ij}

Value

PASS1 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

o1 <- c(1, 0, 0, 0)
o2 <- c(0, 1, 0, 0)
o3 <- c(0, 0, 1, 0)
o4 <- c(0, 0, 0, 1)
PASS1(o1, o2, o3, o4,
      sqc = rep(1:4, 4:1), att = .1, dec = -.05,
      ifc = -.025, rdm_weights = FALSE, noise = 0)