MINERVA2 {JoF}

Modeling Judgments of Frequency with MINERVA 2

Description

Modeling Judgments of Frequency with MINERVA 2

Usage

MINERVA2(x, y, ..., sqc, L, dec = NULL)

Arguments

x

input handled by MINERVA 2. Values -1, 0 and 1 are allowed. -1 represents the absence of a feature, 0 the irrelevance of a feature and 1 the presence of a feature.

y

another input handled by MINERVA 2. At least two inputs are needed for the simulation.

...

other 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.

L

learning parameter. This is the proportion of a correctly stored vector. L = 1 means 100 % of the input is processed correctly. If L is a vector, each input could be handled differently. So L = c(.5, .6, .9) means, input x is correctly stored to 50 %, input y is stored to 60 % and the third input (inserted in ...) is stored with 90 % probability.

dec

decay is not part of the original version of MINERVA 2. This is just implemented for a better comparison with the other models of JoF. In dec = NULL, decay has no effect. For dec = 'curve' decay uses a forgetting curve. If dec is a numeric Vector e. g. dec = c(.8, .9, 1) the memory traces are weighted. The first represented trace is weighted by .8 the second by .9 and the youngest trace by 1. The value dec = 1 corresponds with the original model.

Details

Calculations of MINERVA 2 contain four steps.

S_{i} = \frac{\sum_{j=1}^{N}{P_{j}T_{ij}}}{N_{i}}

A_{i} = S_{i}^{3}

I = \sum_{i=1}^{M}{A_{i}}

relative JoF = \frac{I_{j}}{\sum_{j}^{K}{I_{j}}}

Value

MINERVA2 returns the relative judgment of frequency

References

Dougherty, M. R., Gettys, C. F., & Ogden, E. E. (1999). MINERVA-DM: A memory processes model for judgments of likelihood. Psychological Review, 106(1), 180.

Hintzman, D. L. (1984). MINERVA 2: A simulation model of human memory. Behavior Research Methods, Instruments, and Computers, 16, 96–101.

Examples

#This example is presented in Dougherty,
#Gettys, & Ogden, 1999 (p. 185)
H1 <- c(-1, 1, 0, 1, 0, -1, 1, -1, 0)
H2 <- c(-1, 0, 0, 1, 0, 0, 1, 0, 0)
x <- MINERVA2(H1, H2, sqc = c(2, 1), L = 1)