Interference measurement model by Oberauer and Lin (2017).

Three versions of the Interference measurement model by Oberauer and Lin (2017). - the full, bsc, and abc. IMMfull(), IMMbsc(), and IMMabc() are deprecated and will be removed in the future. Please use imm(version = 'full'), imm(version = 'bsc'), or imm(version = 'abc') instead.

Usage

imm(
  resp_error,
  nt_features,
  nt_distances,
  set_size,
  regex = FALSE,
  version = "full",
  ...
)

IMMfull(resp_error, nt_features, nt_distances, set_size, regex = FALSE, ...)

IMMbsc(resp_error, nt_features, nt_distances, set_size, regex = FALSE, ...)

IMMabc(resp_error, nt_features, set_size, regex = FALSE, ...)

Arguments

resp_error: The name of the variable in the provided 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 radian using the deg2rad function.
nt_features: A character vector with the names of the non-target variables. The non_target variables should be in radians and be centered relative to the target. Alternatively, if regex=TRUE, a regular expression can be used to match the non-target feature columns in the dataset.
nt_distances: A vector of names of the columns containing the distances of non-target items to the target item. Alternatively, if regex=TRUE, a regular expression can be used to match the non-target distances columns in the dataset. Only necessary for the bsc and full versions.
set_size: Name of the column containing the set size variable (if set_size varies) or a numeric value for the set_size, if the set_size is fixed.
regex: Logical. If TRUE, the nt_features and nt_distances arguments are interpreted as a regular expression to match the non-target feature columns in the dataset.
version: Character. The version of the IMM model to use. Can be one of full, bsc, or abc. The default is full.
...: used internally for testing, ignore it

Value

An object of class bmmodel

Details

Domain: Visual working memory
Task: Continuous reproduction
Name: Interference measurement model by Oberauer and Lin (2017).
Citation:
- Oberauer, K., & Lin, H.Y. (2017). An interference model of visual working memory. Psychological Review, 124(1), 21-59

Version: `full`

Requirements:
- The response vairable should be in radians and represent the angular error relative to the target

The non-target features should be in radians and be centered relative to the target

Parameters:
- mu1: Location parameter of the von Mises distribution for memory responses (in radians). Fixed internally to 0 by default.
- kappa: Concentration parameter of the von Mises distribution
- a: General activation of memory items
- c: Context activation
- s: Spatial similarity gradient
Fixed parameters:
- mu1 = 0
- mu2 = 0
- kappa2 = -100
Default parameter links:
- mu1 = tan_half; kappa = log; a = log; c = log; s = log
Default priors:
- mu1:
  - main: student_t(1, 0, 1)
- kappa:
  - main: normal(2, 1)
  - effects: normal(0, 1)
- a:
  - main: normal(0, 1)
  - effects: normal(0, 1)
- c:
  - main: normal(0, 1)
  - effects: normal(0, 1)
- s:
  - main: normal(0, 1)
  - effects: normal(0, 1)

Version: `bsc`

Requirements:
- The response vairable should be in radians and represent the angular error relative to the target

The non-target features should be in radians and be centered relative to the target

Parameters:
- mu1: Location parameter of the von Mises distribution for memory responses (in radians). Fixed internally to 0 by default.
- kappa: Concentration parameter of the von Mises distribution
- c: Context activation
- s: Spatial similarity gradient
Fixed parameters:
- mu1 = 0
- mu2 = 0
- kappa2 = -100
Default parameter links:
- mu1 = tan_half; kappa = log; c = log; s = log
Default priors:
- mu1:
  - main: student_t(1, 0, 1)
- kappa:
  - main: normal(2, 1)
  - effects: normal(0, 1)
- c:
  - main: normal(0, 1)
  - effects: normal(0, 1)
- s:
  - main: normal(0, 1)
  - effects: normal(0, 1)

Version: `abc`

Requirements:
- The response vairable should be in radians and represent the angular error relative to the target

The non-target features should be in radians and be centered relative to the target

Parameters:
- mu1: Location parameter of the von Mises distribution for memory responses (in radians). Fixed internally to 0 by default.
- kappa: Concentration parameter of the von Mises distribution
- a: General activation of memory items
- c: Context activation
Fixed parameters:
- mu1 = 0
- mu2 = 0
- kappa2 = -100
Default parameter links:
- mu1 = tan_half; kappa = log; a = log; c = log
Default priors:
- mu1:
  - main: student_t(1, 0, 1)
- kappa:
  - main: normal(2, 1)
  - effects: normal(0, 1)
- a:
  - main: normal(0, 1)
  - effects: normal(0, 1)
- c:
  - main: normal(0, 1)
  - effects: normal(0, 1)

Additionally, all imm models have an internal parameter that is fixed to 0 to allow the model to be identifiable. This parameter is not estimated and is not included in the model formula. The parameter is:

b = "Background activation (internally fixed to 0)"

Examples

if (FALSE) { # isTRUE(Sys.getenv("BMM_EXAMPLES"))
# load data
data <- oberauer_lin_2017

# define formula
ff <- bmmformula(
  kappa ~ 0 + set_size,
  c ~ 0 + set_size,
  a ~ 0 + set_size,
  s ~ 0 + set_size
)

# specify the full IMM model with explicit column names for non-target features and distances
# by default this fits the full version of the model
model1 <- imm(resp_error = "dev_rad",
              nt_features = paste0('col_nt', 1:7),
              nt_distances = paste0('dist_nt', 1:7),
              set_size = 'set_size')

# fit the model
fit <- bmm(formula = ff,
           data = data,
           model = model1,
           cores = 4,
           backend = 'cmdstanr')

# alternatively specify the IMM model with a regular expression to match non-target features
# this is equivalent to the previous call, but more concise
model2 <- imm(resp_error = "dev_rad",
              nt_features = 'col_nt',
              nt_distances = 'dist_nt',
              set_size = 'set_size',
              regex = TRUE)

# fit the model
fit <- bmm(formula = ff,
           data = data,
           model = model2,
           cores = 4,
           backend = 'cmdstanr')

# you can also specify the `bsc` or `abc` versions of the model to fit a reduced version
model3 <- imm(resp_error = "dev_rad",
              nt_features = 'col_nt',
              set_size = 'set_size',
              regex = TRUE,
              version = 'abc')
fit <- bmm(formula = ff,
           data = data,
           model = model3,
           cores = 4,
           backend = 'cmdstanr')
}