bmm (version 1.0.1)

sdm: Signal Discrimination Model (SDM) by Oberauer (2023)

Description

Signal Discrimination Model (SDM) by Oberauer (2023)

Usage

sdm(resp_error, version = "simple", ...)
sdmSimple(resp_error, version = "simple", ...)

Value

An object of class bmmodel

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.

version

Character. The version of the model to use. Currently only "simple" is supported.

...

used internally for testing, ignore it

Details

see the online article 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

• 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) { # isTRUE(Sys.getenv("BMM_EXAMPLES"))
# simulate data from the model
dat <- data.frame(y = rsdm(n = 1000, c = 4, kappa = 3))

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

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