Signal Discrimination Model (SDM) by Oberauer (2023)

Usage

sdm(resp_error, links = NULL, version = "simple", ...)

sdmSimple(resp_error, links = NULL, version = "simple", ...)

Arguments

resp_error: The name of the variable in the dataset containing the response error. The response error should code the response relative to the to-be-recalled target in radians. You can transform the response error in degrees to radians using the deg2rad function.
links: A list of links for the parameters. Currently does not affect the model fits, but it will in the future.
version: Character. The version of the model to use. Currently only "simple" is supported.
...: used internally for testing, ignore it

Value

An object of class bmmodel

Details

see vignette("bmm_sdm_simple") for a detailed description of the model and how to use it. * Domain: Visual working memory

Task: Continuous reproduction
Name: Signal Discrimination Model (SDM) by Oberauer (2023)
Citation:
- Oberauer, K. (2023). Measurement models for visual working memory - A factorial model comparison. Psychological Review, 130(3), 841-852
Version: Simple (no non-targets)
Requirements:
- The response variable should be in radians and represent the angular error relative to the target
Parameters:
- mu: Location parameter of the SDM distribution (in radians; by default fixed internally to 0)
- c: Memory strength parameter of the SDM distribution
- kappa: Precision parameter of the SDM distribution
Fixed parameters:
- mu = 0
Default parameter links:
- mu = tan_half; c = log; kappa = log
Default priors:
- mu:
  - main: student_t(1, 0, 1)
- kappa:
  - main: student_t(5, 1.75, 0.75)
  - effects: normal(0, 1)
- c:
  - main: student_t(5, 2, 0.75)
  - effects: normal(0, 1)

Examples

if (FALSE) {
# simulate data from the model
library(bmm)
library(brms)
dat <- data.frame(y = rsdm(n = 1000, c = 4, kappa = 3))

# specify formula
ff <- bmf(c ~ 1,
          kappa ~ 1)

# specify prior
prior <- prior(normal(1,2), class='Intercept', dpar='c')+
   prior(normal(1,2), class='Intercept', dpar='kappa')

# specify the model
fit <- bmm(formula = ff,
           data = dat,
           model = sdm(resp_error = 'y'),
           prior = prior,
           cores = 4,
           backend = 'cmdstanr')

# extract coefficients and plot fit
coef <- exp(brms::fixef(fit)[2:3,1])
hist(dat$y, breaks=60, freq=F)
x <- seq(-pi,pi,0.01)
lines(x, dsdm(x, mu=0, c=coef['c_Intercept'],
              kappa=coef['kappa_Intercept']), col='red')
}