# MINERVA2: Modeling Judgments of Frequency with MINERVA 2 In JoF: Modelling and Simulating Judgments of Frequency

## Description

Modeling Judgments of Frequency with MINERVA 2

## Usage

 `1` ```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.

Si = (sum(Pj)*Tj) / Ni

Ai = Si^3

I = sum(Ai)

relative JoF = Ij / Sum(Ij)

## 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

 ```1 2 3 4 5``` ```#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) ```

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